Transmission ratio distortion (TRD), or significant deviations from Mendelian inheritance, is a well-studied phenomenon on autosomal chromosomes, but has not yet received attention on sex chromosomes. TRD was analyzed on 3832 heterosomal single nucleotide polymorphisms (SNPs) and 400 pseudoautosomal SNPs spanning the length of the X-chromosome using 436,651 genotyped Holstein cattle. On the pseudoautosomal region, an opposite sire-TRD pattern between male and female offspring was identified for 149 SNPs. This finding revealed unique SNPs linked to a specific-sex (Y- or X-) chromosome and describes the accumulation of recombination events across the pseudoautosomal region. On the heterosomal region, 13 SNPs and 69 haplotype windows were identified with dam-TRD. Functional analyses for TRD regions highlighted relevant biological functions responsible to regulate spermatogenesis, development of Sertoli cells, homeostasis of endometrium tissue and embryonic development. This study uncovered the prevalence of different TRD patterns across both heterosomal and pseudoautosomal regions of the X-chromosome and revealed functional candidate genes for bovine reproduction. Significant deviations from Mendelian inheritance, well known as transmission ratio distortion (TRD), have been observed in a number of diploid organisms ,2,3,4,5.Despite the recent interest in targeting TRD regions across the whole genome or, alternatively, evaluating for the absence of homozygous haplotypes in livestock species ,13,14,15In this study, we aimed to investigate the inheritance of single nucleotide polymorphisms (SNPs) and haplotypes from parents to offspring in order to characterize and understand the biology of TRD for the bovine sex chromosomes. The main objectives of this research were: (1) to generalize TRD models with male- and female-offspring-specific TRD components, (2) to determine the pseudoautosomal and heterosomal parts of the sex chromosomes using trios (sire-dam-offspring genotypes), (3) to characterize TRD on both heterosomal and pseudoautosomal parts of the Holstein cattle sex chromosomes, and finally, (4) to annotate genes and to evaluate the functional consequences of TRD regions via functional analyses.Bos taurus genome assembly. All SNP markers mapped in sex chromosomes and genotyped for at least 10 sire-dam-offspring trios were selected for analyses, resulting in a total of 6577 SNPs distributed across the X-chromosome . These animals were sampled from all available Holstein genotypes  and were genotyped within the first 90 days after birth to minimize offspring selection issues ,18. Animromosome . The numIn order to carry out haplotype analyses, 974 SNPs were selected from the heterosomal part of the X-chromosome . The num♂) and female offspring (♀) can be carried out by modeling the probability of transmission of alleles from heterozygous parents [♂ is the TRD parameter ranging between −0.5 and 0.5. As described by Casellas et al. (2017) [s♂) and dam-specific (αd♂) TRD. The same applies to female offspring to obtain α♀, αs♀ and αd♀.Taking an autosomal  chromosome region as a starting point, the analysis of TRD for male  region of the X chromosome, but restricted to dam-specific TRD.Two different approaches were used for analyzing TRD across the bovine X-chromosome. Transmission ratio distortion was evaluated using both SNP-by-SNP  and a sliding windows approach of 2-, 4-, 10- and 20-SNP across 974 imputed SNPs as described by Id-Lahoucine et al. 2019) . For sli . For slhttps://blast.ncbi.nlm.nih.gov/Blast.cgi accessed on 10 December 2020) against the bovine reference genome. The sequence was retrieved using the bovine UMD3.1 genome assembly from NCBI database. After this step, a short sequence of 20 bp around the genomic position of the markers was extracted to obtain the nucleotide context around the TRD markers. Using the genome data viewer, regions with homology between the short sequence around the markers and the Y-chromosome were investigated. The whole assembly of the bovine Y-chromosome is completely available for visualization only for the bovine genome assembly Btau_4.6.1 and, therefore, this assembly was used for the data visualization of the Y-chromosome. It is important to reinforce that neither for UMD 3.1 or ARS-UCD 1.2 an assemble of bovine Y-chromosome is available, which results in the impossibility to use these assemblies for the abovementioned analysis.Mendelian inconsistency was estimated under a pseudoautosomal and heterosomal inheritance models using trios of genotypes  in order to determine the nature of SNPs across the X-chromosome. Both analyses of LD and recombination events were performed. For recombination events, the sum of recombination probabilities was calculated for markers in pseudoautosomal parts of the X-chromosome using the package hsphase in R . In addi10(BF) of the associated marker (SNPs or haplotypes with TRD) were assigned for each positional candidate gene.The genes mapped around the TRD regions identified by both SNP- and haplotype- based methods were annotated using R software (Version 3.2.3) and the R package: Genomic functional Annotation in Livestock for positional candidate Loci (GALLO) . The genwww.qiagen.com/ingenuity 15 December 2020) to identify the canonical metabolic pathways, diseases and functions associated with the list of candidate genes harboring deviations from Mendelian inheritance on the bovine X-chromosome (including both SNPs and haplotypes). In addition, functional analysis was performed to explore the existence of signaling networks connecting those genes [Functional analysis were performed using the IPA  software  region specific for each sex chromosome. The pseudoautosomal region in cattle is located in the long arm of the X-chromosome and the short arm of the Y-chromosome. Its physical length is estimated to be between 5–9 Mb . Other sFrom the preliminary scan, 5935 and 780 SNPs were shown as exhibiting heterosomal and pseudoautosomal inheritance, respectively. In order to guarantee a reasonable statistical power, only SNPs with at least 100 trios (sire-dam-offspring) of genotypes and a Mendelian inconsistency < 1% were maintained for TRD analyses . After tCouldrey et al. (2016) , reporte20.18, 625 heterozygous sires and 1458 under-represented offspring).Decisive evidence with Bayes factor  according to Jeffreys’ scale  was idenOpposite sire-TRD between male- and female-offspring were observed across the whole pseudoautosomal part of sex chromosomes with a specific pattern . This TRThis opposite sire-TRD pattern could reinforce the high recombination rate in the cattle pseudoautosomal region, as it was also described on other mammalian species, which exceed the genome average by 10–20 times . Notice On the other hand, 15 pseudoautosomal SNPs (from 180) with opposite sire-TRD patterns were in the undetermined regions . In contrast to the main pseudoautosomal part, we did not observe a similar pattern . It is important to emphasize that opposite sire-TRD between male- and female-offspring not necessarily imply recombination between X- and Y-chromosomes. Therefore, this pattern could also be explained by different linkage of alleles to X- and Y-chromosome.Across the X-chromosome, we identified 45 initial SNPs that are exhibiting sire-TRD via one single sex whereas null TRD in the opposite sex. After discarding possible random TRD at ≤0.001%, according to the approximate empirical null distribution of TRD , 8 and 7The different magnitudes of TRD could also support imprinting for some of them, as TRD ranged from 0.04 to 0.36 in male-offspring and between 0.03 to 0.29 in female-offspring. For these SNPs, the number of under-represented offspring ranges from 47 to 1014 (1 SNP with ≥1000) for males and from 54 to 2183 (3 SNP with ≥1000) for females. The number of heterozygous sires ranges from 7 to 302 and from 13 to 1281 for male- and female-offspring, respectively. The minor allele frequencies of the detected SNPs ranges from 0.08 to 0.48. It is important to mention that moderate-to-high TRD magnitude signals were observed in regions with low or extremely low frequencies, suggesting the effects of TRD selection as observed in autosomal chromosomes as well [d = 0.02. The maximum magnitude observed for dam-TRD was |0.12| and displaying 287 under-represented offspring. In general, the prevalence of TRD across the heterosomal regions was small in comparison to pseudoautosomal and autosomal regions. This observation could be related to the biological differences between sexes, such as the processes of germ cell formation in males versus females  [Among 3832 heterosomal SNPs, 12 SNPs were significantly detected with decisive evidence (BF ≥ 100) for dam-TRD . Among tgenesis) .Across 974 SNPs of the heterosomal part of the X-chromosome, haplotypes displaying TRD were detected with decisive evidence of BF (≥100). After discarding regions with <10 heterozygous dams and <50 informative offspring, the number of regions was 944, being 44, 71, 282 and 597 detected for 2-, 4-, 10- and 20-SNP haplotypes, respectively. The number of regions at <0.001% margin of error reduced to 14, 26, 138 and 319, respectively. When correcting for multiple tests with kernel smoothing, the number of regions reduced to 69 haplotype windows explaining the observed TRD among the corresponding linked regions. The TRD was observed in both male- and female-offspring and with strong magnitudes ranging up to |0.48|. The top 20 regions according to BF are summarized in Recessive TRD pattern  was targeted on pseudoautosomal SNPs  and heterosomal SNPs/haplotypes  on sex chromosomes. Whereas no recessive TRD patterns were found in SNP-by-SNP analyses, haplotype analyses revealed eight haplotypes . These rLOC790278 pseudogene on the Y-chromosome, which has a corresponding version on the X-chromosome (SHROOM2 gene). The pseudogene LOC790278 is mapped on the heterosomal region of Y-chromosome (based on the Btau 4.6.1 annotation), before the SINE element responsible to reduce the homology between the X- and Y-chromosomes in the boundary between the heterosomal and pseudoautosomal [SHROOM2 is a X-specific gene in cattle. However, following the gene annotation from NCBI genome data viewer, this gene is mapped to the boundary between the pseudoautosomal and the heterosomal regions, due to the location of GPR143, the gene responsible to define the beginning of the pseudoautosomal region [Among the SNPs with highest opposite sire-TRD between male- and female-offspring, the 20 bp sequence around BTX:143,928,317 SNP was identified within the utosomal . Additiol region . The resp-values < 0.05: citrulline degradation, spermine biosynthesis, phosphoribosyl diphosphate biosynthesis I, arginine biosynthesis IV, proline biosynthesis II (from arginine), urea cycle, pentose phosphate pathway (non-oxidative branch), glycine cleavage complex, phosphatidylcholine biosynthesis I, and acetyl-CoA biosynthesis I (pyruvate dehydrogenase complex). Among these metabolic pathways, the spermine biosynthesis plays a crucial role in male fertility. The gene associated with this pathway, in the current study, was the SMS (Spermine Synthetase) gene. Deletions in the X-chromosomal loci harboring the mice orthologous (SpmS) are associated with several impaired development phenotypes, such as sterility and profound postnatal growth retardation [PCYT1B gene