FOS on SSC7 should be further studied as a critical candidate gene. In addition, BMPR1A was identified as a novel candidate gene affecting the NTV in pigs.The number of thoracic vertebrae (NTV) and number of vertebrae (NV) varies among pig breeds with a high correlation of about 0.8. It is important to discover variants associated with the NTV by considering the effect of the NV in pig. The results suggest that regulation variants on SSC7 might play crucial roles in the NTV and the VRTN g.19034 A > C, LTBP2 c.4481A > C, and 37 missense or splice variants previously reported in a 951-kb interval on SSC7 and 147 single nucleotide polymorphisms (SNPs) on SSC14. To identify NTV-associated SNPs, we firstly performed a genome-wide association study (GWAS) using the Q + K (population structure + kinship matrix) model in TASSEL. With the NV as a covariate, the obtained data were used to identify the SNPs with the most significant genome-wide association with the NTV by performing a GWAS on a PorcineSNP60K Genotyping BeadChip. Finally, a conditional GWAS was performed by fixing this SNP. The GWAS showed that 31 SNPs on SSC7 have significant genome-wide associations with the NTV. No missense or splice variants were found to be associated with the NTV significantly. A linkage disequilibrium analysis suggested the existence of quantitative trait loci (QTL) in a 479-Kb region on SSC7, which contained a critical candidate gene FOS for the NTV in pigs. Subsequently, a conditional GWAS was performed by fixing M1GA0010658, the most significant of these SNPs. Two SNPs in BMPR1A were found to have significant genome-wide associations and a significant dominant effect. The leading SNP, S14_87859370, accounted for 3.86% of the phenotypic variance. Our study uncovered that regulation variants in FOS on SSC7 and in BMPR1A on SSC14 might play important roles in controlling the NTV, and thus these genetic factors may be harnessed for increasing the NTV in pigs.The number of vertebrae (NV), especially the number of thoracic vertebrae (NTV), varies among pig breeds. The NTV is controlled by vertebral segmentation and the number of somites during embryonic development. Although there is a high correlation between the NTV and NV, studies on a fixed NV have mainly considered the absolute numbers of thoracic vertebrae instead of vertebral segmentation. Therefore, this study aimed to discover variants associated with the NTV by considering the effect of the NV in pigs. The NTV and NV of 542 F2 individuals from a Large White × Minzhu pig crossbreed were recorded. All animals were genotyped for  The mammalian spine is composed of the following five types of morphologically different vertebrae: cervical, thoracic, lumbar, sacral, and caudal. In pigs, the number of cervical vertebrae is fixed at seven, as in almost all mammals . Both thSus scrofa Chromosomes (SSCs) 1, 2, 4, 7, 11, 16, and 18 [Hox genes [The NTV in pigs is a highly heritable trait (heritability from 0.24 to 0.78) and shows considerable variation across breeds ,5. Many , and 18 ,13,14. G, and 18 ,16,17. Iox genes . These fox genes  as in otox genes ,13,14, oox genes  seems inAll animals were sacrificed by electric shock in Beijing Fifth Meat Processing Factory according to the guidelines for experimental animals established by the Council of China. The experiments involving animals were approved by the Science Research Department of the Institute of Animal Science, Chinese Academy of Agricultural Sciences (CAAS)  (No. IASCAAS-PG-39).y is the NTV or NV of the animal, µ is the overall mean, a is the random additive genetic effect of the animal, and e is the random residual effect.The phenotypic data included the NTV and NV of 542 individuals from the F2 progeny of 4 Large White pigs crossed with 16 Minzhu pigs. The F1 generation consisted of 9 boars and 46 sows which were mated to produce 542 F2 animals (65 L) in three parities. All these F2 individuals were slaughtered at 240 ± 7 d of age, and the phenotypes obtained from the carcasses were recorded. Genetic parameters pertaining to the NTV and NV were estimated using DMU software with pedigree-based linear models that are based on the following equation :y = µ + VRTN) g.19034 A>C, g.20311_20312ins291 [LTBP2) c.4481A > C [Sus scrofa genetic variant database in Ensembl (http://asia.ensembl.org/Sus_scrofa/Location/Variant), 37 missense or splice variants in a previously reported 951-kb interval on SSC7 [http://primer3.ut.ee/)  under the following conditions: 94 °C for 5 min; 5 cycles at 60 °C for 20 s and 72 °C for 45 s; 30 cycles at 52 °C for 20 s and 72 °C for 45 s; 72 °C for 10 min. The PCR products were resolved using 1.5% agarose-gel electrophoresis  and photographed under UV light. They were then purified and sequenced on an ABI 377 sequencer .All the animals were genotyped for “Vertebrae Development Associated” (12ins291  and “Lat481A > C . Through on SSC7  were obtee/) see . Genomicp = 1.10 × 10−6). The phenotypic variation explained by each SNP is calculated based on a formula for R2 using Tassel software as shown here .Population structure and cryptic relationships were considered to minimize false positives and increase statistical power. The kinship derived from the whole-genome SNPs was set as a random effect to control for family effects. The GWAS for the NTV was performed using the TASSEL 5.0  software  with thehttp://asia.ensembl.org). The nearest gene to the SNP was considered as a candidate gene for NTV.Since SNP M1GA0010658 showed the maximum association with the NTV, a conditional GWAS was re-run with this SNP fixed to detect novel associated variants. We calculated the distance between each significant genome-wide SNP and the annotated genes upstream and downstream of the SNP by using the Ensembl database  pigs from VICA group Co., Ltd. , and 129 Songliao Black (SLB) pigs from the Jilin Academy of Agricultural Sciences were used in this study. The NTV was recorded after slaughter. Genotypic effects were analyzed using a one-way ANOVA through the GLM procedure of the SAS software package version 8.2  with genotype as a fixed effect. Duncan’s multiple-range tests were used for assessing the significance of the differences among the means of different genotypes.Linkage disequilibrium analysis was performed on the chromosomal region which contained all the SNPs significantly associated with the NTV on SSC7 and SSC14, respectively. The genotypes of all SNPs for 542 individuals and pedigrees were used to detect haplotype blocks. The visualized haplotype blocks were detected using the HAPLOVIEW V3.31 program  with defThe NTV and NV phenotypes observed are summarized in PGF), and these variations could explain 7.80% of the phenotypic variance. All 31 SNPs showed significant additive effects with no dominant effect. This region contained a previously reported 951-kb interval, which comprised the reported candidates VRTN and LTBP2 for the NTV. In addition, 37 non-synonymous variants were detected during the whole-genome sequencing, and each animal was genotyped for these variants. For VRTN g.20311_20312ins291, no polymorphism was detected in this population. To evaluate the effects of these variants, LTBP2 c.4481 A > C, and VRTN g.19034 A > C, we identified them through a GWAS. The results suggested that the effects of these variants and two genes on the NTV were not significantly associated with the trait at the genome-wide level (CA10) on SSC12 and BMPR1A on SSC14.Using the merged SNP data obtained from the 542 F2 individuals, a GWAS revealed a strong association peak (QTL) on SSC7 . The Q-Qde level . Howeverde level . The canRPS6KL1), eukaryotic translation initiation factor 2B subunit beta (EIF2B2), mutL homolog 3 (MLH3), acylphosphatase 1 (ACYP1), NIMA related kinase 9 (NEK9), zinc finger C2HC-type containing 1C (ZC2HC1C), transmembrane p24 trafficking protein 10 (TMED10), Fos proto-oncogene, AP-1 transcription factor subunit (FOS), and Jun dimerization protein 2 (JDP2).Within the 25.76-Mb region on SSC7, the linkage disequilibrium analysis detected four haplotype blocks as ALGA0043941-ALGA0043942 for 19 kb, ALGA0043941-ALGA0043962 for 451 kb, DIAS0001088-ALGA0122954 for 479 kb, and MARC0073299-DRGA0008061 for 469 kb . Since tA conditional GWAS was performed by fixing the strongest significant SNP, M1GA0010658, on SSC7. Only the SNPs S14_87859370 and S14_87859377, located on SSC14, were determined to be significantly associated with the NTV A,B. The p < 0.05)  D. Althou < 0.05) E. Nevert < 0.05) E.VRTN, LTBP2, and “Visual System Homeobox 2” (VSX2) [VRTN g.19034 A > C [LTBP2 c.4481A > C [In this study, we did not observe any animals with an NTV of 14 or 16 in the populations with an NV of 22 or 20, respectively. The heritability of the NTV and NV was similar to that in previous reports . The hig” (VSX2) ,24,25,2634 A > C  and LTBP481A > C  have beeFOS, also known as AP-1, is an important member of the Notch signaling pathway [FOS could inhibit the expression of Notch-1 [fos −/− mice displaying growth-retarded, develop osteopetrosis with deficiencies in bone remodeling and tooth eruption [FOS was also regarded as a critical candidate gene for the porcine NTV in this work.On SSC7, a 479-Kb region including nine annotated genes was identified as a critical fragment influencing the NTV. Among these nine genes,  pathway  and coul Notch-1 , which i Notch-1 . That hoeruption  suggesteeruption , FOS wasBMPR1A, which was selected as a good candidate due to its essential role in BMP (Bone morphogenetic protein) signaling [BMPR1A, also known as ALK-3 and Brk-1, encodes a type I “Transforming Growth Factor-β” (TGF-β) family receptor for the BMP-2 and BMP-4 signaling pathways [Hox genes are key regulators of morphogenesis along the axial skeleton [Hox gene expression as a mechanism for generating a combinatorial code that specifies the unique identities of the segments and their derivatives [BMPR1A die by E9.5 due to defects in mesoderm formation [BMPR1A expression may control vertebral patterning via classical BMP-Smad-Hox signaling. These results indicate that S14_87859370, located in BMPR1A, is a potential binding site for a transcriptional regulator of the BMP pathway, and this observation may have important implications for further studies on the regulation of the NTV in pigs.Surprisingly, when the effect of SSC7 was fixed, two variants on SSC14 showed significant genome-wide associations with the NTV. In addition, different from the SNPs on SSC7, we detected dominant effects at the two SNPs. To our knowledge, this is the first report with a dominant effect on NTV. These results suggested that the NTV was a complex trait with additive and dominant effects. Both of them were located in ignaling . BMPR1A,pathways . In micepathways . BMPs arpathways  and are pathways . The actpathways . Followipathways ,37. Smadpathways . Hox genskeleton . Vertebrivatives . BMP sigivatives , and itsivatives . The disormation  has led FOS on SSC7 should be further studied as a critical candidate gene. On SSC14, to our knowledge, this is the first report showing that a natural variant in BMPR1A significantly contributes to the NTV in pigs. The identified variations provide valuable molecular information that may be harnessed to increase NTV in pigs.Our results suggest that regulation variants on SSC7 might play crucial roles affecting NTV. The


