Additionally, 28,298 markers were included for the evaluation of previously identified disease risk markers from the literature and databases, and those present in the Japanese population were extracted using the reference panel. Through genotyping 286 Japanese samples, we found that the imputation quality r2 and INFO score in the minor allele frequency bin >2.5–5% were >0.9 and >0.8, respectively, and >12 million markers were imputed with an INFO score >0.8. From these results, JPA NEO is a promising tool for genotyping the Japanese population with genome-wide coverage, contributing to the development of genetic risk scores.Ethnic-specific SNP arrays are becoming more important to increase the power of genome-wide association studies in diverse population. In the Tohoku Medical Megabank Project, we have been developing a series of Japonica Arrays (JPA) for genotyping participants based on reference panels constructed from whole-genome sequence data of the Japanese population. Here, we designed a novel version of the SNP array for the Japanese population, called Japonica Array NEO (JPA NEO), comprising a total of 666,883 markers. Among them, 654,246 tag SNPs of autosomes and X chromosome were selected from an expanded reference panel of 3,552 Japanese, 3.5KJPNv2, using pairwise   The Tohoku Medical Megabank (TMM) Project was launched as part of reconstruction efforts following the Great East Japan Earthquake on March 11, 2011, and aims to establish a next-generation medical system for precision medicine and personalized healthcare .We have performed genome/omics analyses within the TMM project and established an integrated biobank that includes biospecimens, health and clinical information and genome/omics data to develop a research infrastructure for genomic medicine . Taking et al. showed that the selection algorithms of the set of SNPs for genotyping (referred to as tag SNPs) based on the r2, which are widely used for pairwise LD measures using reference genome sequences from different populations  and of limited haplotype diversity, depending on the population , 8, and ulations . Using tulations . In largulations , 13. Simulations , the Axiulations  and the ulations ; by the ulations  and by tulations .Most of these large-scale projects adopted the Axiom system because of the flexibility of the manufacturing array, the highly automated assay process and the robust sample tracking with a 96-array layout. Concurrent with the trend towards developing ethnic arrays, we also selected the Axiom system to design a Japanese-specific SNP array [the Japonica Array (JPA)]. We designed the first version of the Japonica Array (JPAv1) in 2014 . JPAv1 cr2 of the LD measure, aiming to improve imputation accuracy and to standardize the data for use in meta-analyses conducted anywhere in the world. We also report the progress of genotyping TMM participants by using all three versions of the JPA.To enhance direct genotyping of previously identified disease risk variants and to obtain maximum genomic coverage with the expanded whole-genome reference panel including nearly 4,000 Japanese individuals , we aimer2 of LD statistics  the maximum score of annotation by ANNOVAR .The list of tag SNPs and disease-related markers was combined with those of Y chromosome and mitochondrial markers. Based on the combined list, the array was produced using the Axiom myDesign service . Multiple probes were designed for markers that were not included in the Axiom™ validated probe sets. Then, control markers were added, and the total number of markers was adjusted to the maximum number for the Axiom 96-array layout. The full marker list and detailed list of disease-related SNPs are available at the jMorp website  of genomic DNA from blood and saliva samples in the TMM biobank were performed as described previously . Genomici.e. Axiom™ 2.0 Genotyping Assay User Manual for 8-plate workflow). Briefly, target DNA was enzymatically amplified and fragmentated, and after confirmations of concentration and fragment length by NanoDrop (Thermo Fisher Scientific) and TapeStation System (Agilent Technologies), hybridization, ligation and scanning were processed by a semiautomated machine, GeneTitan™ Multi-Channel Instrument (Thermo Fisher Scientific). These processes were conducted using liquid-dispensable robots  and managed by a laboratory information management system . For the QC, the dish quality control (DQC), sample QC call rate and plate pass rate were analysed using control markers for the Axiom platform  according to the Axiom™ Genotyping Solution Data Analysis Guide using Axiom™ Power Tools . Genotyping data satisfying the criteria were used for the following QC analyses.A genotype assay was performed according to the manufacturer’s protocol  given by IMPUTE2, was used to analyse the imputation quality for each marker, where the value 0–1 indicated the uncertainty about the imputed genotype  , in JPA r2 . This hai.e. ≥0.005, ≥0.01 or ≥0.05 and pairwise r2 of LD measures; r2 ≥ 0.5 or ≥0.8. Two metrics were used to evaluate tag SNP performance: (1) genomic coverage, which is the proportion of untyped sites with at least one tag SNP with r2 greater than a given threshold and (2) the number of variants obtained by genotype imputation above the threshold of a given INFO score, which is an index of imputation accuracy. When tag SNPs were selected by pairwise r2 ≥ 0.8 and MAF ≥ 0.01, the genomic coverage with r2 ≥ 0.8 and the number of imputed variants from the 2KJPN reference panel  with INFO > 0.9 were better or comparable to those of JPAv2 