in this study, which had a haplotype exhibiting TRD. The disruption of function of the first enzyme in the phosphatidylcholine biosynthesis, the choline kinase α, causes embryonic lethality in mice [PCYT1B gene exhibited ovarian tissue disorganization with progressive loss of follicular formation and oocyte maturation [Functional annotation of positional candidate genes displaying deviations from Mendelian inheritance on the bovine X-chromosome was performed for the 55 detected regions: 15 SNPs with specific sex-offspring sire TRD , 12 SNPs with dam-TRD, 20 haplotypes with dam-TRD and 8 haplotypes with recessive TRD . A total of 408 genes were annotated in an interval of 200 Kb (100 Kb up- and downstream) for the individual SNPs or within the haplotype coordinates. A total of 113 genes were annotated in the interval harboring the individual SNPs  and 296 within the haplotype coordinates. It is important to highlight that some genes were annotated in both SNP and haplotype approaches. The human and mouse orthologs were used for those genes without an assigned gene symbol in the bovine reference genome. A total of 295 genes on TRD regions  were annotated. This list of 295 genes was used to perform the functional analysis using the IPA  software to identify the enriched canonical metabolic pathways , diseaseardation . Anotherturation .Using the list of 295 positional candidate genes, 500 diseases and biological functions were enriched (FDR < 0.05). Among the top 10 enriched diseases and biological functions identified, a strong association between the genes and X-linked syndromic phenotypes was observed. It is important to highlight that several X-linked syndromes caused by structural alterations . Deficient mice for TSC22D3 show severe testis dysplasia followed by infertility [The ertility , reinforMECP2, L1CAM, IRAK1, KIF4A, ATP6AP1, G6PD, POLA1, FLNA, IKBKG, HCFC1, EMD, IL13RA2, PAK3, DCX, and TEX11. Some of these genes are responsible for regulating important metabolic pathways or biological processes associated with fertility. Among these processes, it is important to highlight the control of the progression of spermatogenesis, control of ciliary activity, development of Sertoli cells, DNA integrity in spermatozoa, and homeostasis of testicular cells. Additionally, the impact on fertility traits of these 15 functional and positional candidate genes was also evaluated in other species. The MECP2 gene is associated with the development of Rett syndrome in humans [MECP2 gene were previously associated with precocious puberty, undescended testes, and micropenis [L1CAM is considered a pathogenic factor in humans due to its overexpression in ovarian and endometrial carcinomas [IRAK1 gene is differentially expressed in impaired spermatogenesis [KIF4A transcript is crucial for the chromosome positioning and spindle formation, playing an important role during chromosome segregation in mouse oocytes [KIF4A variants were already associated with uterine capacity in beef cattle [ATP6AP1 gene encodes the main accessory protein of V-ATPase, which is a crucial regulator of intra-organellar acidification [ATP6AP1 is associated with immunodeficiency, hepatopathy, and abnormal protein glycosylation in prenatal and postnatal stages [ATP6AP1 gene was described, in zebrafish, as responsible to mediate the dorsal forerunner cell proliferation and the left-right asymmetry [GDP6 deficiency was suggested to be associated with reduced female fertility in humans [FLNA gene is involved with the regulation of the embryonic development of several tissues, such as the cardiac, neural and muscular tissues [FLNA gene plays a crucial role for embryo survival. The HCFC1 gene codifies a transcriptional co-regulator responsible to control cell proliferation, which loss-of-function variants were associated with disrupted neuronal and neural progenitor cells with a deleterious effect in humans and mice [PAK3 gene were found in men with Sertoli-cell-only syndrome, XY gonadal dysgenesis and premature ovarian failure [TEX11 gene was already broadly investigated in genetic association studies regarding male fertility in cattle and other species. This gene was associated with male infertility in humans [TEX11 gene are responsible to explain up to 13% of the total genetic variance for scrotal circumference at 12 months in beef cattle [In addition to the previous reported results, 15 out of the 408 positional candidate genes were previously identified as functional candidate genes for cattle male fertility traits in a systematic review performed by our group . These gn humans . In humacropenis ,65. Durircinomas . Interesogenesis . The fun oocytes ,69. Addif cattle . The ATPfication . The defl stages . In addisymmetry . The GDPn humans . The FLN tissues ,75. Consand mice . In huma failure . The TEXn humans . Indeed,f cattle .Transmission ratio distortion analyses showing deviations from Mendelian inheritance revealed 149 SNPs displaying opposite sire-TRD between male and female offspring with strong magnitude at the beginning of the pseudoautosomal region and gradually reducing along this region until being null at the extreme of the chromosomes. This finding is evidence of the strong linkage of unique SNPs to specific-sex (Y- or X-) chromosomes and depicts the accumulation of recombination events across the pseudoautosomal region of the cattle genome. Moreover, haplotypes were found displaying recessive TRD patterns in female-offspring. Functional analyses and validation analysis using independent populations and methodologies showed that the TRD regions identified in this study were related to key biological processes and molecular functions responsible for regulating processes such as spermatogenesis, development of sertoli cells, homeostasis of endometrium tissue and embryonic development.


Apostichopus japonicus is an economically important species in the global aquaculture industry. Russian A. japonicus, mainly harvested in the Vladivostok region, exhibits significant phenotypic differentiation, including in many economically important traits, compared with Chinese A. japonicus owing to differences in their habitat. However, both the genetic basis for the phenotypic divergence and the population genetic structure of Russian and Chinese A. japonicus are unknown.A. japonicus populations were sampled for whole-genome resequencing. The genetic structure analysis differentiated the Russian and Chinese A. japonicus into two groups. Population genetic analyses indicated that the Russian population showed a high degree of allelic linkage and had undergone stronger positive selection compared with the Chinese populations. Gene ontology terms enriched among candidate genes with group selection analysis were mainly involved in immunity, such as inflammatory response, antimicrobial peptides, humoral immunity, and apoptosis. Genome-wide association analysis yielded eight single-nucleotide polymorphism loci significantly associated with parapodium number, and these loci are located in regions with a high degree of genomic differentiation between the Chinese and Russia populations. These SNPs were associated with five genes. Gene expression validation revealed that three of these genes were significantly differentially expressed in individuals differing in parapodium number. AJAP08772 and AJAP08773 may directly affect parapodium production by promoting endothelial cell proliferation and metabolism, whereas AJAP07248 indirectly affects parapodium production by participating in immune responses.In this study, 210 individuals from seven Russian and Chinese A. japonicus, and found three candidate genes related to the number of parapodium. The results provide an in-depth understanding of the differences in the genetic structure of A. japonicus populations in China and Russia, and provide important information for subsequent genetic analysis and breeding of this species.This study, we performed population genetic structure and GWAS analysis on Chinese and Russian The online version contains supplementary material available at 10.1186/s12864-023-09113-x. Apostichopus japonicus  is mainly distributed along the North Pacific coast, including Russia, Japan, Korea, and the northern coast of China. A. japonicus possesses a range of pharmacological activities, including anti-inflammatory, antioxidant, and antitumor effects, which imparts it with a high medicinal value [A. japonicus, there has been a trend of significant growth in A. japonicus aquaculture production [A. japonicus production reached 196,564 tons.al value . These eal value , 3. In roduction . AccordiA. japonicus varies between geographic populations, with Russian individuals having a greater number of parapodium and thicker walls than Chinese individuals [A. japonicus are found on the back and sides, which is another critical factor in measuring their market value apart from their body colour [A. japonicus is rich in nerve plexus cells and sensory cells [A. japonicus as a sensory organ [A. japonicus parapodium and are involved in signaling in the epidermal layer of the parapodium [The external morphology of ividuals . The pary colour . The tipry cells , which pry organ . In addirapodium .A. japonicus phenotypes. For example, GWAS were applied by Wang et al [A. japonicus sex determination, which were found to be distributed on Chr4, Chr9, Chr17, and Chr18. This confirmed the possibility of an XX/XY system of sex determination in sea cucumbers. Using GWAS, Ge et al [A. japonicus color variation, located on Chr17, Chr21, Chr15, and Chr05, and determined that endothelin-converting enzyme-1 may be a key enzyme in the occurrence of color variation. Additionally, in our previous research, GWAS was used to identify potential SNP-associated loci for A. japonicus traits such as the number of parapodium and growth traits [Sea cucumber farming in China has a long history, dating back to the Shang Dynasty, 1300 B.C., and has undergone a long period of domestication and extensive selection . Howeverng et al . to idenGe et al . identifh traits , 16. Furh traits .A. japonicus and to facilitate targeted and precise genetic selection involving marker-assisted selection (MAS) and molecular design breeding, a total of 210 samples from seven populations of A. japonicus in Russia and China were whole-genome re-sequenced using the Illumina NovaSeq 6000 sequencing platform. Unlike our previous research on the number of parapodium in A. japonicus, this study introduced a population of Russian A. japonicus with a high number of parapodium, increasing the richness of phenotypic variation and aiding in the identification of more reliable SNP-associated loci.To identify loci or genes associated with parapodium numbers of Apostichopus japonicus populations from the Yellow Sea and the Bohai Sea regions were selected: PingDao , XiXiaoMo , BaShaoDdao , ShanDong , HuangLongWei , LvShun , and Russian A. japonicus , with sample collection sites shown in Fig. A. japonicus were collected from July to December 2020 and transferred to the Key Laboratory of Northern Seawater Aquaculture of the Ministry of Agriculture in rural areas for 3 weeks, during which time the water was kept clear and turbidity-free and fed with seaweed slurry with Spirulina until sampling was completed. Three 1 cm × 1 cm samples of muscle tissue were taken from healthy A. japonicus, frozen in liquid