Microscopic planktonic algae are the base of the marine food web. Although sugars are the most abundant biomolecules in land plants, their concentrations in marine plants appear surprisingly low. We used recently discovered enzymes to dissect microalgae inhabiting the sunlit ocean and found that 26 ± 17% of their biomass consists of the sugar polymer laminarin. The concentration in algal cells increased markedly during the day, in analogy to the seasonal storage of energy in starchy roots and fruits of land plants. Vast quantities of laminarin discovered in the ocean underscore the importance of marine sugars in the global carbon cycle. This work has implications for our understanding of the elemental stoichiometry of microalgae, the most important oceanic food source. 2 into carbohydrates as terrestrial plants. Polymeric carbohydrates  provide carbon for heterotrophic organisms and constitute a carbon sink in the global oceans. The quantitative contributions of different algal glycans to cycling and sequestration of carbon remain unknown, partly because of the analytical challenge to quantify glycans in complex biological matrices. Here, we quantified a glycan structural type using a recently developed biocatalytic strategy, which involves laminarinase enzymes that specifically cleave the algal glycan laminarin into readily analyzable fragments. We measured laminarin along transects in the Arctic, Atlantic, and Pacific oceans and during three time series in the North Sea. These data revealed a median of 26 ± 17% laminarin within the particulate organic carbon pool. The observed correlation between chlorophyll and laminarin suggests an annual production of algal laminarin of 12 ± 8 gigatons: that is, approximately three times the annual atmospheric carbon dioxide increase by fossil fuel burning. Moreover, our data revealed that laminarin accounted for up to 50% of organic carbon in sinking diatom-containing particles, thus substantially contributing to carbon export from surface waters. Spatially and temporally variable laminarin concentrations in the sunlit ocean are driven by light availability. Collectively, these observations highlight the prominent ecological role and biogeochemical function of laminarin in oceanic carbon export and energy flow to higher trophic levels.Marine microalgae sequester as much CO The production rate of organic carbon is controlled by the growth of photosynthetic microalgae in the sunlit ocean, where diatoms alone contribute about 40% of the marine primary production and convert equal amounts of carbon dioxide into biomass as tropical forests . GlycansBulk measurements by NMR and acid hydrolysis suggest algal glycans are crucial for carbon export and sequestration because they can aggregate into sinking particulate organic matter (POM) , 4. Glyc7SI Appendix, Fig. S1). A widely used method for the quantification of glycans in marine organic matter uses acid hydrolysis, followed by colorimetric or liquid chromatographic determination of the constituent monosaccharides  . These side chains are cleaved off by enzymes that are exo-acting β-1,6-glucosidases (GH30). A β-(1→3)–specific exo-glucosidase of family GH3 can further cleave the remaining oligosaccharides into glucose. The reaction products  are readily measurable and proportional to the amount of laminarin in the sample  –18. Lamie sample , 18.Ostreococcus tauri) whereas diatoms contribute mainly to the biomass in the >3-µm fraction  and >10-µm fraction . Submicrometer particles are mostly derived from nonliving organic matter (Dataset S1). Only in the two Arctic and the North Atlantic datasets, samples were obtained from water depths of 350 m below the surface. The data for each of these cruises and the time series are presented in SI Appendix, Table S1 and Dataset S2. The laminarin quantification method was based on specific enzyme hydrolysis and previously developed and validated . A detailed description of these experiments and their results is found in the supplementary information .We measured laminarin concentrations extracted from POM collected during six cruises to the Arctic in 2016 and 2017, North Atlantic, Peru upwelling, Canary upwelling, meridional Atlantic transect, in the Raunefjorden near Bergen, and during two time series in the North Sea near the island Helgoland, resulting in a total of over 250 samples from 51 stations . In shorc matter . Samplinalidated . In the −1; overall mean 0.77 ± 0.66 mg⋅L−1), with laminarin present in microalgae size fractions between 3 and 10 µm, but not detectable in smaller size fractions. The laminarin increase during the 2017 bloom was driven by microalgae larger than 10 µm  . This result was supported by multiwavelength spectrofluorometric analysis . Unlike diatoms, chlorophytes, which are known to primarily produce the polysaccharide starch . Quantification of starch with an enzymatic assay based on amylase (EC 3.2.1.1) revealed that its concentrations were up to 10 times lower than those of laminarin, underscoring the importance of laminarin as a major glycan in the ocean .We monitored laminarin in the North Sea by sampling during spring in 2016 and 2017 . This tie starch , stayed SI Appendix, Figs. S10 and S11) and found a linear correlation  , as well as samples from a meridional Atlantic transect (−1 in the two oligotrophic North and South Atlantic gyre regions and ∼1.4 µg⋅L−1 in temperate regions (−1 on 3-µm-size filters). Similar to the North Sea, the size fraction over 10 µm, which contains larger cells such as diatoms, contained more laminarin than the 3-µm-size fraction.To further develop our understanding of the relationship between algal biomass and laminarin, we used combined chlorophyll A (Chl-a) as growth proxy  for algan = 101) C. We anatransect B. Chl-a  regions B. Betwee regions –24, the R2 = 0.81; P < 0.001, n = 88) . However, LamC:POC ratios and the slopes of the linear correlations observed in the various settings  reveal variable laminarin concentrations relative to POC and, hence, that the bioenergy available to higher trophic species is temporally and regionally variable. Intriguingly, the datasets from the North Sea and Peru upwelling zone, both of which had among the highest concentrations of POC and laminarin, were characterized by the lowest LamC:POC ratios of 21 ± 6% (SD) and significantly deviated from the overall LamC:POC median of 37 ± 17% (interquartile range [IQR])  . Our data suggest a number of reasons, including nutrient concentrations and time of day when samples were taken as important controllers of the LamC:POC ratio.Laminarin-carbon (LamC) and particulate organic carbon (POC) are linearly correlated ( n = 88) B. In the n = 88) . The ArcThe variation of LamC:POC may be explained by light availability. Light-driven increase of intracellular laminarin during the day and decrease during the night has been previously observed in laboratory cultures of diatoms –29. To e−1) and the end of the day (7.7 µmol⋅L−1) (−1⋅h−1 (SD)  and afterward consumed at a rate of ∼0.35 mg⋅L−1⋅h−1. This diel variation of the LamC:POC ratio  B. Notablmol⋅L−1) B and carmol⋅L−1) C could bOC ratio A indicatOC ratio B. In conWe further verified the quantitative contribution of laminarin to POC by mass balance of organic carbon in lipids, proteins, and laminarin. On a subset of 15 samples from the cruise to the Arctic in 2016, we determined protein and lipid masses and converted them into carbon equivalents . In addiSI Appendix, Fig. S14). The elemental composition of particulate organic matter is constrained by the content of the major macromolecules glycans, proteins, lipids, and DNA leading to a mean Redfield ratio of 6.6 (lam), with 3.82 being the theoretical average C/N value of protein . Assuming steady-state dynamics and using the median LamC:POC ratio of 26 ± 17% and the correlation of laminarin with Chl-a , suggesting that laminarin is part of sinking POM and thus plays an important role for carbon export to the deep ocean and the seafloor, respectively. In the Arctic, we found the highest laminarin concentrations at the Chl-a maximum between 16 and 35 m (0.46 ± 0.34 mg⋅L−1 [SD]) , confirming that photosynthetic microalgae are the source of laminarin  obtained in both years was 50 ± 18% (SD), which did not significantly change with depth  , suggesting the LamC:POC ratio of the surface ocean influences deeper waters. We measured laminarin in sinking particles in the North Atlantic  with a marine snow catcher (−1 in the suspended fraction and ∼0.5 mg⋅L−1 in the settling particles) . The laminarin is exported in intact diatom cells sinking on their own or incorporated into marine snow particles like those shown in SI Appendix, Fig. S16. Its presence in sinking, diatom-containing particles (48 ± 7% [SD] LamC:POC) suggests that laminarin contributes significantly to the carbon and energy flow to the deeper ocean.The data obtained in the Arctic and in the North Atlantic Ocean revealed laminarin in POM below the photic zone  throughout the Atlantic   –47. All able S2) .Abundance of laminarin-degrading bacteria in the North Sea , 37, as 2 and light are abundant, and this continuing photosynthesis of polysaccharides may lead to elevated C/N ratios toward the end of algal blooms   indicate a fast enzymatic degradation of laminarin and underscore the relevance of this molecule in the marine carbon cycle, especially in productive regions rich in diatoms and other laminarin-containing algae, such as Phaeocystis spp. Our observations also suggest that laminarin is temporarily preserved from degradation in POM, providing a pathway for laminarin export in sinking diatom cells that are part of marine snow. How the temporally variable LamC:POC ratio in phytoplankton POM relates to carbon export and regulates marine ecosystem properties, such as growth, behavior, and composition of higher trophic species, are important questions, which can now be addressed with recently introduced enzymatic assays that enable identification and quantification of complex glycans.Laminarin is enzymatically readily hydrolyzed to glucose and subsequently converted into metabolic energy. The high abundance of laminarin in phytoplankton POM reported in this study agrees with acid hydrolysis experiments demonstrating that up to 90% of the monosaccharides indeed consist of glucose . Yet, thL−1⋅h−1)  and assuSI Appendix, Table S1). The filtered volume was logged for every sample. Prior to sampling, the glass fiber filters were combusted for 4 h at 450 °C to remove carbon contamination. After sampling, the filters were wrapped in precombusted aluminum foil and kept frozen at −20 °C until further processing. Blank samples were taken by filtering 10 L of MilliQ-H2O. The North Sea was sampled during two campaigns in spring 2016 (from March to June) and 2017 (from January to May) at the long-term ecological research site (LTER) “Helgoland Roads” at the island of Helgoland in the German Bight  on a 1-kDa membrane resulted in a 0.5-L volume, which was frozen at −20 °C for later processing. The sample was then thawed and further concentrated to a volume of ∼25 mL using an Amicon ultrafiltration stirred cell (Merck-Millipore) with a 1-kDa membrane. Afterward, it was desalted by dialysis using a 1-kDa membrane (Spectra/Por). The retentate material was then frozen and freeze-dried (Labogene). This procedure yielded several milligrams of lyophilized material, which could be again dissolved for further analyses.p‐hydroxybenzoic acid hydrazide (PAHBAH) reducing sugar assay, were performed according to Becker et al. . The sample extract from Helgoland at April 27, 2017 was first hydrolyzed with 100 nM FbGH30 for 25 min at 37 °C, and then FaGH17A was applied in the same manner. Between each digestion, the reaction was stopped by boiling the sample for 5 min at 100 °C; precipitated protein was removed by filtration through 0.2-µm centrifuge filters , and an aliquot was taken.Samples were applied on an ICS-5000+ (Dionex) with electrochemical detection on a gold working electrode and a pH reference electrode (Ag/AgCl) according to Unfried et al. . SeparatFor all but the Mid-Atlantic samples, glass fiber filters were used for sampling. The same glass fiber filter (GF/D or GF/F) was used for both the laminarin and particulate organic carbon and nitrogen measurements. After punching out defined pieces of the filter in triplicate, the pieces were subjected to an acidic atmosphere with concentrated HCl for 24 h in a desiccator to remove inorganic carbon. They were then dried for 24 h at 60 °C and afterward packed in combusted tin foil. The carbon and nitrogen quantification was performed by an elemental analyzer  using sulfanilamide for calibration. For the Mid-Atlantic samples, which were filtered on polycarbonate filters, this procedure could not be applied.2O) or deuterated dimethyl sulfoxide (DMSO). NMR spectra were obtained on a Bruker AVANCE 600 spectrometer  and recorded at 600.2 MHz for 1H and 150.9 for 13C nuclei, using a 5-mm PABBI or CPTCI probe head and standard Bruker pulse programs. Trimethylsilylpropanoic acid was used as internal standard for quantification. Signals were assigned as described by Kim et al.  were dissolved in this solvent for laminarin quantification. Quantification was achieved via integration of the well-resolved H-6 proton. Structural characterization and shift comparison with literature values was done of the sample in deuterated DMSO. 13C chemical shifts were calibrated using the methyl resonances of DMSO at 39.5 ppm.Freeze-dried POC filter residues (5 to 250 mg) were dissolved in 500 µL of deuterated water , which is based on Peterson’s modification of the original Lowry assay . The mean relative error was determined to be 3.8%. A blank filter was extracted following the same procedure to check for potential nonlipid contamination. Total protein and lipid concentrations were converted to carbon equivalents, assuming conversion factors of 0.50 and 0.75, respectively  (https://www.bodc.ac.uk/).The Helgoland Roads LTER time series provides Chl-a and temperature data on a weekday basis . Multiwagaea.de) . The stuExtracellular enzymatic activities (EEAs) of polymeric substrates, especially via endo-active enzymes, yield an estimate on the potential hydrolysis rates of laminarin and other polysaccharides by a bacterial community. This technique was developed by Arnosti and colleagues and makes use of fluorescently labeled polysaccharides , 78. Dur−3⋅d−1, that was found by Behrenfeld and Falkowski . Linear models were fitted to the investigated variables. Significant differences in the nonnormal distributions of values among groups were tested by the Kruskal–Wallis test. P values below 0.05 were considered statistically significant. The confidence interval for the median of nonnormal data was calculated via bootstrapping with 1,000 replications .Environmental data used in this manuscript will be made accessible via the public database Pangaea (Supplementary FileSupplementary FileSupplementary File