and Infinium Omni2.5-8  in JPA NEO were MAF < 1% as confirmed by 3.5KJPNv2 . This isWe modified the tag SNPs for JPA NEO from the previous versions with the aim of improving the imputation coverage of the microarray. To verify this point, we analysed the performance of JPA NEO in comparison with that of JPAv2. To this end, the same 286 samples, which were not included in the 3.5KJPNv2 reference panel, were genotyped using both JPA NEO and JPAv2. We found that the median call rates of JPAv2 and JPA NEO for all markers per sample were >99.6% and 99.8%, respectively , indicatMore than 99% of markers were polymorphic in both JPAv2 and JPA NEO, as we intended . Some miWhen we closely inspected the MAF distributions of JPA NEO in comparison with those of JPAv2, we noticed that JPAv2 showed low numbers of MAF markers (15–25%) compared with JPA NEO . We envir2 and INFO score. The mean r2 and INFO score were >0.9 and 0.8, respectively, in MAF bin >2.5–5% of two arrays . Therefore, it seems important to develop a method for utilizing the genotyping data obtained by different JPA array platforms, which we plan to provide as a user guideline when the full data of all 150,000 TMM participants are released.The genotype imputation performance of JPA NEO was evaluated in comparison with that of JPAv2 by performing a small-scale analysis. In the genotyping data obtained by both arrays, monomorphic markers were scarcely observed and the large number of variants were imputed with a high imputation quality JPA NEO incorporates nearly 30,000 disease-related variants previously reported in the literature and stored in databases, to allow for the evaluation of known functional risk alleles in the Japanese population. Because some SNPs with MAF < 1% were included, their SNP cluster plots and the concordance with genotypes obtained by WGS analysis must be carefully assessed. However, qualified disease risk variants can be used for association studies along with phenotype data.The JPAs have been used to perform large-scale genotyping of TMM samples. Whereas we have not experienced any issues with plate QC assessments conducted so far, we are planning to carefully implement batch-based as well as statistical genetic QC analyses to assess whether a plate effect is caused by sample selection bias. Indeed, in the UK Biobank, a sample picking algorithm has been used for genotyping experiments to prevent clustering of participants in the same plate by time or date of collection, collection centre, geography or participant phenotypes . In contAmong the ∼130,000 genotyping data of TMM participants that we have processed so far, samples satisfying the criteria of sample DQC and QC call rate are quite high, especially when using blood samples (>99.8%). However, the pass rate of saliva samples was slightly worse (>89.8%) than that of blood samples in JPAv2. The use of saliva has been reported yield a low rate in other large-scale genotyping projects, for instance, 93.8% in the Genetic Epidemiology Research on Adult Health and Aging cohort . This maData obtained by the genotype imputation array have been successfully utilized for GWAS. Summary statistics of large-scale GWAS are precious for the development of genetic risk scores, such as the polygenic risk score (PRS) . PRSs wiWe believe that our future efforts should be focussed on acquiring genotype data from all participants of the TMM cohorts as well as implementing a GWAS to develop and evaluate genetic risk scores, including PRSs, optimized for the Japanese population. Genomic data obtained by the TMM project will serve as an excellent control for the GWAS executed using other biobanks/cohorts in Japan, and it will also be exploited for GWAS of associated phenotypes and omics data from the TMM project. We also believe that the JPA should continue to be updated. For the next version, we are planning to design a medical checkup array with a minimal set of tag SNPs that nevertheless contains abundant risk SNPs. These efforts will also contribute to further identifying genetic determinants of diseases in those of East Asian ancestry .In conclusion, we designed a new version of the JPA, JPA NEO, to improve both genome-wide coverage and genotyping of disease risk variants. Disease risk variants were selected from the literature and filtered by our reference panel to extract those expected to be present in the Japanese population. Experimental verification using the developed JPA NEO showed greater imputation performance without any bias through a wide range of MAF and with increased imputed variants compared with the previous version. Large-scale genotyping of TMM samples using JPA NEO is now underway. JPA NEO will provide highly accurate, efficient and cost-effective genotyping for the Japanese population. Combining the JPA data of TMM participants with those of other Japanese biobanks/cohorts will be helpful for better understanding the genetic risks of complex diseases, leading to its application for disease risk prediction and prevention and consequently personalized healthcare.K.Ku., C.G., S.M., A.U., S.T., I.N.M., A.O., A.N. and Y.A. performed the computational analyses. M.S.-Y., K.Ku., M.K., S.I., A.O. and H.Ku. prepared the samples and conducted the genotyping experiments. M.S.-Y., M.Y. and K.Ki. wrote the manuscript with assistance from the other authors. M.S.-Y., I.D., J.Y., H.Ka., N.M., S.K., N.F., G.T., M.Y. and K.Ki. conceived and supervised the project. All authors read and approved the final manuscript.JB Online.mvab060_Supplementary_DataClick here for additional data file.