nitrogen immediately after sampling and stored in 2 ml centrifuge tubes at -80 °C.The A. japonicus with well-developed parapodium  was used for the extraction of total genomic DNA in this experiment. Qualified DNA samples were randomly fragmented into 350 bp fragments using a Covaris E220 ultrasonic DNA fragmentation machine . The library preparation process was completed on a T100 Thermal Cycler PCR  instrument using KAPA HyperHlus reagent  after 10 cycles of end repair, addition of polyA tails, addition of sequencing adapters, and purification and amplification. High-throughput sequencing of samples was performed using the Illumina Nova6000 sequencing platform  with a 150 bp paired-end reads .A. japonicus  for comparison. The reference genome we selected is the one with the longest N50 length among the published A. japonicus genomes, with a contig N50 of 190 Kb and a scaffold N50 of 486 Kb [High-quality trimmed reads were compared with consensus sequences obtained by clustering with BWA software  reads anf 486 Kb . Single f 486 Kb , and thef 486 Kb  was usedA. japonicus were performed using GCTA software (v1.93.2) based on SNP data using the EM-REML approach [Heritability estimates for traits in the number of parapodium in the approach . Since S2) were calculated using a sliding window of 100 kb with a sliding step of 10 kb, and a subset of 585,778 SNPs were extracted from the SNP dataset of all samples using an LD threshold of r2 < 0.05. Population structure analyses were performed in Admixture [K values set from 1 to 9. Phylogenetic tree analyses were performed using VCF2Dis (https://github.com/BGI-shenzhen/VCF2Dis) to construct nj trees.To identify independent SNPs for phylogenetic tree construction and structural analyses, LD coefficients (rdmixture  (v1.3.0)2 < 0.05, using PopLDdecay [Data for PCA analysis were similarly analysed using independent SNPs , and PCA analysis was performed using Plink  . Fixation index (FST) analysis and reduction of diversity (ROD) analysis were used to identify regions of high divergence at the genomic level in Russian and Chinese A. japonicus. The FST values between the two groups were calculated using a 100 kb sliding window (sliding step of 10 kb), and the sliding window with the top 5% of FST values was selected as a highly divergent candidate region. Tajima's D test within populations was performed separately using vcftools using a 100 kb non-overlapping sliding window. A perl script was used based on Pipop1 and Pipop2 are the values of nucleotide diversity (π) of the two populations) Calculation of inter-population ROD values [Population selection analyses were performed using vcftools  , ionForge , and enrionForge .Genome-wide association analysis was performed using a mixed linear model (LMM) in the GEMMA  (0.98.3)10P > 7.4. For the significant SNP loci obtained, the following strategy was used for analysis: firstly, the corresponding genes and associated functions were found in the spiked genome annotation file based on the SNP location information. Secondly, to analyse the functions of these candidate genes more accurately, eggNOG was used to annotate and find their cognate gene functions. Thirdly, these SNPs were analysed for upstream and downstream genomic LD blocks. Fourthly to explore whether the significant SNP loci were located within the selected genomic regions obtained from FST and ROD analysis.Based on the Bonferroni correction method according to −ΔΔCt method. The Student's t-test was used to assess the difference between groups.qRT-PCR was used for gene expression analysis. Primers were designed by Primer 5.0  raw reads were obtained per sample. After removal of low-quality sequences and adaptor sequences, 2180.6 × 109 valid reads were retained with an average valid read rate of 86.12%  was conducted in which the proportion of the total variance explained by PC1 and PC2 was 19.38% and 13.43%, respectively. The PCA plots , China_Group2 had the second longest LD decay distance (~ 0.5 kb), and China_Group1 had the shortest LD decay distance (~ 0.25 kb)  Fig. a. For thkb) Fig. b. These P < 0.05 in Wilcoxon test). Using the corresponding values in the top 5% window for calculation of π values as the comparison node, the π values for China_Group1, China_Group2, and the Russia group were 0.00465, 0.00517, and 0.00526, respectively.Nucleotide diversity (π) intuitively reflects the genetic diversity of the population. The results of the nucleotide diversity analysis for the three subgroups are shown in Fig. D analysis was used to examine positive selection effects by calculating differences in the number of segregating loci (θw) and nucleotide diversity (π). In this study, Tajima’s D values were calculated using a 100 kb sliding window . To investigate the reasons for the difference in number of parapodium between the Russian and Chinese A. japonicus, we correlated the results of the GWAS analysis with those of the FST and ROD analyses ; AJAP08772 and AJAP08773 were significantly up-regulated in A. japonicus with a high number of parapodium (> 55) compared with individuals with a low number of parapodium (< 33), whereas the opposite was observed for AJAP07248  analysis of the three populations revealed that China_Group1 had the lowest genetic diversity and the Russia group had higher genetic diversity. In general, populations with high genetic diversity are usually subject to low levels of positive selection –51. HoweFST analysis. Seven of these pathways were associated with antimicrobial peptide or antimicrobial humoral response, which play a crucial role in immunity by disrupting bacterial membranes [A. japonicus.Functional enrichment analysis of candidate genes identified through population selection analysis revealed significant enrichment of pathways related to antimicrobial peptide, humoral immunity, and apoptosis in genes within candidate intervals determined through embranes , 53 and embranes . In addiFST analysis, and five of these loci were also located within the selected interval obtained from the ROD analysis. These findings provide a genetic explanation for the positive selection of parapodium number in Russian A. japonicus.By combining the significantly different intervals of the China and Russia groups with the results of the GWAS analysis, we identified seven significantly associated SNP loci on Scaffold199, Scaffold254, and Scaffold255 located within the selected interval determined by the A. japonicus. SEMA5A, a homolog of AJAP08772, has been shown to stimulate endothelial cell proliferation and migration, while also inhibiting apoptosis [A. japonicus with a larger number of parapodia. This suggests that these genes may play a role in enhancing the growth of parapodia in A. japonicus by increasing cell metabolism and proliferation rates.In this study, eight SNP loci significantly associated with the number of parapodium were determined by the linear mixed model with Bonferroni correction. In the LD block analysis, LD blocks were identified at Scaffold254 555 kb–560 kb and Scaffold255 0.05 kb–6.97 kb, and the crucial genes AJAP08772 and AJAP08773 were identified in these two blocks associated with the number of parapodium in A. japonicus individuals that developed fewer parapodium. Gene homology annotation indicates AJAP07248 encodes arginase. Studies have shown that Nitric oxide (NO) regulation is the first step in the host defense mechanism of A. japonicus [A. japonicus NO production and a enhance in the A. japonicus's immune response to pathogenic microorganisms [A. japonicus may be associated with immune-related functions, possibly through the regulation of arginase synthesis. Further research is needed to better understand the relationship between parapodium and immune function in this species.In addition, we observed that the expression of AJAP07248 was significantly higher in the aponicus , that NOaponicus . Inhibitrganisms . These rApostichopus japonicus from China and Russia were subjected to high-throughput genome resequencing. The Chinese populations were divided into two subgroups that were primarily associated with geographic distribution. Genetic diversity analysis revealed a high degree of linkage between loci in the Russian population, which may have undergone stronger positive selection. Population selection analyses yielded genomic intervals between the Russia and China groups. Functional analysis of candidate genes within these intervals indicated differences in antimicrobial peptides, humoral immunity, apoptosis, and inflammatory responses. Three candidate genes significantly associated with parapodium number were identified by GWAS analysis. We hypothesized that AJAP08772 and AJAP08773 directly affect parapodium production by promoting endothelial cell proliferation and metabolism, whereas AJAP07248 indirectly affects parapodium production by participating in immune responses. The present study provides insight into the differences in genetic structure of Chinese and Russian populations of A. japonicus, and provides important information for subsequent genetic analysis and breeding of this species.A total of 210 individuals of Additional file 1: Supplementary table 1. Primer information of candidate genes.Additional file 2: Supplementary Fig. 1. Location statistics of SNP loci. Supplementary Fig. 2. Density map of the number distribution of parapodia in 210 Apostichopus japonicas. Supplementary Fig. 3. GO analysis results of candidate genes obtained by FST analysis. Supplementary Fig. 4. GO analysis results of candidate genes obtained by ROD analysis. Supplementary Fig. 5. Intersection of candidate intervals for FST and ROD. Supplementary Fig. 6. Enrichment analysis of genes within FST and ROD candidate intervals.