Vitis species. We report an improved, chromosome-scale Cabernet Sauvignon genome sequence and the phased assembly of nine wild and cultivated grape genomes. By resolving twenty Vitis SDR haplotypes, we compare male, female, and hermaphrodite haplotype structures and identify sex-linked regions. Coupled with gene expression data, we identify a candidate male-sterility mutation in the VviINP1 gene and potential female-sterility function associated with the transcription factor VviYABBY3. Our data suggest that dioecy has been lost during domestication through a rare recombination event between male and female haplotypes. This work significantly advances the understanding of the genetic basis of sex determination in Vitis and provides the information necessary to rapidly identify sex types in grape breeding programs.It remains a major challenge to identify the genes and mutations that lead to plant sexual differentiation. Here, we study the structure and evolution of the sex-determining region (SDR) in  Grapevine is one of a few ancestrally dioecious crops that are reverted to hermaphroditism during domestication. Here, the authors identify candidate genes related to male- and female-sterility in grapes and describe the genetic process that led to hermaphroditism during domestication. Despite its rarity, dioecy is widespread phylogenetically, suggesting it has evolved independently on multiple occasions.Plant species possess a variety of mating systems. Some are monoecious, with separate male and female flowers on the same plant, and others are hermaphroditic, with bisexual flowers. Occasionally plants have separate male and female individuals, a mating system called dioecy. Dioecy ensures outcrossing, but it occurs in only 5–6% of angiosperms4. The first step includes a recessive mutation that interrupts male function. Individuals with a homozygous male-sterility mutation retain only female function, and a population with this mutation contains both females and hermaphrodites (gynodioecy). The second step requires a dominant mutation that suppresses female function, leading to males. This two-locus system can maintain separate sexes only if the two loci are completely linked, because recombination between them could restore hermaphrodites5. Support for the two-locus model has been found in several species, including papaya6 (Carica papaya), strawberry7 (Fragaria virginiana), Silene latifolia8, Actinidia spp.9 and grapes10 (Vitis spp.). However, the sterility mutations that cause dioecy have not been identified completely in any species11. Thus far, the best candidates are from asparagus and kiwifruit13. In asparagus, for example, females lack a gene associated with tapetal development13 and mutant males without a putative female-suppressor gene revert to hermaphrodites12.Dioecy has been the focus of numerous evolutionary and genetic studies, both because of its multiple evolutionary origins and because several economically important crops are dioecious. A common hypothesis about the origin of dioecy is the two-locus model, which requires that dioecy evolved from an hermaphroditic ancestor in two stepsVitis. All ~70 wild Vitis species are dioecious14, suggesting that dioecy has been conserved since the origin of the genus. One Vitis species, the cultivated grapevine , has reverted to hermaphroditism, even though its wild ancestor Vitis vinifera ssp. sylvestris (hereafter Vv sylvestris) is dioecious. This shift of mating system occurred during domestication ~8000 years ago15, perhaps following a rare recombination event between male (M) and female (F) haplotypes11. Therefore, Vitis spp. have individuals of three types  hermaphrodites within Vv vinifera, which have perfect flowers with functional pistils and stamens that bear fertile pollen. The three types are determined by the genotype at the SDR. Males are heterozygous for male and female haplotypes (MF), females are homozygous (FF), and cultivated Vv vinifera hermaphrodites are either homozygous for hermaphrodite haplotypes (HH) or heterozygous (HF).Here we study the sex-determining region (SDR) within the genus pes Fig. : (i) mal18. In Vitis spp., the SDR maps genetically to ~150 kbp of chromosome 2 that contains between 15 and 20 genes18. Polymorphisms within the region have high linkage disequilibrium in Vv sylvestris, suggesting low or no recombination between M and F haplotypes10. It has been hypothesized that this region contains the recessive male-sterility and dominant female-sterility alleles predicted by the two-locus model, and their identification has been attempted by comparative gene expression analyses19. One candidate gene, the adenine phosphoribosyltransferase gene VviAPT3, is expressed in the carpel primordial of male plants, suggesting a role in pistil abortion20.Previous genetic and genomic studies have identified the approximate boundaries of the SDR in grapesVitis sex determination has been that the Vv vinifera reference genome represented only a partially assembled F haplotype21. More recent work has resolved the partial sequence of four SDR haplotypes, including three H and one F haplotypes22. Yet, despite substantial progress, our understanding of the SDR and the potential determinants of sex have been hampered by the absence of information from M haplotypes.Until recently, a major limitation in the study of To fill this gap, we report nine phased diploid genomes of cultivated hermaphrodites, and wild male and female grapes. All of these genomes are based on high-coverage, Pacific Biosciences (PacBio) long-read sequencing. For each genome, haplotypes within the genetically defined SDR have been curated manually, and transcripts expressed from the region have been measured during early and late stages of flower development in male, female, and hermaphrodite plants. With these extensive sequence and expression data, we compare the F, H, and M haplotypes to better define the SDR, identify candidate sex-determining genes, and assess whether H haplotypes owe their origin to a recombination event.Vitis accessions, including three hermaphrodite Vv vinifera cultivars , four Vv sylvestris accessions , and one male V. arizonica. In addition, the genome of one male Muscadinia rotundifolia was sequenced as a dioecious outgroup to Vitis spp.23  based on known genotypes  and the M and F haplotypes of M. rotundifolia  and H  haplotypes. These length differences reflect the presence of sex-linked structural variants . For example, the 12 Vitis F haplotypes shared eight large deletions relative to the H haplotype of Cabernet Sauvignon, encompassing a total length of 117.4 kbp  gene was absent from H and M haplotypes relative to F haplotypes. The overall structure and gene content were well-conserved between Vitis and M. rotundifolia species. Despite a large inversion, gene content and order in the M haplotype of M. rotundifolia was similar to Vitis M haplotypes, and the F haplotype of M. rotundifolia was identical in gene content and order to Vitis F haplotypes , and the first F-linked SNP was ~40 kbp downstream from the dense cluster of M-linked SNPs , and one in the third FMO gene. These nonsynonymous M-linked SNPs represent potential female-sterility mutations. Similarly, we detected 89 nonsynonymous F-specific SNPs across ten genes , seven in a PLATZ TF-coding gene (PLATZ), 18 in the first FMO gene, 26 in the second FMO, 11 in the third FMO, 11 in the hypothetical protein VviFSEX, and four in VviAPT3. Three of these SNPs introduce a premature stop codon in the first two of four FMO genes . Alleles tended to cluster by sex type across most of the SDR with the pattern of clustering varying along the locus  clustered most M sequences apart from F and H sequence, with the VviYABBY3 and aldolase alleles forming clades that separated M from F and H orthologs , which caused a frameshift and premature stop codon in all F alleles . Because the same 8 bp deletion was also found in the F haplotype of M. rotundifolia  shared by all F and M haplotypes, respectively, relative to the Cabernet Sauvignon H reference. Only two of these INDELs were within exons  between all 52 pairs of F and M alleles of INP1 to be 0.0275 substitutions per base . Assuming a generation time of 3 years and a nucleotide substitution rate of 2.5 × 10−9 substitutions per base per year28, we infer that M and F alleles diverged ~16.5 million years ago , a value within the range of uncertainty of the estimated split time between Vitis and Muscadinia29.The sex-specificity of VviINP1 INDEL further, PCR primers were designed to amplify INDEL-specific alleles of the gene. Seven additional Vv vinifera (HF and HH genotypes) accessions and two more distant central Asian Vitis species, V. piasezkii  and V. romanetii , were analyzed. The presumably functional VviINP1 allele was present only in genotypes that contained H and M haplotypes, and the INDEL was present only in genotypes containing an F haplotype . In both populations, all F1 individuals with female flowers were homozygous for the presumably nonfunctional VviINP1 allele. All F1 individuals with male flowers carried one functional and one nonfunctional copy of VviINP1  across 50 Vv vinifera (HF) accessions  Vitis females and could be responsible for the absence of colpi in Vv sylvestris female pollen grains31. Together, the sequence, phylogenetic, association, and functional evidences suggest that a recessive allele of VviINP1 containing an 8 bp deletion interrupts male function, making VviINP1 a plausible male-sterility candidate.Because a functional copy of VviYABBY3 were associated with flowering and flower development, including SHORT VEGETATIVE PHASE (SVP), which is involved in the control of flowering time by temperature32, and BES1-INTERACTING MYC-LIKE1 (BIM1), a brassinosteroid-signaling component involved in A. thaliana male fertility33. Similarly, we identified TF-binding motifs unique to F haplotypes upstream of VviINP1, exostosin, KASIII, PLATZ, FMOs, VviFSEX, WRKY, and VviAPT3  and male and female Vv sylvestris DVIT3351.27 (MF), and O34-16 (FF). Previous studies have analyzed gene expression in the SDR and have compared flowers of different sexes19. However, these data were sampled from flowers at early developmental stages and may have missed the late steps of sex determination. Accordingly, we sampled flowers at three developmental stages: (i) during the early development of the reproductive structures, (ii) pre-bloom during pollen maturation, and (iii) at anthesis. Sequencing reads were mapped to both haplotypes of the phased Cabernet Sauvignon genome and expression was compared between individuals at each developmental stage. We focused on genes that showed sex-specific gene expression profiles—e.g., genes that were more highly (or lowly) expressed in the female plant compared to the male plant and to the hermaphrodite.To examine sex-specific regulation, we quantified transcript abundance by RNA-sequencing (RNA-seq) of flower buds from hermaphrodite VviINP1 was significantly more highly expressed in pre- and post-bloom female flowers compared to male and hermaphroditic flowers . Similarly, WRKY was more highly expressed in male and hermaphroditic flowers than in female flowers at all developmental stages. Three genes were differentially expressed at only one stage: TPP, aldolase and beta-fructofuranosidase (BFRUCT)  Fig. . Two genked Fig. , and thaked Fig. , it is r35. To assess the relationships between sex-linked genes and other genes throughout the genome that may affect development, we performed a Weighted Gene Co-expression Network Analysis across developmental stages (WGCNA36) . This module included two genes in the SDR encoding a PPR-containing protein and VviAPT3. The module also included genes involved in hormonal signaling, like two uridine diphosphate glycosyltransferases (UGTs) that are orthologous to AtUGT85A1 and AtUGT85A3 and could be involved in active cytokinin homeostasis37. WRKY expression was central (i.e. highly connected) in the red module . This module included an ortholog of A. thaliana SEPALLATA1 , an MADS-box gene necessary for floral organ development38. Altogether, these data suggest that sex-linked polymorphisms affect the regulation of some SDR genes and also that some of these genes are highly