P-value 3.70 × 10–6 at bins 1.05, 1.10, 3.04, 3.05, 4.08, and 10.04. Linkage mapping identified five quantitative trait loci (QTL) at bins 1.03, 2.06, 4.08, 7.03, and 9.00. The phenotypic variation explained (PVE) value of each QTL ranged from 5.40 to 12.45%, accounting for the total PVE value of 40.67%. Joint GWAS and linkage mapping analyses identified a stable genomic region located at bin 4.08. Five significant SNPs were only identified by GWAS, and four QTL were only detected by linkage mapping. The significantly associated SNP of S10_95231291 detected in the GWAS analysis was first reported. The linkage mapping analysis detected two new QTL on chromosomes 7 and 10. The major QTL on chromosome 7 in the region between 144,567,253 and 149,717,562 bp had the largest PVE value of 12.45%. Four candidate genes of GRMZM2G328500, GRMZM2G162250, GRMZM2G114893, and GRMZM2G138949 were identified, which played important roles in the response of stress resilience and the regulation of plant growth and development. Genomic prediction (GP) accuracies observed in the GWAS panel and DH1 population were 0.61 and 0.51, respectively. This study provided new insight into the genetic architecture of quantitative resistance of common rust. In tropical maize, common rust could be improved by pyramiding the new sources of quantitative resistance through marker-assisted selection (MAS) or genomic selection (GS), rather than the implementation of MAS for the single dominant race-specific resistance gene.Common rust is one of the major foliar diseases in maize, leading to significant grain yield losses and poor grain quality. To dissect the genetic architecture of common rust resistance, a genome-wide association study (GWAS) panel and a bi-parental doubled haploid (DH) population, DH1, were used to perform GWAS and linkage mapping analyses. The GWAS results revealed six single-nucleotide polymorphisms (SNPs) significantly associated with quantitative resistance of common rust at a very stringent threshold of  Puccinia sorghi, is one of the major foliar diseases in maize, which can cause up to 49% grain yield loss in susceptible varieties  associated with partial resistance to common rust . These sGenome-wide association study (GWAS) is a useful tool for identifying molecular markers significantly associated with the target trait and exploring the underlying candidate genes . In a coGenomic selection (GS), also known as genomic prediction (GP), is an extension of MAS that uses genome-wide markers to predict the genomic estimated breeding values (GEBVs) of the unphenotyped lines for selection . GP can In this study, a GWAS panel and a bi-parental DH population were used to perform GWAS, linkage mapping, and GP analyses, where both populations were phenotyped in multi-environment trials to evaluate their responses to common rust and genotyped with genotyping-by-sequencing (GBS) single-nucleotide polymorphisms (SNPs). The main objectives of this study were to: (1) detect the significantly associated SNPs, major QTL, and putative candidate genes conferring common rust resistance in tropical maize by the combined use of GWAS and linkage mapping; (2) explore the potential of GS and GP for improving common rust resistance; and (3) estimate the GP accuracies under different factors affecting the accuracy estimation.1 cross formed with two elite inbred lines of CML495 and La Posta Sequia C7 F64-2-6-2-2-B-B-B, CML495 shows good resistance to common rust, and La Posta Sequia C7 F64-2-6-2-2-B-B-B is susceptible to common rust.A GWAS panel of 282 genetically diverse inbred lines was used for the GWAS and GP analyses in this study . The GWABoth populations were evaluated for response to common rust under consistently high natural disease pressure at several locations in Mexico. The DTMA panel was evaluated at Agua Fria in the state of Puebla  in 2008, 2009, 2010, and 2012. Two tropical maize inbred lines (B.T.Z.T.R.L.BA90 12-1-1P-1P-1-1-1-1P-1-B/BTZTVCPR92A 27-7P-1-1P-1P-4P-B-B)-B-60TL-1-1-B-B-B-B and CML139 were used in all the trials as the resistant and susceptible checks, respectively. The population of DH1 was evaluated in two locations in 2013 at El Batan in the state of Mexico  and Santa Catarina in the state of Nuevo Leon , respectively. For the DH1 population, the parental lines were used as the resistant and susceptible checks. A randomized complete block design with three replications was used for all trials. Each plot consisted of 11 plants