It is important to correctly filter out artificial variants to avoid false genotyping or estimation of allele frequencies. Here, we present an efficient and effective framework, inGAP-family, to discover, filter, and visualize DNA polymorphisms and structural variants (SVs) from alignment of short reads. Applying this method to polymorphism detection on real datasets shows that elimination of artificial variants greatly facilitates the precise identification of meiotic recombination points as well as causal mutations in mutant genomes or quantitative trait loci. In addition, inGAP-family provides a user-friendly graphical interface for detecting polymorphisms and SVs, further evaluating predicted variants and identifying mutations related to genotypes. It is accessible at https://sourceforge.net/projects/ingap-family/.Accurately identifying DNA polymorphisms can bridge the gap between phenotypes and genotypes and is essential for molecular marker assisted genetic studies. Genome complexities, including large-scale  Based on highly confident polymorphism markers discovered, inGAP-family could be further applied to detect meiotic recombination events from progeny genomes or to identify causal mutations in genotypic studies. Compared with SAMtools e.g., detection of crossovers (COs) and GCs and identification of causal mutations or quantitative trait loci (QTLs). The predicted polymorphisms can be examined through a graphical interface of inGAP-family.The inGAP-family is designed for accurate detection of polymorphisms and identification of meiotic recombination events and causal mutations from resequencing data of specific individuals, family-based samples, or pooled populations. As shown in e.g., detection of meiotic recombination points Most artificial polymorphisms are caused by sequencing errors, incomplete coverage of tandem repeats, or false mapping of non-allelic reads. These artificial polymorphisms, if are recognized as genomic markers, will bring a destructive effect on those studies, which are sensitive to rare genomic loci, To eliminate the unreliable variants, predicted polymorphisms need to be thoroughly evaluated by the following procedures. First, SNP and small indel candidates are filtered out if located within or adjacent to large-scale SVs, which lead to false mapping of non-allelic paralogous sequences inGAP-family employs a graphical interface to detect meiotic recombination events (COs and GCs) from high-throughput resequencing data of meiotic progeny genomes. Input files include: a reference genome, which does not have to be one of the parental genomes; a set of high-quality SNPs and indels between two parental genomes (as described above) for genotyping procedures; mapping files of high-throughput sequences of progenies against the reference genome. Each uniquely mapped read of a progeny genome is genotyped if overlapped with one or more polymorphism loci. Then allelic ratio is calculated for each SNP as follows:i-th SNP in the window, respectively. The genotype calling of a window is then defined as:The window size is approximately 20 kb for resequencing data with 20× depth on a genome of 100 Mb when having an SNP density of one substitution per 200 bp between ecotypes. Allelic ratio of a window is determined by:Adjacent windows with the same genotype  are grouped into larger blocks for each chromosome. Positions of COs are identified when genotypes switch between neighboring blocks, and are refined by examining the genotypes of flanking SNPs. Furthermore, the interface of inGAP-family allows users to manually modify CO boundaries or import a list including positions and genotypes of CO blocks. A SNP with allelic ratios inconsistent with the background genome is marked as a potential GC locus, and adjacent converted SNPs are grouped into one GC event.2 progenies hybridized from the mutant and another ecotype (a wild type) are selected for pooled sequencing when they exhibit the given phenotypes. As mentioned in the section of recombination event detection, high-quality polymorphisms are used as genetic markers between the two parental lines. Each read of F2 progenies is genotyped from mapping results against the reference genome sequence if overlapped with SNPs or indels. Sliding windows with size of 200 kb and step of 100 kb are applied for estimating the allelic ratio 2 progenies employed. Allelic ratios of windows are further smoothed, by employing longer sliding windows (size of 2 Mb and step of 500 kb), to calculate first-order and second-order derivatives for obtaining extreme values , which represent candidate regions carrying causal mutations. The predicted ranges of candidate regions associated with causal mutations can be refined through the graphical interface of inGAP-family when needed. Furthermore, inGAP-family identifies novel SNPs on peak/valley regions for EMS-induced mutants, then performs: 1) filtering out heterozygous SNPs whose allelic ratio smaller than 0.75; 2) excluding SNPs whose alternate allele is consistent with parental genomes; and 3) extracting SNPs that exhibit transitions from ‘G’ to ‘A’ or from ‘C’ to ‘T’. In addition, inGAP-family can also identify causal insertions by prediction and scanning of novel SVs on candidate regions for mutants induced by T-DNA insertion. Detected mutations are then annotated according to their annotation models , and are further classified as synonymous or non-synonymous when located in coding regions as potential for change- or loss-of-functions.inGAP-family identifies causative mutations from bulked DNA of multiple progenies with same phenotype at a single-base resolution. In the studies of genetic mapping, mutations are induced on one ecotype by EMS, T-DNA insertion, or others. FArabidopsis thaliana, Columbia  and Landsberg erecta  er is downloaded from the 1001 Genomes project website (http://1001genomes.org/projects/MPISchneeberger2011/index.html) for evaluation of variant calling by different methods. The genome of a meiotic progeny of the two ecotypes  was introduced to test the performance of inGAP-family on uncovering meiotic recombination events. Short reads of Col, Ler, and the meiotic progeny genome were aligned to the TAIR10 assembly of A. thaliana (Col) reference genome To evaluate the performance of inGAP-family, we downloaded the whole genome sequences of two ecotypes of A. thaliana mutant in Ler ecotype, carrying a recessive mutation on the ABA DEFICIENT 3  gene involved in abscisic acid biosynthesis and salt tolerance. The mutant was backcrossed with wild-type Col, and 100 F2 progenies with salt-tolerance phenotype were selected for pooled DNA sequencing. The bulked DNA was sequenced on an Illumina HiSeq 2000 platform, providing 44 million 101-bp paired-end reads . High-confidence polymorphisms between Col and Ler were obtained as described above. Short reads were aligned to the TAIR10 reference genome using BWA We also tested the effectiveness of inGAP-family on detection of causal mutations based on genetic mapping. We obtained an EMS-induced A. thaliana, which is widely used for genetic and genomic analyses on gene functions er, revealed by many resequencing data Eukaryotic genomes usually contain large amount of repetitive sequences including transposable elements (TEs), gene duplicates, and tandemly repetitive oligonucleotides that may differ in copy number among species. The genomic SVs must be carefully considered for calling of polymorphisms and phenotype detection. The model plant er on the Col reference genome, some reads from TEs  exhibiting single copy in Col genome , while three artificial SNPs were called when non-allelic reads from the non-allelic duplicate were mapped to the same site. As there are 3903 annotated TE genes and more repetitive sequences according to the annotation of Col genome, obtaining reliable allelic polymorphisms is challengeable for genetic studies.CNVs can also lead to false positive variant calls or genotypes due to wrong mapping of non-allelic reads. We totally identified 1463 regions with singe copy in Col but two or more duplicates in Ler reads B and C ier genome , provided by 1001 Genomes Consortium We compared the performance of inGAP-family, SAMtools er have similar sequences in different loci causing false mapping of non-allelic short reads on reference genome. Furthermore, 4204, 6808, and 6823 artificial SNPs associated with transpositions, duplications, and complex SVs, respectively, were then removed from the final SNP list. Second, the read depth and allele ratio for each polymorphism were examined for recognizing artificial or unreliable SNP calls. In addition, 13,746 SNPs were ignored due to insufficient number of supported reads or abnormal coverage of depths. Similarly, 100,926 of 115,495 small indel candidates were retained by filtering out those located in various types of SVs and tandemly repeated regions.After removing those false predictions due to misalignment of non-allelic reads in SV regions and other factors , inGAP-fer and Col ecotypes er (SRX202247). Furthermore, compared with 992 algorithm-specific SNPs reported by inGAP-family, GATK reported 132,091 additional SNPs supported by neither 1001 Genomes Consortium nor inGAP-family  on the genome, and plays a critical role in ensuring the proper segregation of chromosomes 2 plant hybridized from Col and Ler to evaluate the performance of inGAP-family. Short reads of the F2 plant were aligned against the Col reference genome using BWA 2 plant was split into 11,637 sliding windows (size of 20 kb with step of 10 kb), and allele ratio was estimated on each window for genotyping (see Method). As a result, for example, Chr1, containing 2972 windows, was divided into three continuous blocks representing different genotypes: the first 20.2 Mb of Chr1 was genotyped as Ler homozygous region, the next 5.3 Mb region was heterozygous, and the last 3.5 Mb region was Col homozygous  is genotyped, inGAP-family can easily identify novel mutations, including substitutions, small indels, and large-scale SVs, within windows with expected allelic ratios. To test the performance of inGAP-family upon real case studies, we sequenced a DNA pool of 100 F2 progenies with salt-tolerance phenotype hybridized from an EMS-induced Col mutant and a wild-type Ler ecotype (see Method). A total of 44 million paired-end reads (2 × 100 bp) were obtained on an Illumina platform, providing a sequencing depth of 25× on average. The reference genome was partitioned into 1167 windows, for which genotype was determined according to the allelic ratio. A region in Chr1  was found to exhibit the expected allele ratio , and thus affected the alternative splicing of its transcript , whose product functions in the abscisic acid biosynthesis pathway , peanut (Arachis hypogaea), potato (Solanum tuberosum), rapeseed (Brassica napus), and sugarcane (Saccharum spontaneum L.), contain two or more sets of chromosomes with high sequence similarity, bringing great challenges for short read mapping and subsequent SV identification. These SVs are difficult to be accurately identified by short NGS reads. This problem can be partially overcome by incorporating with data of the third-generation high-throughput sequencing platforms, e.g., Pacific Biosciences An essential concern of the procedure of inGAP-family is to remove artificial variants brought by non-allelic sequences from large-scale SVs and other types of repetitive sequences. SVs including large deletions, insertions, and duplications are detected by inGAP-sv, which exhibits better performance than other popularly used methods  and is aDe novo mutations (DNMs) make important contributions to genome diversity across many species e.g., PolyMutt e.g., analysis of meiotic recombination, genetic mapping, and detection of causal mutations. In this study, we employ an integrated comprehensive strategy, inGAP-family, to predict and refine SNPs and small indels directly from NGS sequence mappings or from polymorphisms of third-party software (with VCF format), e.g., GATK In summary, the advances of NGS technologies and computational methods for variation discovery and genotyping have greatly facilitated the identification of DNA polymorphisms and large-scale genomic variants, and promoted many genetic studies depending on molecular markers. Although many tools are available for variant calling https://sourceforge.net/projects/ingap-family/.inGAP-family is written in platform-independent Java programming language, and can be freely accessed at CRA002018), and are publicly accessible at https://ngdc.cncb.ac.cn/gsa.Sequencing data in this study have been deposited in the Genome Sequence Archive Qichao Lian: Methodology, Software, Formal analysis, Writing - original draft, Visualization. Yamao Chen: Investigation, Writing - review & editing. Fang Chang: Validation. Ying Fu: Validation. Ji Qi: Supervision, Conceptualization, Software, Writing - review & editing. All authors have read and approved the final manuscript.The authors have declared no competing interests.