connected within coexpression modules that participate in sex determination and other developmental processes.The differentially expressed SDR genes included TFs and genes associated with hormone signaling that may play significant regulatory roles in coexpression networksVitis is interesting because grapevine is one of a few ancestrally dioecious crops that reverted to hermaphroditism during domestication11. Structurally, wild Vitis spp. have perfect flowers  implicates these genes in leaf and carpel development42.By mapping the PT3 Fig. . Under tPT3 Fig. . Notablypes Fig. . In addient Fig. . We therTPP to VviAPT3 , one dioecious male V. arizonica (b40-14), and one dioecious male M. rotundifolia (Trayshed). For RNA-seq, inflorescences were collected in April and May 2019 from vines at the University of California Davis . These times correspond to two developmental stages49: flowers pressed together (E-L 15) and full flowering with 50% caps off . At E-L 23, pre- (time 2) and post-bloom (time 3) flowers were collected separately, each represented by three biological replicates. Young leaves from additional genotypes were collected for a marker assay. This included seven Vv vinifera, V. piasezkii and V. romanetii and two F1 populations (Supplementary Table 1 population was the result of a cross between the pistillate Vv vinifera F2-35 (Carignane × Cabernet Sauvignon) and V. arizonica b42-26. The other F1 population was produced by crossing female Vv vinifera 08326-61 (selfing of Cabernet Franc) and Vv sylvestris DVIT3351.27. The sexes of F1 individuals were evaluated by flower morphology and the presence of fruit.Different plant tissues were collected from several genotypes for genome sequencing, RNA-seq and marker assay. All plant material was immediately frozen and ground to powder in liquid nitrogen after collection. For genome sequencing, young leaves were collected from three hermaphrodite 24 from each of the nine previously described samples. DNA purity, quantity, and integrity were evaluated with a Nanodrop 2000 spectrophotometer , Qubit 2.0 Fluorometer together with the DNA High Sensitivity kit , and by pulsed-field gel electrophoresis, respectively. SMRTbell libraries were prepared as described in Minio et al.50 and sequenced on a PacBio Sequel system using V3 chemistry, and on a PacBio RS II  using P6-C4 chemistry for Vv vinifera cv. Merlot . Summary statistics of SMRT sequencing are provided in Supplementary Data 1 populations were performed using a modified CTAB protocol17. DNA concentration was measured using Qubit 2.0 Fluorometer. For the bulk segregant analysis, 120 DNA samples from the F1 population Vv vinifera F2-35 × V. arizonica b42-26, 60 females and 60 males, were selected to constitute four pools of 30 individuals each, two made of DNA from male individuals and two made of DNA from female individuals. A total of 1 μg DNA per sample was used as the input material. DNA-seq libraries were prepared using the Kapa LTP library prep kit . Libraries were evaluated for quantity and quality with the High Sensitivity chip on a Bioanalyzer 2100  and sequenced in paired-end 150 bp reads on an Illumina HiSeq4000. Total RNA were extracted from floral buds as described in Rapicavoli et al.51 from each of the three genotypes described above. cDNA libraries were prepared using the Illumina TruSeq RNA sample preparation kit v.2  and sequenced in single-end 100 bp reads on an Illumina HiSeq4000 (Supplementary Data High molecular weight genomic DNA (gDNA) was isolated as in Chin et al.Vv vinifera cv. Merlot was performed at DNAnexus   to improve the contiguity of the primary assembly , BLAT v.36x257 was used to determine the overlap between sequences and contigs were manually joined. Junction gaps of ten bases were added between overlaps. A schematic representation of the haplotypes was made using the Gviz Bioconductor package v.1.20.058.The grape SDR was identified by aligning the SSR marker VVIB2359 with a maximum of 3 kbp upstream regions from the gene transcriptional start sites. TF-binding sites were identified using the R package TFBSTools v.1.22.060 and the JASPAR database (R package JASPAR2018 v.1.1.161).For each haplotype, promoter sequences were extracted for all the genes of the SDR using the R package GenomicFeatures v.1.36.4vinifera accessions from previous studies28 (mean depth = 21.6×) were trimmed using Trimmomatic v.0.3662 to remove adaptor sequences and bases for which average quality per base dropped below 20 in 4 bp window. Trimmed paired-end reads were aligned onto Cabernet Sauvignon primary pseudomolecules (hap1) using Minimap2 v.2.1763. Alignments with a mapping quality <10 were removed using Samtools v.1.964. PCR duplicates introduced during library preparation were filtered in MarkDuplicates in the picard-tools v.1.119 (https://github.com/broadinstitute/picard). INDEL realignment was performed using RealignerTargetCreator and IndelRealigner (GATK v.3.565). Sequence variants were called using HaplotypeCaller (GATK v.4.1.2.065). SNPs were filtered using VariationFiltration (GATK v.4.1.2.065), according to the following criteria: variant quality (QD) > 2.0, quality score (QUAL) > 40.0, mapping quality (MQ) > 30.0, and <80% missing genotypes across all samples. Linkage disequilibrium measured as the correlation coefficient of the frequencies (r2) were calculated using Plink v.2.066 for pairwise SNPs with a minor allele frequency (MAF) > 0.05 across the whole SDR (chr2:4750000..5100000).Illumina whole-genome resequencing data from 50 67 and the --mum option using Cabernet Sauvignon hap1 as a reference. Structural variants  including deletions, insertions, duplications, inversions, translocations, and complex insertion-deletions (CIDs) and short INDELs (<50 bp) were called using show-diff and show-snps, respectively. SNPs were called using show-snps from MUMmer v.4.0.0 and the -1 filter. Comparison between haplotypes for SVs and SNPs was performed using multiinter from BEDTools v.2.19.168. Polymorphisms were considered as fully sex-linked only if they were strictly fixed in one sex haplotype compared to the other two sex haplotypes. SNPs and INDELs were confirmed by manually inspecting the alignments of each genotype whole-genome short- and long-read sequences onto their corresponding genomes. Alignments were visualized using Integrative Genomics Viewer (IGV) v.2.4.14 and phasing the two haplotypes69.Pairwise alignments of all the haplotypes were performed using NUCmer from MUMmer v.4.0.070 using the Neighbor-Joining method71 and 1000 replicates. Evolutionary distances were computed using the Poisson correction method72 and are expressed as the number of amino acids or base substitutions per site. All positions with less than 5% site coverage were eliminated. Phylogenetic analysis of the INP1 coding sequences from Vitis spp. and M. rotundifolia was performed with seven outgroups: three Brassicaceae: Matthiola incana, A. thaliana, Capsella rubella, Solanum lycopersicum (Solanaceae), Eschscholzia californica (Papaveraceae), and two Poaceae, Zea mays and Brachypodium distachyon30  was computed for each sequence pair using the yn00 program in the PAML package v.4.974. The resulting distribution of dS values was not significantly different from a normal distribution using a Shapiro test  and hence normality was assumed to construct 95% confidence intervals.All 52 possible pair of sequences were constructed between the F and M alleles of 62 and the following settings: LEADING:3 TRAILING:3 SLIDINGWINDOW:10:20 CROP:100 MINLEN:36. For the four samples with pair-ended reads, only the first mate was used and samples were randomly down-sampled to 26 million reads with seqtk v.1.0-r57-dirty (https://github.com/lh3/seqtk) before trimming. These reads were then mapped onto Cabernet Sauvignon phased genome and Vv sylvestris DVIT3351.27 genome using HISAT2 v.2.0.575 with the following settings: --end-to-end --sensitive --no-unal -q -t --nondeterministic -k 1 ). A soft-thresholding power of 12 with a scale-free model fitting index R2 > 0.92 was applied with a minimum module size of 30. The weighted network was converted into an unweighted network preserving all connections with topological overlap mapping metric (TOM) > 0.02, and imported into Cytoscape v.3.7.277.Illumina reads were trimmed using Trimmomatic v.0.36VviINP1: INP1_HM-F, 5′-CCTACTACGAAGCCCTTGACC-3′; INP1_F-F, 5′-CAGGCCTACTACGGACCTC-3′; INP1-R, 5′-CCGTGCAGCGTTCAAATTCACTG-3′. Actin-specific primers78 were used to provide a positive amplification control. The PCR conditions included a 3 min denaturation at 95 °C, followed by 35 cycles of 35 s at 95 °C, 30 s at 60 °C and 40 s at 72 °C, and a final extension for 5 min at 72 °C.Three primers  were designed to determine the presence or absence of an 8 bp deletion in 62 and the following settings: LEADING:7 TRAILING:7 SLIDINGWINDOW:10:20 MINLEN:36. Trimmed paired-end reads were aligned onto Cabernet Sauvignon hap1 genome using BWA v.0.7.17-r118879 with default parameters. PCR and optical duplicates were removed with picard-tools v.2.0.1 (http://broadinstitute.github.io/picard/). Sequence variants were called using UnifiedGenotyper from GATK v.3.5-0-g36282e465. Variants were filtered according to the following criteria: read depth (DP) > 5 and quality score (QUAL) > 40.Illumina reads from the four pools (mean depth = 172×) were trimmed using Trimmomatic v.0.36Further information on research design is available in the Supplementary InformationPeer Review FileReporting SummaryDescription of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2Supplementary Data 3Supplementary Data 4Supplementary Data 5Supplementary Data 6Supplementary Data 7Supplementary Data 8Supplementary Data 9Supplementary Data 10


Next generation sequencing (NGS) technologies produce a huge amount of biological data, which poses various issues such as requirements of high processing time and large memory. This research focuses on the detection of single nucleotide polymorphism (SNP) in genome sequences. Currently, SNPs detection algorithms face several issues, e.g., computational overhead cost, accuracy, and memory requirements. In this research, we propose a fast and scalable workflow that integrates Bowtie aligner with Hadoop based Heap SNP caller to improve the SNPs detection in genome sequences. The proposed workflow is validated through benchmark datasets obtained from publicly available web-portals, e.g., NCBI and DDBJ DRA. Extensive experiments have been performed and the results obtained are compared with Bowtie and BWA aligner in the alignment phase, while compared with GATK, FaSD, SparkGA, Halvade, and Heap in SNP calling phase. Experimental results analysis shows that the proposed workflow outperforms existing frameworks e.g., GATK, FaSD, Heap integrated with BWA and Bowtie aligners, SparkGA, and Halvade. The proposed framework achieved 22.46% more efficient F-score and 99.80% consistent accuracy on average. More, comparatively 0.21% mean higher accuracy is achieved. Moreover, SNP mining has also been performed to identify specific regions in genome sequences. All the frameworks are implemented with the default configuration of memory management. The observations show that all workflows have approximately same memory requirement. In the future, it is intended to graphically show the mined SNPs for user-friendly interaction, analyze and optimize the memory requirements as well. The knowledge base of biological data can be collected from natural life, scientific experiments, and research archives. Classical organism databases are purposeful where species-specific data are available, as it has great significance in new discoveries. The biological databases have a significant role in bioinformatics as it helps to approach a wide range of biological data along with increased varieties of organisms. Many biological research studies have been piloted and formed significant resources for genomic data. It is often declared that these data resources have not been fully explored yet . These dNext generation sequencing (NGS) technology produces a huge amount of biological data that requires powerful computational devices, large memory and specific hardware and software to manipulate a particular problem i.e., SNPs detection, microarray analysis, and phylogenetic tree construction and analysis . Hadoop The Illumina, ABI solid and 454 Life Sciences sequencing technologies have been used in the detection of genomic variation ,9,16. Thhttps://gdc.cancer.gov/resources-tcga-users/). These samples were sequenced on the Genome Analyzer II platform . All sequences were in FASTQ format (csfastq for SOLiD), mined from the NCBI database of genotypic and phenotypic (dbGap) Sequence Read Archive toolkit (SRA) [With the emergence of technology, the cost of processing has been decreased but the size of data has been exponentially increased ,20,21. Fit (SRA) . After ait (SRA) . Additioit (SRA) . Severalit (SRA)  and machit (SRA)  methods it (SRA) .High-performance computing technology is being developed to process genomic data sources and perform computational analysis of life sciences . Many re<key, value> pairs. The map phase takes input in <key, value> pairs. It produces the output in the form of a <key, value> pairs. The output key-value can be different as compared to the input key-value. The output of various map tasks is group together. The keys and associated set of values are sent to the Reduce phase. The Reduce phase operates on keys and an associated list of values. The output of Reduce is being concatenated and written on HDFS. The proposed framework for SNPs detection using the Map-Reduce paradigm is presented in This research aims to improve SNPs detection in order to enhance the accuracy rate and optimize execution time. Our proposed framework relies on the Hadoop Map-Reduce programming model  which enThe FASTA  and FASTMore, some preprocessing steps are also applied to the reference genome prior to the actual execution of Map-Reduce functions, e.g., the reference genome is divided into a preset number of non-overlapping segments. This segmentation is performed on chromosomal regions of approximately equal-sized; where, the chromosomal regions corresponds to the reduce tasks available for execution. The number of reduce tasks can be configured in advance based on the size of the reference genome. Moreover, it is also ensured that all the required data i.e., configuration files and binaries are accessible to each compute node. When all the required data are fetched to each compute node then these preprocessing phases can be ignored. Performing preprocessing on datasets to make them available on each corresponding compute node before actual execution minimizes the overhead of file I/O.