Traits such as seed weight, shelling percent, percent sound mature kernels, and seed dormancy determines the quality of peanut seed. Few QTL (quantitative trait loci) studies using biparental mapping populations have identified QTL for seed dormancy and seed grade traits. Here, we report a genome-wide association study (GWAS) to detect marker–trait associations for seed germination, dormancy, and seed grading traits in peanut. A total of 120 accessions from the U.S. peanut mini-core collection were evaluated for seed quality traits and genotyped using Axiom SNP (single nucleotide polymorphism) array for peanut. We observed significant variation in seed quality traits in different accessions and different botanical varieties. Through GWAS, we were able to identify multiple regions associated with sound mature kernels, seed weight, shelling percent, seed germination, and dormancy. Some of the genomic regions that were SNP associated with these traits aligned with previously known QTLs. For instance, QTL for seed dormancy has been reported on chromosome A05, and we also found SNP on the same chromosome associated with seed dormancy, explaining around 20% of phenotypic variation. In addition, we found novel genomic regions associated with seed grading, seed germination, and dormancy traits. SNP markers associated with seed quality and dormancy identified here can accelerate the selection process. Further, exploring the function of candidate genes identified in the vicinity of the associated marker will assist in understanding the complex genetic network that governs seed quality. Arachis contains 30 diploid wild species, a wild tetraploid species (Arachismonticola Krapov. & Rigoni), and a cultivated peanut (Arachishypogaea L.) . Th. Th16]. ® fungicide  before evaluating the accessions for seed germination. Two replications of 25 seeds were tested for seed germination by spreading them on a soaked germination paper towel and covering them with another paper towel. The temperature of the growth chamber was maintained at 30 °C for 12 h with 8 h of light and 20 °C for 12 h without any light, and the relative humidity was maintained at 85%. The number of germinated seeds was measured at 7, 14, and 21 days. A seed was declared dead if it showed no sign of germination and was rotten.The procedure for seed germination testing has been documented in the previous publication . The weiThe grading criteria, shelling percentage, sound mature kernels (SMKs), and loose shelled kernels (LSKs) were determined nondestructively using a prototype X-ray imaging device for grading peanuts . The LSK is defined as the peanut kernels in the sample that were shelled and separated from the shells or hulls during the harvesting process and is expressed as a percentage of the total sample weight. The percent KRS (kernels riding screen) are the kernels that, after shelling, will ride a 6.4 mm × 19 mm slotted screen and is expressed as a percentage of the total weight of clean in-shell pods. The percent KRS represents the peanuts that are of sufficient size to be sold as edible peanuts. SMKs are the KRS that have been sorted by hand and visually inspected for damage and the damaged/discolored kernels removed. The shelling percentage is the fraction of the peanut pods that are peanut kernels expressed as a percentage of the total weight of in-shell pods. The peanut sample pass under an X-ray energy source on a conveyor belt. A detector positioned below the belt detects the intensity of the X-ray spectrum transmitted through the peanut. A two-dimensional image is analyzed, classifying the particles in the image as foreign material, LSK, or in-shell peanuts. The density of each particle is proportional to the darkness of the gray-scale (darker is more dense), and the weight of the particle is estimated from the projected area and the density of the particle. Image analysis also estimates the weight of in-shell kernels with a minimum diameter greater than or equal to 6.4 mm, the weight of in-shell kernels smaller than 6.4 mm, total kernel weight, and the total hull weight.Leaf tissue samples were collected for each accession, and DNA was isolated using the CTAB protocol. DNA quality and quantity were measured using NanoDrop-2000 . All the samples were genotyped on the Axiom_Arachis array ,18 at Ge®9.2, Cary, NC, USA). Mean, range, and CV% were calculated using Excel, and graphs were drawn using Sigma Plot 13.0 .Correlation was determined between different seed grade, seed weight, germination rate and seed dormancy using the “PROC CORR” command of SAS software  approach as described in the manual of GAPIT software  and softr2 = Y = Xβ + Zu + e. Here, Y is the vector of phenotype, X is incidence matrix for fixed effects, β is coefficient vector for fixed effects, Z is incidence matrix for random additive genetic effects, u is coefficients vector of random effect, and vector of random residuals is denoted by e. A Manhattan plot of the −log10 p value), and genome-wide association analysis was performed as described in a previous publication . A totalD blocks . A totalD blocks  with the-package .Arachis hypogaea, Tifrunner) genome to identify candidate genes [Arachis hypogaea v1.0 gene models from https://peanutbase.org/gbrowse_peanut1.0 (access on 7 July 2020) and extracted using Python script.We used a recent sequence of tetraploid peanut cultivar  suggest that shelling percent is the least variable trait, while seed weight, LSK, and SMK are highly variable traits among the mini-core accessions .r2 of 0.2 in the SVS software. Population analysis was conducted by structure analysis using high-quality SNPs  and 69 (suggestive) genomic regions were found for seed characteristics, i.e., SMK, LSK, shelling percent, and 100 seed weight . MarkersGenes that play a putative role in seed development or germination were searched in the vicinity of the significant markers. A total of 832 genes were identified in the vicinity of 11 significantly associated SNPs (excluding redundant associated SNPs) to seed germination and dormancy , Table 4QTLs studies use biparental populations with few recombination opportunities and are restricted to genetic variation between two parents. In contrast, a GWAS study with a diversity panel can provide higher resolution and identify more genomic regions that contribute to the quantitative traits. In the current GWAS study, we did corroborate previously known genomic regions for seed, pod, and germination traits. We identified some novel genomic regions that have never been reported for these traits. In total, we identified 277 SNPs associated (excluding redundant associated SNPs)  with eitMany traits studied here had a significant correlation, suggesting a good chance of co-localizing these quantitative traits on the same chromosome. SMK and shelling percent had a strong correlation of −0.4, and we found that SNPs strongly associated with these traits were co-localized on chromosomes A05, B05, and B08 . ShellinIt was also discerned that SNPs associated with a trait were present on both homoeologous chromosomes, thus suggesting similar genomic regions of A and B subgenomes contribute to these traits. For example, we identified SNPs associated with 100 seed weight on chromosomes A02, B02, A10, and B10. Similarly, we identified SNPs related to SMK on chromosomes A05, B05, A08, and B08. For shelling percent, we found associated SNPs on chromosomes A03, B03, A05, B05, A06, B06, A10, and B10.We identified an SNP on chromosome A05 to be associated with seed germination as well as seed dormancy, and it explains about 20% of phenotypic variation. Previous research work has identified a major QTL on chromosome A05 for seed dormancy . AnotherWe also identified genomic regions on chromosomes A02, A04, A06, A08, and B08 to be strongly associated with seed dormancy and early seed germination. Such regions might also be vital for peanut breeding program for seed dormancy, which have been missed in QTL studies contrived on fewer biotypes of the peanut core collection. These regions also contain many crucial genes for seed dormancy, for example, in vicinity of associated region of Chr. A04, we found multiple copies of chalcone synthase, cytochrome P450 (Arahy.10MYPD), WRKY transcription factor ,41, diffFor SMK, we identified two strongly associated genomic regions on chromosomes A07 and B05. A QTL study using a RIL population found QTL for TSMK  on chromosomes A07 and A05 (homoeologous of B05). A05 and B05 chromosomes are also known to have QTLs for seed weight, seed size, and pod size ,31. FurtIn summary, we identified previously known and novel genomic regions associated with seed quality traits and germination. Such regions can now be used for enhancing the peanut breeding program. Many candidate genes also identified in the vicinity of the associated marker warrant further research to identify their roles in seed development and germination.


It identifies associated functional haplotypes that may prove more robust in determining the genotypic determinants of desirable crop phenotypes. In brief, riceExplorer evaluates a global crop database (IRRI 3000 Rice Genomes) to identify haplotypes that are associated with extreme phenotypic variation at the global level and recorded in the database. It then examines which potentially useful variants are present in the local crop panel, before distinguishing between those that are already incorporated into the elite breeding accessions and those only found among secondary varieties . Results highlight the effectiveness of our pipeline, identifying potentially useful functional haplotypes across the genome that are absent from elite cultivars and found among landraces and other secondary varieties in our breeding program. riceExplorer can automatically conduct a full genome analysis and produces annotated graphical output of chromosomal maps, potential global diversity sources, and summary tables.Agricultural crop breeding programs, particularly at the national level, typically consist of a core panel of elite breeding cultivars alongside a number of local landrace varieties (or other endemic cultivars) that provide additional sources of phenotypic and genomic variation or contribute as experimental materials . Three issues commonly arise. First, focusing primarily on core development accessions may mean that the potential contributions of landraces or other secondary accessions may be overlooked. Second, elite cultivars may accumulate deleterious alleles away from nontarget loci due to the strong effects of artificial selection. Finally, a tendency to focus solely on SNP-based methods may cause incomplete or erroneous identification of functional variants. In practice, integration of local breeding programs with findings from global database projects may be challenging. First, local GWAS experiments may only indicate useful functional variants according to the diversity of the experimental panel, while other potentially useful loci—identifiable at a global level—may remain undiscovered. Second, large-scale experiments such as GWAS may prove prohibitively costly or logistically challenging for some agencies. Here, we present a fully automated bioinformatics pipeline ( A key is; GWAS) . For exas; GWAS) . Furthers; GWAS) .via induced switching from outbreeding mating systems (e), increases in linkage disequilibrium . For example, in a GWAS, genomic variants exhibiting significant correlations with investigated phenotypes will be identified in the context of the accession panel being investigated . Other pFurthermore, while GWAS methodologies have become a commonplace tool in identifying key functional variants they suffer from some inherent drawbacks. These include low-informativeness of SNP markers , the infIn Thailand, rice is the primary staple food crop, and the country has a well-developed rice improvement research infrastructure spanning several large institutions. Thailand has fully embraced the omics age and also has ongoing genetic modification/editing programs in operation involving rice and other crops . In receHowever, it is unknown whether the genomic potential of the Thai rice resource (TRR) is maximally capable of achieving optimal agricultural performance required to continue to feed the national population, increase its value to local farmers, and increase its commercial output by capitalizing on its status as the planet’s number one rice exporter . As the 3,000 Rice Genomes project (3KRG)  is the curator of the ongoing t (3KRG) . This put (3KRG) , while at (3KRG) , among wt (3KRG) . We devet (3KRG)  that areOryza sativa is likely an amalgam of two distinct lineages hailing from two wild Oryza species that largely delineate distinct indica and japonica variety types  map across all chromosomes that graphically indicates potential LD relationships of both identified haplotypes of interest juxtaposed against positions of key genes previously identified as functionally impacting the evaluated phenotype, construction of a neighbor-joining phylogenetic tree, structure-type plots evaluating population genomic relationships, and whole-genome and individual chromosome site-frequency spectrum plots. Finally, riceExplorer generates several data files formatted for use with various publicly available software permitting subsequent analyses of diversity relationships.Finally, to be of optimal utility for crop breeding programs, it is necessary that a user may interpret catalogued functional haplotype information contextually by evaluating findings with respect to diversity relationships among accessions across investigated panels. Moreover, relatedness reconstruction may permit further analyses such as predictive genetics . For domty types . Thus, driceExplorer pipeline (available on GitHub) is based on previously published, free-to-use software. It is piped together via the Bash programming language with elements of custom-made Python programs that format files between different software applications and perform both quantitative and database search analyses. Thus, it renders an easy-to-understand outputted record along with numerous graphical