<key, value> pairs, where each key is generated from SAM records i.e., <id_chromosomal_region, position_of_mapping>; the key shows the exact location (mapping position) of mapping in the reference genome. Index function performs indexing BAM  file and index a coordinate-sorted BAM file for fast random access. The concatenation function processes intermediate SAM and BAM files. It replaces groups of reads in the BAM file. It allows us to replace all groups of reads in the input file with a single new read group and allocate all reads to this reading group in the output BAM file. Sort algorithm sorts and merges BAM or SAM file and removes the duplicate reads. Genome reads that are aligned to the same chromosomal region are grouped together to form a single reduce task.The input sequence reads are divided into segments as the default size of HDFS i.e., 64 MB. The Bowtie aligner v.2  is used <key, value> pairs as input for the single chromosomal region, which is stored in SAM or BAM file format. Here, multiple instances are created to perform SNPs calling. Heap is an accurate and highly sensitive SNP detection tool for high throughput sequencing data and offers equally dependable SNPs with distinct locus to genomic prediction (GP) and genome-wide association studies (GWAS) [<key, value> pairs. Variant calling format (VCF) file is generated at the end of each reduce task. VCF file consists of the SNPs detected in the corresponding chromosomal region. Finally, all the VCF files are merged into a single VCF file to present all the SNPs detected among the samples. The mining of SNPs generates the output to show the region-wise saturation. SNP caller calls the SNPs and generates the output which provides the number of SNPs. This study has improved the SNP caller results and SNP mining which shows the specific positions in the genome where the SNPs exist. It is more helpful for the target-based investigation of SNPs in a specific range of a genome.P represents error probabilityQ represents quality score (Phred Score).Generally, the number of reduce tasks as much greater than the number of reducers; a number of reduce tasks are executed parallel. A particular task accepts all sorted intermediate s (GWAS) . Heap pes (GWAS) , that fuAlgorithm 1. Dynamic load balancing algorithmProcedure_generate_BAM_files1.Elements of the distributed dataset {region_id, }Input 2.Obtain the number of sequence reads for each segmentSeq_reads_per_segment 3Obtain the total number of sequence readsTotal_number_of_reads 4.Obtain chromosomal region-based input data and group themChromosomal_region.cacheChromosomal_region 5.Compute the average number of reads based on load balancing regionAvg_seq_reads 6.Generate BAM to further divide a region (if required) and build BAM filesChromosomal_region.foreach{ end procedureIn order to get the best performance from available resources, a dynamic load balancing algorithm as shown in Algorithm 1 is applied to balance the load, which remains active in process execution. A region with too many reads can be further divided via dynamic load balancing, so the execution time for several procedures in the workflow depends on the number of reads being processed. It is used as a local resource manager and is responsible for managing computing resources. Particularly, the dynamic load balancing algorithm consists of load estimation and resource management components. The load estimation component is used to calculate a load of a task instance while considering the size of data and training parameters, which are used to represent the computational complexity. The resource management component is used to assign the estimated amount of resources physically. It is worthful to note here that the dynamic load balancing algorithm does not change the resource scheduling algorithm of the Hadoop framework. Rather, it takes over the resources that have been pre-assigned for each lunched task. Then, the dynamic load balancing algorithm is used to re-assign the resources for sub-constitute tools in each task via reconfiguring their runtime parameters.https://www.gzip.org/). The human genome hg19 resource bundle available from [Experimental datasets are obtained from NCBI  and DDBJble from  is used ® Core™ i7-7700K with four cores @ 4.20 CPU having eight threads along with 64-GB of memory installed, running on 64-bit instruction set kernel Linux (Ubuntu 16.04.6 LTS) operating system (OS). The real clusters comprise of 8, 16, and 32 compute nodes, the machine used in single node pseudo cluster are configured as server and rest of each node consist of Intel(R) Core i5-7600K with four cores @ 3.8 GHz CPU, 16-GB of memory installed, running on 64-bit instruction set kernel Linux (Ubuntu 16.04.4 LTS) OS. All the nodes are connected through the 10Gbit/s Ethernet network.Various experimental setups are used for the evaluation of the proposed framework in comparison with other state-of-the-art models e.g., GATK 4.0, FaSD, Halvade, and SparkGA. Single node pseudo cluster and real clusters consist of 8, 16, and 32 working nodes are used for scaling and analysis. Single node pseudo cluster consists of IntelSensitivity, Specificity, and Accuracies are the terms that are mostly associated with a classification test and they statistically measure the performance of the test. In classification, we divide a given data set into two categories based on whether they have common properties or not by identifying their significance in a classification test. In general, sensitivity indicates, how well the test predicts one category and specificity measures how well the test predicts the other category. Whereas Accuracy is expected to measure how well the test predicts categories. If an SNP detected, further it has two possibilities as either it is true or not which is termed as a true positive (TP) and false positive (FP) respectively. Similarly, on the other hand, if an SNP is not detected, then it has also two categories i.e., true negative (TN) or false negative (FN). In , true deMost of the SNPs caller algorithm detects the SNPs and generates the output in VCF file format. The output shows the details of SNPs detected and the number of SNPs. SNPs mining facilitates to identify the region-wise position of SNPs throughout the genome length in terms of position ID. The ID contains the starting position and ending position of a genomic region where SNPs exist. The region length tells the length of the region of these SNPs.To evaluate the correctness and validity of the proposed framework sample datasets were extracted from all benchmarked datasets with consistent length i.e., 2000 genome length with 101 read length and executed on a single node. Each workflow experiment was executed 100 times and average time in seconds is computed for sample datasets, where the results of real clusters are recorded in minutes for clear visualization and ease of understanding. Results analysis of sample datasets shows that the proposed framework produced good results than others. For scalability analysis all the workflows are evaluated on real compute clusters of different configurations i.e., 8 compute nodes @ 116 GHz processing power with 32 cores equipped with 112 GB of memory, 16 compute nodes @ 237.60 GHz processing power with 64 cores equipped with 304 GB of memory and 32 compute nodes @ 471.2 GHz processing power with 128 cores equipped with 560 GB of memory. All the nodes are connected through 10 Gbit/s Ethernet network. GATK correctly calls SNPs if enough numerals of reads coverage are delivered i.e., 20× or more for enough sensitivity in genome re-sequencing, which is difficult under low read coverage, 7× or lower. FaSD uses the Bowtie for sequence read’s alignment by default. Additionally, it requires high processing hardware infrastructure. Heap improves the sensitivity and accuracy of SNPs calling with lower coverage NGS data. Heap reduces the FP rate and accomplishes the highest F-scores with low coverage (7×). F-score is the harmonic means of sensitivity and PPV.The default configurations for memory utilization and management are considered for all the existing workflows. For a fair comparison, the default configuration for memory management of Hadoop Map-Reduce is also considered for the proposed model as described next. On every node, Map-Reduce updates mapred-site.xml file with the number of map and reduce slots based on the number of computing instances available on the node. Traditionally, data are stored in block units. The memory path is updated upon the writing of each data block and finally reaches to the end of the array for redirection to the head. To make sure that data are written into the memory, the policy is re-written for selecting the storage path in the HDFS. Data files are assigned paths with different priorities, sort them based on priority, and then store the file paths into the array of the data node and check the paths in the array from the start when data is written. The observations and analysis of memory utilization show that all the workflows including the proposed model consume approximately the same memory.GATK uses the BWA aligner as the default aligner, however, in  the GATKResults given in SNP is a variation of a single nucleotide that exists at a particular locus in the genome, where respective variant exists to a noticeable degree in the population of a residence. Detecting SNPs in high dimensional genomic data is difficult, due to the growing number of genetic variations in genome sequences. It is helpful in biological research to assess an individual’s reaction to certain drugs, defenselessness towards environmental factors like toxins, and risk of disease. The Hadoop is a novel platform using the Map-Reduce programming framework which runs on any cluster only with the prerequisite of Java. It provides the scalability, reusability, and reproducibility features. The Hadoop Map-Reduce can also be used for fast computation and processing to detect the SNPs in genome sequences. Hadoop Map-Reduce proves the capability to process NGS data to detect the SNP in less time with higher accuracy. In this research study, we proposed Hadoop based framework integrated with Heap for SNPs detection which enhances the SNPs detection rate and optimizes the execution time. The proposed framework is executed on a various number of nodes with different configurations. To validate the framework, different benchmark datasets have been used and the results are recorded for comparison with other state-of-the-art pipelines. This research contributed as a novel framework for SNP detection which has improved the SNPs detection rate, optimized the execution time and mined SNPs as well.In the future, it is intended to identify SNPs associated with complex diseases such as cancer, diabetes, and heart disease on a large scale, e.g., cloud computing environment integrated with optimization technique of artificial intelligence and mine them. It is also intended to optimize memory requirement in the future.