figures that can be applied to any agricultural focal species (not only rice) with appropriate genotype–phenotype data. Aside from initial stages that require the downloading of publicly available sequence data, our pipeline is straightforward to use, being able to run as a single utility. Importantly, our pipeline represents a financially prudent method by which the user may search their own genomic resources for potentially useful functional variants. This is possible because a full evaluation of a locally held sequenced accession panel can be searched for associated haplotypes without any monetary or logistical investment into large-scale experimental projects requiring phenotypic evaluation. Fundamentally, riceExplorer provides a descriptive database resource that can inform breeding program strategies that may range from traditional breeding methods to modern state-of-the-art genomics-assisted breeding technologies.Our gvcf format. The remaining samples are of external origin sourced from various foreign research agencies .The TRR comprises hundreds of rice germplasm accessions that have undergone whole-genome sequencing. For this study, we used 279 available sequenced accessions  to produ3000 Rice Genomes (3KRG) database among the 41 grain length (GL) genes identified by http://rice.uga.edu/). As π is sensitive to sample size, we used rarefied sampling to control for this confounding factor and reported π directly from the 279 sample TRR panel against the distribution of 100 estimates of π from 279 randomly selected accessions from 2,103 accessed IRRI samples.It order to initially assess Thai rice resource (TRR) panel genomic diversity with respect to grain length, we used the nucleotide diversity π;  metric triceExplorer pipeline comprises an initial, semi-automated first step followed by a fully automated analysis when run in a Linux environment . It can be implemented on any agricultural focal species with appropriate genotype–phenotype data whose annotated gene database is incorporated in the snpEff software suite (The re suite .gvcf format) and associated phenotypic values for individual accessions. Despite the availability of more than 3,000 sequenced accessions, 3KRG recommends (for obvious reasons of tractability and computing constraints) that a core set of 72 accession that best represent global rice diversity is primarily used for analyses. However, despite this recommendation, the 72-accession core panel does not have a complete set of associated phenotypic assays that are also curated under 3KRG (see: https://snp-seek.irri.org/_variety.zul). Of the 72 core accessions, only 49 have data for grain length (GL), the trait which we focus on in this paper.The first step is to decide an appropriate reference library for initial inquiry. For our TRR, the IRRI 3KRG project  is approsampleSelector.py script (available on the GitHub pages). As input, it takes the full available GL data from the 3KRG pages . In order to maximize phenotypic diversity and genetic representation among the reference library, the program identifies the held accessions that have maximal and minimum phenotypic values both within all designated regions  and rice variety types . Consequently, for our analysis, sampleSelector.py indicated a further 151 varieties’ gvcf files to download from 3KRG (3kricegenome.s3.amazonaws.com/Nipponbare/IRIS_XXX-XXXXX.snp.vcf.gz). From this, a data file featuring accession name and associated GL phenotype can be compiled for input to the main pipeline.For this initial step, we have therefore developed the rom 3KRG , alongsigvcf format), associated phenotypic measurements of the reference library, a sequenced focal library , an associated population file describing sample variety types, and a list of genomic regions of interest. For this last item, we use the Michigan State University rice annotation project list of ca. 55,000 annotated genes . In addition, the pipeline can accept a list of annotated genes known to have primary function associated with the focal trait, in order to annotate the final graphical output. For this, we included the 41 genes identified by Li et al.  reformats the bcftools output ready for the next step. For all generated files containing SNP information, we use the snpEff software suite  reconstructs the functional haplotype occurring in each accession. For each accession, the program builds the haplotype according to whether the reference or alternative allele is present at each identified functional SNP location. For example, if an annotated gene region contains four functional SNPs, different functional haplotypes are considered that contain any combination  of those four SNPs  within that gene for each accession. For each reconstructed haplotype, the mean associated phenotype is calculated across all 3KRG individual accessions carrying that haplotype. This is done because the large number of possible haplotype alleles  means it is not possible to conduct a meaningful analysis against individual phenotype scores . If the mean associated phenotype for those accessions (and the haplotype is found across a minimum of five accessions) is greater or less than  the user-stipulated percentile range from the distribution of the phenotypic data, that haplotype is recorded as being associated with extreme phenotypes  within the reference library panel. For our analysis, we chose a percentile value of 0.866 which represents 1.5 standard deviations from the mean .The next pipeline process uses outputted bcftools) to: (i) identify presence/absence of the identified haplotypes, and (ii) evaluate whether that haplotype is present/absent among elite cultivars (ECs), or more pertinently, non-focal varieties (NFVs) in the breeding program panel using the potential_rgd_sourcesLoop.py Python script. Finally, the pipeline outputs a number of summary tables and annotated graphical outputs according to evaluated chromosomes in the analysis.The final pipeline step then searches the focal  sequence data (using lim assignments (http://rice.uga.edu/downloads_gad.shtml). To easily visualize common functional types, we removed common technical terms from the GOSlim putative function assessments and performed word cloud analyses based on number of hits using the Python library “wordcloud” (https://pypi.org/project/wordcloud/). Full results are also tabulated.To assess the most commonly identified gene functions identified by our pipeline, we converted all identified MSU annotated genes into their GOSriceExplorer pipeline performs a number of subsequent analyses to evaluate diversity relationships between accessions and linkage disequilibrium patterns within chromosomes across both the reference and focal sequencing libraries. Additionally, the pipeline also outputs formatted files that may be used as input for more robust phylogenetic and demographic analyses. Alongside publicly available software, an additional 12 custom Python scripts are included with the GitHub pages for these analyses (described below).In order that the user may evaluate the potential value of identified LGHs, the cftools. vcf files are compiled for individual chromosomes and then also merged for a whole genome summary. Across the genome from our investigated libraries, a total of 16,596 SNPs were recovered that passed the quality control criteria.To minimize computation time, the pipeline first identifies SNPs with high coverage across accessions. We assume that the employed reference library is of high quality and therefore coverage across the focal library is evaluated. A single SNP with the highest coverage that passes several quality criteria  is selected from each examined gene region. SNPs across accessions are then called using Bvcf files. As indica is by far the favored rice variety type in Thailand and our focal panel consists of 257 out 279 cultivars, LD was only calculated among indica accessions to (a) provide appropriate analyses, and (b) to avoid incorporating distinct and potentially confusing LD relationships likely to be found among different variety types. From the resultant matrices, a custom Python script then plots the LD heat maps. However, this script also incorporates the LGH data generated from the primary pipeline in order to annotate the LD plots so that identified LGHs are positionally marked according to the chromosomal location. Graphical outputs are generated for all chromosomes with individual plots generated according to whether LGHs are high or low extreme values or whether they have been identified within elite cultivars or non-focal varieties, and their positions relative to previously identified genes of interest.First, the R packages LDheatmap  and snpSvcf is converted into Fasta format retaining the reference/alternative allele base calls. Next, custom Python scripts calculate pairwise Kimura-2-parameter (K2P) distances  global database  at 295 MSU annotated genes across all accession types  haplotypes per sampled accession on chromosome 7. The Americas and Southeast Asian samples contributed the most to japonica LGHs (around 50%) at 6.7 and 4.7 haplotypes per sampled accession, respectively. The Americas and the Subcontinent contribute most to admix LGHs are , while African samples contributed most (7.0) per accession. Few LGHs were identified on aus and aro 3KRG samples. This pattern on chromosome 7 of mostly indica LGH contributions is typical across all other chromosomes (riceExplorer identified (In order to: (i) understand the distribution of LGHs found within NFVs: (ii) further understand the origins of genomic diversity among Thai rice; and (iii) catalog genomic diversity among Thai rice, our bioinformatics pipeline outputs a summary figure that breaks down potential geographic haplotype origins according to rice variety group  has 15 LGHs on chromosome 7  and focal (TRR) panels, cluster together in a monophyletic clade, with IRRI aus and adm biotypes forming sister clades . However, in contrast to phylogenetic findings  and provide a useful catalogued record of their untapped potential genomic contribution.Agricultural crop breeding programs, potentially conducted at the national level, may primarily focus on the performance and development of elite focal cultivars (ECs). Attention will normally focus on development and improvement of ECs in the forms of marker-assisted selection, gene-editing and other forms of selective breeding that may have encouraged powerful directional selection. Such anthropologically mediated evolutionary forces may induce a plethora of incidental outcomes that encourage the accrual of non-lethal deleterious mutations across nontarget regions of the genome . This hasnpEff software)  , and ass regions  against LOC_ Os07g39220, LOC_Os07g41200, LOC_Os07g41240 and LOC_Os07g42410; riceExplorer indicate that the landrace accession Ai-Tai (W00223) contains 15 LGHs in this region  for this chromosome. This is unsurprising as most of the TRR panel comprises indica varieties that is the favored staple rice type in Thailand. Most of these (> 50%) originate from varieties from Africa and the Americas in the 3KRG panel. However, our pipeline also controls for sample size and evaluates haplotype sources according to the contribution of the number of accessions in the original reference  panel. When controlling for this sampling bias, we show that European accessions (which provided only 6% of IRRI accessions) associate the most with LGHs (6.7) per accession for indica on chromosome 7. This result is likely sensible. If the Thai breeding program has mostly focused on endemic accessions and others from the nearby regions , we may expect that novel LGHs are likely to disproportionately come from other regional accession panels. Thus, future research within Thailand may consider incorporating more European indica accessions into the program in the hope that more useful gene regions that are compatible with the TRR may be identified. Such reasoning may prove fruitful, as theory dictates that hybrid vigor may yield more useful phenotypes  panel. Further outputs allow evaluation of site-frequency spectrum (SFS) patterns  may aid predictive breeding investigations .We expect that our pipeline has provided a starting point to various avenues of research that may lead to the incorporation of available genomic resources into TRR ECs. This may improve the genetic background of future varieties developed to exhibit robust, high-yield stress tolerant phenotypes. With respect to our own dataset, it appears that chromosome 7, at least, including specific genes identified by the pipeline, should be considered as an area of potential improvement within the TRR. Finally, while we present  1–4 see  against riceExplorer bioinformatics pipeline provides such a system that is easy to implement, cheap to use, and provides a catalog of potentially useful loci referenced against global databases. Our analyses show the potential of riceExplorer having identified 114 gene regions where potentially useful genomic haplotypes associated with a key agronomic trait (grain length) are found in NFVs, while absent among ECs.Agricultural crop breeding programs are likely to (i) exaggerate deleterious alleles at nontarget loci among focal elite cultivars (ECs), (ii) overlook the potential genomic resources residing in their non-focal variety (NFV) accessions, and (iii) over-rely on SNP-based methodologies featuring notable drawbacks. As a result, it is desirable to have an automated genomic cataloging system to monitor crop panels at the haplotype level. Our The original contributions presented in the study are included in the article/CD, TT, and SW: conceptualization. CD: carried out programming, workflow design, and writing the original draft. SW, VR, MS, and WA: molecular biology and sequencing. BT: genomic data curation. CD and SW: writing the review and editing. TT: supervision. All authors contributed to the article and approved the submitted version.This study was supported by grant from National Science and Technology Development Agency, Thailand (NSTDA grant number: P-18–51456).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.