The camels sampled from the central desert of Iran exhibited higher relatedness than Sudanese and lower than Arabian Peninsula dromedaries. Recent Ne of Iran’s camels was estimated to be 89. Predicted Tajima’s D (1.28) suggested a bottleneck or balancing selection in dromedary camels in the central desert of Iran. A general decrease in effective and census population size poses a threat for Iran’s dromedaries. This report is the first SNP calling report on nearly the chromosome level and a first step towards understanding genomic diversity, population structure and demography in Iranian dromedaries.The development of camel husbandry for good production in a desert climate is very important, thus we need to understand the genetic basis of camels and give attention to genomic analysis. We assessed genome-wide diversity, linkage disequilibrium (LD), effective population size (Ne) and relatedness in 96 dromedaries originating from five different regions of the central desert of Iran using genotyping-by-sequencing (GBS). A total of 14,522 Single Nucleotide Polymorphisms (SNPs) with an average minor allele frequency (MAF) of 0.19 passed quality control and filtering steps. The average observed heterozygosity in the population was estimated at 0.25 ± 0.03. The mean of LD at distances shorter than 40 kb was low ( Camels have a unique morphology and physiology as they are capable of providing vital products in a desert climate, even under harsh conditions. Camelini (one- and two-humped camels) and Lamini (New World camels) are two tribes of the Camelidae family . Camelidn = 41), Bahabad (n = 8), Khatam (n = 17), Mehriz (n = 8) and Saghand (n = 22)) ) .The blood samples were gathered with EDTA tubes, and genomic DNA was extracted using the modified salt-out method. In this method, optimization includes utilization of separate buffers instead of buffy coat isolation, chloroform for DNA phase isolation and extraction of purified DNA, and sodium acetate for more concentrated DNA . The extThe sequence reads were mapped to the dromedary reference genome assembly on chromosome level  by usinFST among populations were calculated by using the StAMPP package in R [r2 (Equation (1)) [ABP, abP, AbP, and aBP are the haplotype frequencies and AP, aP, BP, and bP are the allele frequencies, respectively. The LD decay plot was created in popLDecay [Quality control (QC) steps, linkage disequilibrium (LD), genome-wide diversity (observed and expected heterozygosity), as well as admixture analyses were performed using TASSEL V5.0 . Variantage in R . To inveion (1)) :(1)r2=(PopLDecay .Ne) was predicted using the pairwise LD [r2, Ne, and c (Equation (1) [r2 was estimated following Equation (3) [c is the recombination rate, n is the number of individuals sampled; β = 2 when the gametic phase is known and β = 1 if instead the phase is not known. Ne during t generations ago was predicted (Equation (4)):TN is the effective size t generations ago, computed as t = (2ƒ(tc)) − 1 [tc is the recombination rate defined for a specific physical distance between markers and optimally adjusted with the mapping functions mentioned above; α = {1, 2, 2.2} is a correction for the mutation rates [Effective population size (rwise LD . The SNerwise LD  based ontion (1) ). The cotion (3) :(2)E(r2)ct)) − 1 ; ct is ton rates .ijk is the difference between sequence i and j.Tajimas’s D was estimated to investigate demography or signals of selection (Equation (5)) .Tajima_A total of 14,522 SNPs resulted after filtering and were used for final analysis. The largest gap between SNPs  was located on chromosome 11. We note that this is the first SNPs-based report at chromosome-level . Before r2 = 0.089 ± 0.234) was observed at distances shorter than 40 kb, which decreased to 0.019 ± 0.034 in 40–60 kb. The LD decay trend is shown in r2 > 0.20 related to 0–40 kb intervals (Linkage disequilibrium (LD) was estimated for each pairwise combination of SNPs . Average LD   .jk = −0.011 ± 0.113).Because of a limited number of bull camels and half-sib calves within herds, there were node clusters visible, although the median of relatedness among camels was very low  was estimated as 89 to 487, respectively . The treCamelus bactrianus breeds in East Asia by using the GBS technique [S across different studies can be due to different techniques or the heterozygosity levels in the other camel groups.Guo et al., (2020) identified 2,433,732 variants in 366 camels, including the seven domestic echnique . Bahbahaechnique . Al Abrijk = − 0.007 ± 0.018) and lower ones than individuals from the Arabian Peninsula (median Ajk = 0.347 ± 0.093) [Heterozygosity of the Iranian central desert dromedaries was predicted as low (0.25 ± 0.30). Arabian Peninsula and Sudanese dromedaries’ ectively . The cam± 0.093) . Predict± 0.093)  and consr2 (LD) was 0.155, 0.156, and 0.128 across all chromosomes for three sheep breeds [r2 = 0.6 in 5 kb and up to 20 kb in Thoroughbred horses. The average r2 > 0.2 at 1.0–1.5 Mb intervals was predicted in two pig populations [Until recently, LD patterns of many livestock species have been reported. For example, the amounts of LD at 0–30 kb distances in Angus, Charolais, and C beef cattle were 0.29, 0.22, and 0.21, respectively . The meap breeds . Corbin p breeds  reportedulations . The meaPrincipal component analysis suggested that Sudanese camel populations are almost homogeneous . On the The trend of Ne shows a bottleneck around 2000–6000 years ago in Iranian central desert dromedary camels. This is consistent with a decline in Ne in Old World camels around 4000–5000 years ago and might be related to the domestication scenarios of dromedary and Bactrian camels [Commercial SNP arrays have been developed for many livestock animals but no SNPchip is available for dromedaries. GBS based on whole-genome sequencing has solved the problem of ascertainment bias  as the e


Although the frying oil of barley showed higher p-anisidin value than that of wheat, the oil samples’ deterioration level measured in this study was low. The antioxidant capacity and polyphenol content in the three flours samples were higher than those in wheat sample, with buckwheat producing the greatest values, followed by Job’s tears, and then barley. Thus, deep-fried products prepared using the three flours demonstrated superior antioxidant capacity owing to the abundance of antioxidant components. Therefore, tempura can be enjoyed in a healthier manner by using batter prepared using those flours, and substituting wheat flour with the three flours can increase the antioxidant capacity of deep-fried products.Tempura is a dish of battered and deep-fried foods, and wheat flour is typically used; however, barley, buckwheat, and Job’s tears have an antioxidant capacity. This study investigated whether replacing wheat flour with flours from these three crops in tempura affects the antioxidant capacity and deterioration of frying oil. Radical scavenging activity and polyphenol content of tempura were measured by chemiluminescence-based assay and the Folin–Denis method, respectively. The peroxide value,  Each of the methods described above effectively improved the quality of tempura. Another study by Martínez-Pineda et al. . Al. Al10].