TMEM154). We estimated the haplotype frequencies of TMEM154 in the USA (USDA-ARS) and Brazil (Embrapa) Gene Banks by using two different SNP genotyping methodologies, FluidigmTM and KASPTM. We also genotyped the ZNF389_ss748775100 deletion variant in Brazilian flocks. A total of 1040 blood samples and 112 semen samples from 15 Brazilian breeds were genotyped with Fluidigm for the SNP ZNF389_ss748775100 and 12 TMEM154 SNPs. A total of 484 blood samples from the Santa Inês breed and 188 semen samples from 14 North American sheep breeds were genotyped with KASP for 6 TMEM154 SNPs. All the Brazilian samples had the “I/I” genotype for the ZNF389_ss748775100 mutation. There were 25 TMEM154 haplotypes distributed across the Brazilian breeds, and 4 haplotypes in the US breeds. Haplotypes associated with susceptibility were present in almost all breeds, which suggests that genetic testing can help to improve herd health and productivity by selecting non-susceptible animals as founders of the next generations. Fluidigm and KASP are reliable assays when compared with Beadchip arrays. Further studies are necessary to understand the unknown role of TMEM154 mutations, host–pathogen interaction and new genes associated with the clinical condition.Small ruminant lentiviruses (SRLVs) affect sheep and goats worldwide. The major gene related to SRLV infections is the Transmembrane Protein Gene 154 ( Lentivirus in the Retroviridae family. They are distributed worldwide, causing significant economic losses in many countries . Endpoint fluorescence image data were acquired on the EP1 Fluidigm imager, and the genotype calls were obtained by the Fluidigm SNP Genotyping Analysis Software.We used two genotyping methodologies in the present study: http://www.lgcgenomics.com/genotyping/kasp-genotyping-reagents/ accessed on 12 July 2015]. Oligonucleotides were designed with the Kraken™ software system according to the manufacturer’s instructions .The North American breeds and the Santa Inês samples from the CPATC were genotyped by KASP, using 10 ηg of DNA per sample in a 96-well plate format. The reaction and the components used in the KASP methodology are described at [OAR17_5388531 (rs414338245), TMEM154_N70I (rs427737740), TMEM154_E35K (rs408593969), TMEM154_I102T, TMEM154_A13V-Fl2 and TMEM154_T25I were genotyped by both the KASP and Fluidigm methodologies. The SNP ZNF389_ss748775100 and SNPs TMEM154_D33N (rs429882112), TMEM154_E31Q_v2, TMEM154_I74F (rs410216979), TMEM154_L14H, TMEM154_T44M (rs420489630) and TMEM154_E84Y were genotyped only by the Fluidigm methodology. The ZNF389 deletion variant NC_019477.1: g.29500068_29500069delAT ovine chromosome 20, NCBI dbSNP ss748775100) was genotyped with CGAATGGATCTTCAAGGCTTA and CAGCTTTTCCATGCAGAGTC as amplification primers, designed according to White et al. [TMEM154 SNPs were designed according to Heaton et al. [The SNPs e et al.  and obsen et al. , observiOAR17_5388531 within Illumina® Ovine SNP50 and Illumina® Ovine 600K were used to determine genotype reproducibility among methodologies. We were able to access 14 and 7 common animals between Fluidigm and Illumina® Ovine SNP50 and Illumina® Ovine 600K, respectively  in 27 samples  [TMEM154_E35K and OAR17_5388531 SNPs using r2 statistics [Chi-square test (p < 0.05) [Clustering was used to define the genotype classes for each SNP for both methods and was processed using GenomeStudio . The confidence threshold for each genotype was >0.90 for both methodologies. Samples, in batches of 96, underwent quality control using SNP & Variation Suite v8.9.1—SVS  , eliminaatistics  was alsoatistics . Allele atistics . Monomor < 0.05)  was perfTMEM154_I102T, TMEM154_A13V and TMEM154_T25I). After the quality control of the Fluidigm methodology, 845 samples with a call rate >0.80 remained. The SNPs OAR17_5388531, TMEM154_A13V-Fl2, TMEM154_D33N, TMEM154_E31Q_v2, TMEM154_E35K, TMEM154_I74F, TMEM154_N70I, TMEM154_T44M and TMEM154_E84Y successfully passed quality control. The SNPs (TMEM154_L14H and TMEM154_T25I) with a call rate <0.75 were excluded from the analysis. The SNP TMEM154_I102T was monomorphic, presenting the allele “T” in all breeds genotyped and was not included in the haplotype analysis. The locus ZNF389_ss748775100 was also monomorphic in all populations genotyped in this study, with all breeds homozygous for the “I/I” genotype. The r2 measure between the TMEM154_E35K and OAR17_5388531 alleles across all populations were 0.96 and 0.93 on Fluidigm and KASP, respectively, indicating a strong LD between the allele “C” of the locus OAR17_5388531 and the allele “G” of the SNP TMEM154_E35K.All SNPs and samples passed the quality control filters for the KASP methodology. From the 6 SNPs genotyped, 3 were monomorphic in all breeds (OAR17_5388531 ranged from 0.0% in the Hog Island breed to 100% in the Katahdin and Damara breeds (TMEM154 E35 (highly susceptible haplotypes 2 and 3) and, consequently, are animals that are more prone to develop the disease. Alternatively, breeds with a low frequency of the C allele have a higher frequency of TMEM154 K35 (less-susceptible haplotype 1), and, therefore, are animals that are less susceptible to OPP.The frequency of the ‘‘C’’ allele of the SNP a breeds . Breeds TMEM154 gene  generated 25 different haplotypes for the Brazilian sheep. The populations with the highest and lowest number of haplotypes were the BBGA and Damara, with 12 and 1, respectively. Among these haplotypes, the most common was ‘‘AQNETNFEI’’ (highly susceptible) (20%), occurring in 13 of the 16 breeds, followed by ‘‘AQDKTNFEI’’ (susceptible) (16%), which occurred in 13 breeds. The rarest haplotypes were “AQNETIFYΔI” (3%), found only in the Dorper breed, followed by ‘‘AQDKMNFEI’’ and AQNEMIIEI’’, both with a 1% frequency in the BBGA samples. Other rare haplotypes were ‘‘AQDEMNFEI’’ and ‘‘AQNKMNFEI’’, with a 1% frequency and present only in the Bergamasca breed. Furthermore, the haplotypes ‘‘AQNETNIYΔI’’ (1%) and ‘‘AQNEMNIYΔI’’ (1%) were unique to the Pantaneiro sheep. Finally, the haplotype ‘‘AQDKTIFYΔI’’ (1%) was detected only in the Rabo Largo breed, and the haplotypes ‘‘AQDETNIEI’’ (1%) and ‘‘AQDETIIEI’’ (1%), were exclusive to the Santa Inês  generated 4 haplotypes among the US breeds and the Santa Inês from the Embrapa Tabuleiros Costeiros (CPATC) (The 6 SNPs genotyped with the KASP methodology ( (CPATC) . The mosOAR17_5388531 was genotyped on 14 common animals on both Fluidigm and Illumina® Ovine SNP50. The reproducibility of the genotypes between methodologies was 100% , and, once again, a total of 20 alleles were the same between the methodologies  and the Embrapa Meio Norte-PI (CPAMN) populations have “T” as their highest frequency allele with frequencies of 0.75 and 0.63, respectively  . Furthermore, the EBDA-BA has a frequency of 0.5 for each allele. For some loci, e.g., TMEM154_A13V-Fl2 and TMEM154_E31Q_v2, we observed rare alleles with low frequencies in single populations: the CNPC  and CPAMN , respectively .For some loci, a difference in the allele frequency was observed according to the geographic region of origin of the Santa Inês flock. The  > 0.05) . Similarectively . Ultimatulations . Still, TMEM154_L14H and TMEM154_T25I) as they showed call rates <0.75 in 11 of the 12 plates genotyped. The quality filter for samples eliminated 307 samples with a call rate <0.80 in this methodology. This may be due to the high confidence threshold set up for each genotype, >0.90, increasing the accuracy of the results. It is important to mention that the SNP TMEM154_I102T was monomorphic in all breeds genotyped across both methodologies.The KASP method was 100% successful, as all designed SNPs were converted into primers and genotyped, and none of the SNPs or samples were eliminated by call rate in this methodology. For the Fluidigm methodology, we designed 13 assays and performed 12 different runs with 96.96 plates, in which each run has to be considered a separate event. Analysis of the entire dataset resulted in the elimination of 2 of the 13 SNPs  in the Fluidigm methodology can be explained by the higher confidence call, and each 96.96 plate genotyped represents a different PCR reaction, considered an independent genotyping event.TMEM154 are considered highly susceptible to OPP. In contrast, haplotypes encoding (K) lysine at the same position are considered less susceptible [OAR17_5388531 and TMEM154_E35K was expected and is in accordance with the results obtained by a previous study [OAR17_5388531 among the populations, with a high frequency of the “C” allele in all populations analyzed. A high value of r2 indicates that the SNP OAR17_5388531 can estimate highly susceptible TMEM154 alleles in all the studied populations. OAR17_5388531 can be used as a surrogate to relate the “C” allele with susceptibility, easing the genotyping process for breeders that have already performed it using Illumina® Ovine SNP50 BeadChip or Illumina® Ovine 600K BeadChip. Breed populations with a higher occurrence of the “C” allele are predicted to have a higher frequency of K35 animals, which is associated with a lower susceptibility to the disease [Haplotypes encoding (E) glutamate at position 35 of ceptible . The higus study . Mainly, disease .It is expected that distinct Santa Inês populations within the county present differences in allele frequencies, as demonstrated by previous authors with neutral markers . The hypThe selection of animals with favorable haplotypes is crucial to ensure the quality and the genetic variability of the germplasm stored in biobanks. Therefore, results such as those found here, where a single population of a breed has a rare allele or differences in allele frequencies, can aid in the long-term conservation of breed diversity and function as an important resource for breeders.TMEM154_I102T variant was observed for the first time in the Santa Inês breed, during resequencing by a previous study [TMEM154_N70I variant. However, in our study, none of the populations presents heterozygous animals for the TMEM154_I102T locus. The TMEM154_N70I locus in the Santa Inês breed has a frequency of 0.6% for the allele “A” and 0.4% for the allele “T”. For the Santa Inês from the CPATC, the frequency was 0.7% for the allele “A” and 0.3% for the allele “T”, indicating heterozygous animals in the flocks. Overall, the allele “T” of the TMEM154_N70I locus has a total frequency of 5.9% in Brazilian sheep and 23.7% in North American sheep. Heaton et al. [TMEM154_I102T based on two reads of nine with high-quality scores, but, here, we could not reproduce the rare genotype found by the authors in any of the Brazilian breeds. We used this variant to test the reproducibility and check if this was an exclusive mutation of the Santa Inês breed.The us study . The autn et al.  called tK35 variant of TMEM154 had a reduced incidence of OPP infection, which leads to an improvement in productivity. Furthermore, the author suggests that the selection of sheep with the TMEM154 haplotype “1” in flocks with a high frequency of haplotype “3”, can be a cost-effective alternative to reduce the economic damage caused by OPP. However, further investigation is required to understand the effect of other TMEM154 mutations on the susceptibility to the disease and whether these effects can be extended to multiple breeds.Heaton et al.  defined TMEM154 is suitable for specific SRLVs strains and ovine breeds. Nonetheless, the same authors propose that generalization to the whole genetic spectrum of Lentiviruses, ovine breeds and epidemiological situation across the globe may need further validation.SRLVs present high genetic variability, contributing to the evolution of multiple viral strains worldwide . MVV-likTMEM154_E35K with a susceptibility to the disease must be undertaken carefully as, in the Merinoland breed, a high number of KK (less-susceptible genotype) sheep was positive for SRLV. Therefore, follow-up breed-specific studies can be useful to detect new TMEM154 variants associated with the development of MV, as well as to understand the unknown role of already identified markers. Furthermore, other genes such as Ovine-DRB1 can be associated with a susceptibility to MV, implying that studies