In this study, a comparative study of the genomic regions in the eight high-yielding varieties (HYVs) was conducted with those of the four non-HYVs. The Next-Generation Sequencing (NGS) mapping on the Nipponbare reference genome identified a total of 14 common genomic regions of japonica-originated alleles. Interestingly, the HYVs shared japonica-originated genomic regions on nine chromosomes, although they were developed through different breeding programs. A panel of 94 varieties was classified into four varietal groups with 38 single nucleotide polymorphism (SNP) markers from 38 genes residing in the japonica-originated genomic regions and 16 additional trait-specific SNPs. As expected, the japonica-originated genomic regions were only present in the japonica (JAP) and HYV groups, except for Chr4-1 and Chr4-2. The Wx gene, located within Chr6-1, was present in the HYV and JAP variety groups, while the yield-related genes were conserved as indica alleles in HYVs. The japonica-originated genomic regions and alleles shared by HYVs can be employed in molecular breeding programs to further develop the HYVs in temperate rice.The inter-subspecific crossing between  Oryza sativa), indica and japonica. Indica rice is known to be adaptable to tropical regions, while japonica rice is grown in temperate regions. Therefore, indica and japonica have different characteristics , Milyang 23 [PRJNA264250], Dasan (or Dasanbyeo) [PRJNA222717], Cheongcheong (or Cheongcheongbyeo) [PRJNA616202], Nampung (or Nampungbyeo) [PRJNA616219], Hanareum (or Hanareumbyeo) [PRJNA616209], Takanari [PRJNA616222], and Minghui 63 [PRJNA616216]. The raw sequence data of Tongil, Yukara, IR 8, and TN1 are available in a previous study [indica and japonica genomes. There were two types of values calculated in this study: variety value and reference value. The variety value calculated whether it was of japonica-type parental allele  of Tongil or a Tongil-like variety or not. The variety value of SNP was calculated as the sum of the following values: ‘1’ ; ‘2’ ; ‘4’ ; and ‘0’ . If the SNP is the same as the Nipponbare reference, the value ‘1’ was given to the SNP; otherwise, the value ‘0’ was given. The SNPs showing variety value ‘4’ and reference value ‘1’ were called japonica-type SNPs. Then, the total number of japonica-type SNPs in each 100 kb block, which is the approximate chromosomal distance for the linkage disequilibrium (LD) decay rate in rice [japonica.Genotype calling was performed in order to identify SNPs that originated from the  in rice , in eachjaponica-originated traits. Out of the many SNPs in the genes, only one SNP with a substitution polymorphism between indica (IR 8 and TN1) and japonica (Nipponbare and Yukara) per representative gene was selected for the genomic validation. The SNP markers for the Fluidigm platform  were designed using the method by Seo et al. [A total of 39 representative genes were selected from the selected regions. The representative genes were well-annotated in the public gene/QTL databases, as of 6 January 2017 (RAP and UniProt). Then, only the genes containing non-synonymous SNPs in the predicted exon and UTR regions between HYVs and non-HYVs were selected. We assumed that these genes might be a part of the candidate genes that are associated with o et al. . To desio et al. . After cYoung leaves of each plant from all materials used in this study were collected for DNA extraction at the tillering stage. Genomic DNA was extracted using the modified cetyltrimethylammonium bromide (CTAB) method, as described by Murray and Thompson . The conGenotyping was performed using the BioMark™ HD system (Fluidigm) and 96.96 Dynamic Array IFCs (Fluidigm), according to the manufacturer’s protocol in National Instrumentation Center for Environmental Management (NICEM), Seoul National University . Specific Target Amplification (STA) was performed prior to SNP genotyping analysis. PCR was performed in a 5 μL reaction containing 50 ng of the DNA sample, according to the manufacturer’s protocol. For genotyping, SNPtype assays were performed using STA products, according to the manufacturer’s protocol. The genotyping result was acquired using Fluidigm SNP Genotyping Analysis software. All the genotype-calling results were manually checked and any obvious errors in the homozygous or heterozygous clusters were curated.We analyzed basic marker statistics, such as major allele frequency (MAF), heterozygosity, and polymorphism information content (PIC) of SNP markers using PowerMarker V3.25 . PowerMajaponica-originated regions of eight HYVs were selected. After checking if all the selected QTLs were unique or redundant, the redundant QTLs were discarded and only the unique QTLs were left remained. After the filtering step for the QTLs, they were classified into seven categories. The category classification was conducted by checking the characters and the trait names manually.We extracted QTL information from the Q-TARO database at 25th August 2017. The physical position in Q-TARO is based on IRGSP build 4 of the Nipponbare genome, while IRGSP 1.0 of the Nipponbare genome was used in this study. Thus, the physical position of the start and end of each QTLs in Q-TARO were converted into the physical positions of IRGSP 1.0. Then, the QTLs overlapped on common indica–japonica crosses, the whole genomes of HYVs and four varieties were sequenced on the Illumina platform. A large number of short reads were mapped onto the reference Nipponbare genome and were then assembled into a consensus sequence. The number of sequence yields and the number of reads and mapping depths were varied. For example, a total of 66,464,246 reads of the Cheongcheongbyeo genome, corresponding to 9,991,040,272 bp (10 Gb), were generated, representing a 21-fold mapping depth. Nipponbare showed the largest number of sequence yields and number of reads and mapping depths  genome. More than 90% of the SNPs were detected in the intergenic region. The smallest number of SNPs were in the 5′ untranslated region (UTR). Two indica varieties, IR 8 and TN1, represented a relatively large number of SNPs, as compared to seven HYVs except for Minghui 63. Among the seven HYVs, Milyang 23 showed the smallest number of SNPs  of co-located QTLs was detected in the Chr6-1 region for eating quality. All the regions were co-located with the QTLs for abiotic stress tolerance  of selected genes was located on Chr1-2, which is the largest region, spanning 2 Mb. Only one gene was selected from Chr2-2, Chr7-2, and Chr11-1. The size of these three blocks was 0.1–0.2 Mb. The genes annotated from the major criteria of interest were Os01g0348900, Os06g0130000, Os06g0130100 (stress tolerance), Os06g0130400 , and Os01g0367100   .japonica-originated genomic regions, by one marker per one gene. The SNPs for the marker were selected from among the non-synonymous SNPs. Five SNP markers, out of the 39 SNP markers, were designed in 3′ or 5′ UTR. In addition, 14 agronomic traits, related to SNP markers in indica-japonica SNP set 2 [Os01g0348900 on the Chr1-2 block and two SNP markers which showed low base call quality, SaF-CT and SLG7-GC, in indica-japonica SNP set 2. Therefore, we conducted a further analysis using a total of 54 polymorphic SNP markers  were developed by the International Rice Research Institute (IRRI). IRBB23, IRBB61, and IRBB66 are near-isogenic line series of IR24 for the improvement of the resistance against bacterial leaf blight [japonica (CP-SLO) and two indica (SIGADIS and IR8) [The results of genotyping showed a dividing pattern for 94 varieties. A phylogenetic analysis of 94 varieties was carried out using 54 polymorphic SNP markers. There were four groups, including IND1, IND2, HYV, and JAP, in the phylogenetic tree . All theeongbyeo . Taebaekf blight . IR24 isand IR8) . Furtherjaponica-like regions and discriminated IND1, IND2, and HYV by the frequency of japonica-alleles. The HYV group showed 93.8% for japonica-alleles frequency, which is similar to that of JAP. A total of 16 SNP markers were related to yield and some agronomic traits could distinguish JAP from the other three groups. For the 16 SNP set, the JAP group represented 86.1% of japonica allele, while the other three groups showed a lower japonica allele frequency, which was less than 30%. The HYV group contained indica alleles and were informed by the makers that were linked to the genes associated with plant architecture , yield potential , and subspecies differentiation . They contained more than 50% japonica alleles using the markers linked to the genes for grain shape and quality  of HYV-type. Practically, the markers designed in the japonica-originated genomic regions and the yield-related markers from indica varieties can differentiate HYV from indica and japonica varieties. In fact, a high proportion of japonica alleles on Chr1-1, Chr1-2, and Chr3-1 were found in IND2, which consist of three aus varieties, one wild rice relative accession, and one weedy rice variety.A total of 38 SNP markers were developed in the common Tongil-like indica group, based on indica–japonica SNP sets reported in a previous study [japonica-type SNPs were detected within the genomes of HYVs after resequencing analysis. Furthermore, collocations of 14 japonica-originated genomic regions commonly present in Tongil-like HYVs, inherited from indica–japonica crosses, were investigated. This suggests that these japonica-type genomic segments were commonly and repeatedly selected during independent breeding programs in the temperate rice cultivation area. To investigate the role of these japonica segments in HYVs, a comparative study of the reported QTLs and representative gene selection were conducted. Eating quality, stress tolerance, and yield related traits might be main drivers for the selection of the rice HYV breeding program.All eight HYVs used in this study were clustered into the us study . Interesjaponica-type regions and 16 trait-specific SNP markers, were used for the genotyping of 94 diverse varieties across indica and japonica. For the 38 SNP marker set, 16 SNP markers were located on chromosome 1, which was not identified as a japonica genomic segment in Takanari. Consequently, Takanari was clustered into the IND1 subgroup, although it showed a similar genotypic pattern to the other HYVs. The Chr1-1 and Chr1-2 regions were co-located with some abiotic tolerance and yield related QTLs. Furthermore, there were several selected genes that conferred abiotic stress tolerance and yield potential in the blocks. For instance, Os01g0337100 (OsTPS1) reported an association with abiotic stress response and tolerance by knock-out and overexpression [Os01g0367100 (PHD1) was shown to be a gene that was involved in galactolipid biosynthesis and affected photosynthetic efficiency [PHD1 on grain yield was also reported using the 3K rice genome panel [japonica genomic segment from a different natural environment and/or breeder’s selection.A total of 54 SNP markers, including 38 SNP markers developed from 38 selected genes for 14 common pression ,41. Os01ficiency . Recentlme panel . Takanarjaponica-type bWx allele on the SNP marker WAXY-TG, which was designed on the splicing site in the intron of Wx gene in the japonica-type region. The Wx gene only contains synonymous SNPs, although it is located within Chr6-1; thus, it was not selected in our study. We previously developed a functional SNP marker for the Wx gene [Wx gene is a hotspot for grain quality [japonica-type SNP pattern on the common japonica-type region on chromosome 6 [japonica varieties [Os06g0130400, one of the selected genes in Chr6-1, was also reported as the gene controlling starch grain size in endosperm [Os06g0130000 and Os06g0130100 were reported for resistance to rice blast and bacterial blight, and tolerance to drought and salt stress, respectively [Wx gene, might be mainly selected for eating quality and latent stress tolerance.In addition, 18 varieties out of the 19 HYV-types, showed  Wx gene . The gen quality  and has  quality . The othmosome 6 . Tongil-arieties ,47. Furtndosperm . Os06g01ectively ,50. Therjaponicas [japonica chromosomal introgression regions identified in this study were regarded as putative temperate region adaptable and improved the eating quality of indica. For this reason, the varieties developed by indica–japonica crosses could also be considered as ‘temperate indica’. Recently, new elite rice varieties showing high yield potential and high grain quality were developed by the precise pyramiding of major genes controlling yield and grain quality traits [indica using an inter-subspecific cross and marker-assisted selection (MAS) [indica varieties, which are adaptable to the temperate region with high yield potential and good eating quality, can be efficiently achieved through inter-subspecific crosses and marker-assisted selection using the SNP markers developed in this study. Nevertheless, to dissect the exact contribution of japonica-type regions in HYVs, a comprehensive genetic and physiological analysis, by applying the molecular markers developed in japonica-type regions to the segregation populations derived from cross between HYVs and indica, is necessary. In addition, the functional studies of genes in the regions, as well as the selected ones in this study, are also required.When HYVs were developed by inter-subspecific hybridization, the breeders aimed at not only transferring some of the desirable characteristics, like resistance to lodging, blast, and yield, but also at retaining the ecological adaptability and eating quality of aponicas . The japy traits . Furtheron (MAS) . In otheindica, were revisited by the genomic analysis of HYVs. The 14 japonica-originated genomic regions and alleles identified in this study shared by HYVs could be applied in further development of more HYVs through inter-subspecific rice breeding in temperate rice.Consumer preference in grain shape and quality during conventional breeding procedures, without sacrificing the high-yield potential of


FROH), homozygosity (FHOM), genomic relationship matrix (FGRM) and pedigree (FPED). Furthermore, we identified genomic regions with high ROH frequencies and annotated their candidate genes.Runs of homozygosity (ROHs) are homozygous segments of the genome where the two haplotypes inherited from the parents are identical. The current availability of genotypes for a very large number of single nucleotide polymorphisms (SNPs) is leading to more accurate characterization of ROHs in the whole genome. Here, we investigated the occurrence and distribution of ROHs in 3,692 Large White pigs and compared estimates of inbreeding coefficients calculated based on ROHs (Sus scrofa chromosomes (SSC) by ROH were on SSC14 and SSC13, respectively. The highest pairwise correlation among the different inbreeding coefficient estimates was 0.95 between FROH_total and FHOM, while the lowest was − 0.083 between FGRM and FPED. The correlations between FPED and FROH using four classes of ROH lengths ranged from 0.18 to 0.37 and increased with increasing ROH length, except for ROH > 10 Mb. Twelve ROH islands were located on four chromosomes . These ROH islands harboured genes associated with reproduction, muscular development, fat deposition and adaptation, such as SIRT1, MYPN, SETDB1 and PSMD4.In total, 176,182 ROHs were identified from 3,569 animals, and all individuals displayed at least one ROH longer than 1 Mb. The ROHs identified were unevenly distributed on the autosomes. The highest and lowest coverages of FROH can be used to accurately assess individual inbreeding levels compared to other inbreeding coefficient estimators. In the absence of pedigree records, FROH can provide an alternative to inbreeding estimates. Our findings can be used not only to effectively increase the response to selection by appropriately managing the rate of inbreeding and minimizing the negative effects of inbreeding depression but also to help detect genomic regions with an effect on traits under selection. The inheritance of identical haplotypes from a common ancestor creates long tracts of homozygous genotypes known as runs of homozygosity (ROHs) , 2. LongF), which is defined as the probability that both alleles at any locus within an individual are IBD [F. Traditionally, F has been estimated based on pedigree information (FPED). Hence, the estimation of FPED relied strongly on the accuracy and amount of pedigree data available [FGRM) using genome-wide single nucleotide polymorphism (SNP) data [F can also measure homozygosity (FHOM) directly and, thus, can accurately reflect the actual percentage of the genome that is homozygous. Furthermore, genomic F allows us to estimate inbreeding and inbreeding depression for specific genomic regions, which is not possible with FPED. In addition, genomic F can be estimated in populations without pedigree information [On the basis of levels of autozygosity, ROHs can be used to estimate the inbreeding coefficient of an individual ( are IBD . Inbreed are IBD . Since ivailable . Currentvailable . The inbNP) data . Genomicormation .F using genotypic data, there is a need to distinguish between alleles that are IBD or identical by state (IBS). This distinction is not easy to make when single markers are analysed, but the use of ROH allows it. Hence, FGRM and FHOM always overestimate inbreeding levels compared to FPED [When computing the more accurate genomic  to FPED , and an FROH) and estimated their correlations with those from other methods, including genomic relationship matrix (FGRM), homozygous coefficients (FHOM) and pedigree-based coefficients (FPED). Moreover, ROH islands may have occurred due to selection for functionally important traits.The objective of this study was to identify and characterize ROH patterns in a Large White pig population. We further computed the inbreeding coefficients based on the ROHs identified  .Sus scrofa 10.2 genome version. To update the SNP positions, we reordered the SNPs according to the newest version of the pig genome, Sus scrofa 11.1. Following this, 34,689 autosomal SNPs remained, with a mean distance of 67.052 kb between adjacent SNPs.In this study, ear tissue samples were collected from 3,692 Large White pigs from two commercial companies. In these two companies, there were 1,466 and 2,226 pigs, respectively. All samples were genotyped with the GeneSeek GGP Porcine HD array. The SNP chip comprises 50,915 probes that are distributed across 18 autosomes and two sex chromosomes according to the Quality control was performed using PLINK v1.90 software  accordinNe) was calculated from linkage disequilibrium (LD) [The effective population size (ium (LD)  using thNT(t) is the effective population size estimated t generations in the past, ct is the recombination rate defined for a specific physical distance between SNP markers t generations in the past, f(ct) is the Haldane mapping function of the genetic distance in Morgans between SNPs, where mentclasspt{minimaNe was computed by implementing SNeP software [Ne estimation were 0.05 and 5 Mb, respectively. The other parameters were set by default [In this study, software . Considesoftware , we usedsoftware . The min default .ROHs were identified for each individual using PLINK v1.90 software , which uns is the number of SNPs per individual, ni is the number of individuals, and where α is the percentage of false positive ROHs (set to 0.05 in the present study), In this study, the ROHs identified were further divided into three classes: 1~5, 5~10 and > 10 Mb.F) were used for all animals:FPED) were computed for 3,569 pigs using pedigree information recorded between 2011 and 2019. For all individuals who passed quality control, complete pedigree records were available for 3 to 10 generations with an average depth of 6.86. Pedigree information on a total of 7,572 animals between 2011 and 2019 was available. The FPED was estimated for datasets  according to Wright’s coefficient [pedigreemm [The genealogical inbreeding coefficients (fficient  with thedigreemm .FHOM) was computed as FHOM = (O − E)/(L − E), where O is the number of observed homozygotes, E is the number expected by chance, and L is the number of genotyped autosomal SNPs.Genomic inbreeding based on homozygous SNPs was determined using PLINK v1.90 software . The inbFGRM) estimates were calculated by GCTA software [FGRM was calculated as xi is the number of copies of the reference allele for the ith SNP, m is the number of SNPs, and pi is the frequency of the reference allele.Genomic SNP-by-SNP inbreeding coefficient (software . The FGRFROH). The FROH for each animal was calculated as i of individual i, and Lauto is the autosomal genome length covered by the SNPs included in the chip.Genomic inbreeding coefficients were also estimated based on ROHs . The biological function of each annotated gene within the ROH islands was determined through an extensive accurate literature search.The gene content of the ROH islands was annotated using the annotation database provided by NCBI (Ne) estimated based on linkage disequilibrium (LD) is illustrated in Fig. Ne from 197 generations to 10 generations ago of each breed was estimated based on the LD decay. An increasing Ne as a function of the number of generations was observed, with a Ne of 99 estimated at 10 generations ago and 288 estimated at 197 generations ago.The tendency of effective population sizes  obtained using all pedigree data available ranged from 0 to 0.156 with an average of 0.011. The four kinds of FROH were calculated based on three classes of ROH and the total ROH lengths. The average genomic inbreeding based on the total observed ROHs  was 0.140 with a range from 0.008 to 0.409 in this population. The estimated FGRM inbreeding coefficients ranged from −0.168 to 1.359 (mean = 0.099), and the estimated FHOM inbreeding coefficients ranged from −0.177 to 0.431 (mean = 0.103).In the present study, seven kinds of inbreeding coefficients were estimated based on the pedigree or genomic data of all individuals. The pedigree-based inbreeding estimates .Figure SIRT1 gene. SIRT1 is related to porcine ovarian cell function, suppresses adipogenesis [MYPN (63.21% of occurrences). The MYPN gene has been documented to be associated with meat and carcass traits in Italian Large White pigs [The percentage of SNPs in ROHs was plotted against the positions of the SNPs along the chromosomes in Fig. ogenesis , 22, andogenesis . The regite pigs . Twelve FPED value is the statistical expectation of the probable IBD genomic proportion [FPED does not take into account the stochastic events of recombination during meiosis [FPED could not completely show the actual relatedness among individuals in the population. With the development of high-throughput genotyping technologies and the reduction in genotyping costs, the inbreeding coefficient can be computed based on molecular information [FHOM cannot distinguish IBD alleles from IBS alleles, it might overestimate inbreeding levels [FHOM and FGRM values can be negative for some individuals. Therefore, using the above three methods to estimate the inbreeding coefficient is not sufficiently accurate. FROH can alleviate the issues mentioned above, and thus, FROH might be a more effective and accurate alternative for quantifying animal relatedness and inbreeding levels in theory [Traditionally, the inbreeding coefficient was estimated based on pedigree data. In reality, pedigree information might be erroneous, such as having a missing parent or incorrect parent information. Furthermore, Large White pigs in the base population could be hypothesized to be unrelated. Moreover, the oportion , and FPE meiosis . Thus, Formation –29. Geneormation . Since Fg levels . In addin theory , 32.FROH in the Large White pig population. The correlation between the FPED and FROH estimates was low (0.18–0.37) (Table FROH > 10 Mb and FPED was the highest (0.37) among those between different classes of ROH and FPED in the Large White pig population, while the correlation between FPED and FROH_total was slightly lower (0.35)  Fig. . Compareg Morgans, where g is the number of generations since the common ancestor. Recombination events can interrupt long chromosome segments, so long ROHs (~ 10 Mb) arise as a result of recent inbreeding (up to five generations ago), and short ROHs (~ 1 Mb) are produced by IBD genomic regions from old ancestors [ROHs can reveal the time that inbreeding occurred given the approximate correlation between the length of a ROH and the distance to the common ancestor due to the occurrence of recombination events over time. Fisher  reportedncestors , which aROH than with GGRM by simulation study.ROHs can be used to improve the accuracies of genomic breeding values (GEBV). EBVs estimated by the traditional BLUP method are based on pedigrees, which are used to construct a numerator relationship matrix (A matrix). If the A matrix is replaced by a relationship matrix based on genotype data (G matrix), GEBV can be obtained with BLUP. Based on the genotype matrix, it is not possible to identify whether IBS marker alleles are IBD or not. ROHs can identify alleles in the same or different individual(s), which indicates IBD . Luan etSIRT1 may regulate granulosa cell apoptosis during follicular atresia in porcine ovaries [SETDB1 plays an essential role in the maintenance of gonocyte survival in pigs, and knockdown of SETDB1 can induce gonocyte apoptosis [PSMD4 effectively inhibits sperm-oocyte binding [GNRHR2 is involved in regulating reproductive behaviour in pigs [CES5A, GAL3ST1 and SPAG17 are essential for spermatogenesis and male fertility [MYPN showed an association with traits related to muscularity in Pietrain×(Landrace×Large White) and Duroc × Pietrain, particularly association with ham weight and lean content in Duroc × Pietrain [SLC12A4 is differentially expressed between the white and red skeletal muscle of Chinese Meishan pigs [SIRT1 may downregulate pig preadipocyte proliferation and differentiation [FMO5 plays a role in increasing glucose metabolism and insulin sensitivity in brown adipose tissue [SIM1 is involved in the regulation of energy homeostasis [KIF1BP and MCHR2 are involved in the regulation of food intake [HORMAD1, TBX15 and WARS2 are also associated with obesity [ADGRB3 is related to environmental information processing and environmental adaptation in domestic yak [Here, the ROH islands harboured many candidate genes controlling economically important traits of Large White pigs Table S. We iden ovaries  and can  ovaries . SETDB1 poptosis . PSMD4 e binding . GNRHR2  in pigs . CES5A, ertility –46. SomePietrain . SLC12A4han pigs . SIRT1 mntiation . FMO5 ple tissue . SIM1 iseostasis , and KIFd intake , 52, whid intake . HORMAD1 obesity , 55. In stic yak .v6.8 tool [Sus scrofa annotation file as background to identify significant (P < 0.05) GO terms and KEGG pathways. Several GO terms  were significant, and two were significant for KEGG  based on run-of-homozygosity islands.