aimed at understanding other genetic factors involved in the occurrence of SRLVs are crucial [Molaee et al.  indicate crucial .ZNF389_ss748775100 and higher proviral concentrations in Rambouillet, Polypay and Columbia sheep from Idaho, US. Conversely, in our study, no breeds genotyped presented the deletion allele, as all populations were homozygous for the insertion “I/I”. According to White et al. [White et al.  establise et al. , animalsZNF389 has been previously associated with MV, further investigation is required to understand the functional importance of this genomic region. Zinc finger proteins (ZFPs) are characteristic DNA binding domains that can be found in a variety of transcription factors [ZNF389 plays a biological role related to ovine lentivirus proviral concentration in sheep. The same authors propose that one or more zinc finger genes located in this region can act as a transcriptional regulator of host genes, such as TRIM5a, limiting the proviral replication of the virus [Although  factors . White e factors  implicathe virus ,50.ZNF389_ss748775100 deletion variant genotypes, the TMEM154 haplotypes and their frequencies in the breeds can help to select animals with favorable alleles and haplotypes. Nevertheless, gene banks should store all genotypes to facilitate future work on susceptibility and resistance. Consequently, these methodologies can be reliable resources for breeders if MV becomes a threat, thus aiding in the conservation of breed diversity.The results obtained in this study can be of paramount importance for the Brazilian and American gene banks. For instance, investigation of the TMEM154 mutations among different Santa Inês populations, resulting from the genetic subdivision previously observed in the breed. Contrary to the insertion allele, the ZNF389_ss748775100 deletion variant is associated with higher proviral concentrations in sheep. However, we did not detect the deletion variant in any of the breeds here.Overall, haplotypes associated with a lower susceptibility risk to OPP are common in both Brazilian and North American sheep. This suggests an opportunity to reduce Lentivirus susceptibility in multiple sheep breeds using genomic selection. There is a significant difference in the allele frequencies of the TMEM154 haplotypes and their frequency suggest a range of genetic variability has been captured by the gene banks. A few breeds showed a lack of variability, which was likely due to a small sample size. Finally, the comparison of the results obtained from the Fluidigm and KASP assays with more robust technologies, such as the 50k and the HD panels, indicate that these methodologies are reliable. Therefore, they can be useful, cost-effective tools to improve genomic selection programs. Further validation is still necessary to understand the unknown role of some of the TMEM154 mutations and the role of other genetic factors associated with the disease.Genetic variability is crucial to ensure the quality of the material stored in conservation centers and gene banks. The results found here of the


Hordeum vulgare L. var. nudum Hook. f.) yield. Genome-wide association study was performed using 269 lines of hulless barley to identify single-nucleotide polymorphism (SNP) markers associated with drought-resistance traits. The plants were cultured under either normal or drought conditions, and various quantitative traits including shoot fresh weight, shoot dry weight, root fresh weight, root dry weight, leaf fresh weight, leaf saturated fresh weight, leaf dry weight, ratio of root and shoot fresh weight, ratio of root and shoot dry weight, shoot water loss rate, root water loss rate, leaf water content and leaf relative water content, and field phenotypes including main spike length, grain number per plant, grain weight per plant, thousand grain weight (TGW), main spike number, plant height, and effective spike number of plants were collected. After genotyping the plants, a total of 8,936,130 highly consistent population SNP markers were obtained with integrity > 0.5 and minor allele frequency > 0.05. Eight candidate genes potentially contributed to the hulless barley drought resistance were obtained at loci near significant SNPs. For example, EMB506, DCR, and APD2 genes for effective spike number of plants, ABCG11 gene for main spike number (MEN), CLPR2 gene for main spike length, YIP4B gene for root and shoot dry weight (RSWD), and GLYK and BTS genes for TGW. The SNPs and candidate genes identified in this study will be useful in hulless barley breeding under drought resistance.Drought stress is one of the main factors restricting hulless barley ( Plants live in complex and changeable environmental conditions, often bring huge misfortune on plant growth Zhu, . As the Hordeum vulgare L. var. nudum Hook. f.) is an important economic crop  is just such a powerful tool for connecting genotypes–phenotypes  to genotype natural populations. Using linear mixed model (LMM) and EmMax, the association between the quantitative traits of drought tolerance and genotype was analyzed, and the SNP loci and chromosome segments significantly associated with the target traits were screened.The 269 hulless barley lines with different drought resistance assessment were used as the GWAS panel in this study . PhenotyIn 2021, 269 pieces of hulless barley lines were planted in germinating boxes and cultured in greenhouse to two leaves stage. Whole-genome DNA of each germplasm resource leaves was extracted by CTAB method  method  with Kimura 2-parameter (K2-P) model using MEGA X software  scatter plot and Manhattan plot were made by the qqman package in R software.Based on the developed high-density SNP molecular markers, GEMMA, FaST-LMM, and EMMAX were used for association analysis. Correlation analysis between phenotypic value of drought-tolerant-resistant traits and genotypes was carried out to obtain the http://plants.ensembl.org/index.html). All genes with coding regions in the 100–500-kb window were used for subsequent analysis.To screen the drought-tolerant-resistant genes near the significant associative loci, the genetic information of specific association regions was queried from barley genome in plant whole-genome information database (The molecular markers of 269 hulless barley lines were developed by Specific-Locus Amplified Fragment Sequencing (LAF-SEQ) to obtain molecular markers in the whole genome. An average of 311,695 SLAF tags were developed per sample for a total of 862,999, including 480,790 polymorphic SLAF tags and 5,532,468 SNP markers. The average sequencing depth of SLAF tags was 10.43 ×, and 1,067.96 Mb reads data were generated. These markers were evenly distributed on the chromosomes of hulless barley . The aveThe 269 lines were divided into 6 groups according to the geographic location information of the samples, and the group information was used for linkage disequilibrium (LD) and evolutionary tree analysis. The phylogenetic tree was colored by the clustering result of STRUCTURE, basically, each cluster was gathered into one block in the phylogenetic tree, especially Q5 and Q6 .Using the SNP information mentioned above, the principal component analysis (PCA) was conducted. The top three principal components could explain 34.23% of the genomic variations, and principal component 1 could explain 18.3%. Consistent with the phylogenetic tree, Q5 and Q6 were separate from other populations .r2. The closer r2 is to 1, the stronger the linkage disequilibrium intensity. The distance between SNPs and r2 was fitted, and the curve of r2 changes with distance was presented. Generally speaking, the closer the distance between SNPs is, the larger r2 is and vice versa. The LD decay (LDD) distance was used as the distance traveled when the maximum r2 value dropped to half. The longer LDD, the lower the probability of recombination within the same physical distance. It should be noted that some regions of Q5 and Q6 had very strong linkage, but the length of the strong linkage was short, indicating that these two groups were subjected to some artificial selection pressure and some loci were selected, leading to linkage in some regions  and locus integrity (integrity > 0.8) to obtain highly consistent SNP loci for GWAS analysis. Genome-wide association analysis was performed using LMM, EMMAX, and FaST-LMM models, respectively. The following table showed the number of significant SNP markers obtained for each phenotype corresponding to each mode and the number of common SNP markers in each model .p-values and expected p-values for each SNP marker . Gene EMB506 is expressed at flowering and heading stage and closely associated with the character (spike number)  gene, which is required for cutin polyester formation , one gene interrelated with chloroplast development and plant growth named Probable GTP-binding protein (OBGC1), and three bacterial infection related genes Peroxidase 2 (PRX112), Probable acyl-CoA dehydrogenase (IBR3) and Ethylene-responsive transcription factor (RAP2-3). The ABC transporter G family member 11 (ABCG11) gene located on chromosome 2H was highly expressed in flowers and young seeds and was closely related to spike number  connected with main spike length (MSL) phenotype. Thereinto, ATP-dependent Clp protease proteolytic subunit-related protein 1 (CLPR) is considered to regulate chloroplast and plant development. Deletion of CLPR alleles resulted in embryonic development delay and leaf albinism  gene. The YIP4B regulates cell wall composition and participate in root and hypocotyl elongation . The second SNP peak was found at chr3H_489630701, and mapped to iron accumulation associated gene Geranylgeranyl pyrophosphate synthase (BTS).Hulless barley is rich in nutrients and is the main food source for Tibetan people , main spike number (MEN), main spike length (MSL), root and shoot dry weight (RSWD), and thousand grain weight (TGW) have been identified under drought conditions. These results indicated that the selected lines could play an important role in exploring the drought-tolerance genes of hulless barley. Hulless barley has great plasticity in adapting to drought stress, which will provide reference for the breeding process of superior hulless barley strains and improve the drought tolerance of hulless barley.EMB506, DCR, and APD2) were identified. ABCG11 and CLPR2 are two effector genes for MEN and MSL traits, respectively. Among the genes related to spike traits, DCR and ABCG11 plays a key role in cuticle formation  were identified. As for ENSP, markers distributed on chromosomes 7H and chromosomes 1H were correlated, a total of 4 SNPs loci on three genes , RSWD trait (chr1H_64014764), and TGW trait  were identified. Under drought conditions, the mutation of these SNPs loci possibly lead to phenotypic changes and improve the adaptation of hulless barley to drought environment. In conclusion, the SNPs identified in this study can be used in drought-tolerance gene analysis, and can provide valuable information for further improvement of crop yield, quality, and adaptability.PRJCA009869.All the raw genome data can be found in the National Genomics Data Center (NGDC) BioProject database with the accession number ZF and KW contributed to the conceptualization and methodology. JL contributed to the experimentation, data analysis, and first draft preparation. XY, YY, JL, and LA contributed to the experimentation and data analysis. JL, ZF, and KW contributed to the supervision and manuscript editing. All authors have read and agreed to the published version of the manuscript.This work was supported by funds from the National Key R & D Program of China (2018YFD1000705 and 2018YFD1000700), National Natural science Foundation of China (32060480), the China Agriculture Research System (CARS-05), the International Science and Technology Cooperation in Sichuan Province of China (Grant No. 2021YFH0113), Qinghai Provincial Academy of Agriculture and Forestry Innovation Fund (2018-NKY-12), and the Double Support Program for Discipline Construction of Sichuan Agricultural University in China.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. 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