in a 2 m row with a width of 0.75 m.Plants were visually evaluated for common rust three times at 7-day intervals, beginning 2 weeks after flowering. Disease severity was evaluated on a 1–5 scale based on the percentage of leaf area covered by lesions. A rating scale of 1 corresponds to high resistance covering 0–10% of the leaf surface, 2 corresponds to weak to moderate infection covering 10–25% of the leaf surface, 3 corresponds to moderate infection covering 25–50% of the leaf surface, 4 corresponds to moderate-to-severe infection covering 50–75% of the leaf surface, and 5 corresponds to severe infection covering > 75% of the leaf surface. For each plot, the final highest score was used for further analysis. In both the DTMA panel and the DH1 population, the resistant and susceptible checks were used as controls to check for adequate levels of disease infection.The multi-environment trial analysis was conducted using META-R Version 6.04 . A mixedYijk is the observation of the kth genotype in the ith environment in the jth replicate, μ is the overall mean, Gk is the effect of the kth genotype, Ei is the effect of the ith environment, Rj(i) is the effect of the jth replication nested on the ith environment, EGik is the effect of the interaction between the ith environment and kth genotype, and εijk is the effect of experimental error. BLUPs across all environments were used for GWAS, linkage mapping, and GP analyses. Broad-sense heritability across all environments was calculated as follows:where i is the number of environments, and j is the number of replications in each environment. All of the factors were set as random effects when calculating heritability.where Young leaves of all the inbred lines and the parental lines were sampled for both populations. DNA extraction was performed using a CTAB method . Genotypr2) between the vectors of SNP alleles was used to assess the level of LD decay across each chromosome, and r2 = 0.1 was used as a cutoff. Population structure was conducted using the STRUCTURE V2.3.4 software (K) = 1–10, and number of iterations = 10. STRUCTURE HARVESTER (K (ΔK) value was used to determine the K value of the number of subgroups.After filtering, 187,409 SNPs were obtained for GWAS in the DTMA panel. The linkage disequilibrium (LD) analysis was carried out using TASSEL V5.0 with a sliding window size of 50 SNPs. A squared Pearson correlation coefficient  method  in GenomMaizeGDB . Within A similarity/linkage (SL) method was used for bin map construction with high-quality unimputed SNPs in the DH1 population, and the details were previously described by Genomic prediction analysis was conducted using the ridge regression best linear unbiased prediction (RRBLUP) model with the rrBLUP package  within tP < 0.01 in both populations, as well as the variance of genotype-by-environment interaction. The estimated broad-sense heritabilities in the DTMA panel and the DH1 population were 0.80 and 0.57, respectively.The descriptive statistics for the response to common rust in the DTMA panel and the DH1 population are presented in The basic information about GBS data before and after filtering is shown in r2 value of 0.1  plot implied that the population structure and family relatedness were well controlled in the GWAS using the FarmCPU method , encoding a UDP-glucose 6-dehydrogenase, contains the most significantly associated SNP of S1_278132829. The second most significantly associated SNP of S1_89238026 was located at the bin of 1.05, it had the second-lowest P-value of 9.81 × 10–10, and the MAF of this SNP was 0.32, with an additive effect of 0.13. It neighbored with the candidate gene of GRMZM2G114893 , which encodes a zinc finger (C2H2 type) family protein.Among all the six SNPs, the two most significantly associated SNPs were identified on chromosome 1. The most significantly associated SNP of S1_278132829 was located at the bin of 1.10, it had the lowest GRMZM2G144004 , encoding a putative uncharacterized protein. The SNP of S3_147594533 had a MAF of 0.11, with an additive effect of 0.15, and it was located at the candidate gene of GRMZM2G162250 , which encodes a Zea mays ARGOS6 (auxin-regulated gene involved in organ size) protein.On chromosome 3, two significantly associated SNPs were identified, i.e., S3_118933375 located at the bin of 3.04 and S3_147594533 located at the bin of 3.05. The SNP of S3_118933375 had a MAF of 0.10, with an additive effect of −0.17, and it was 587 bp away from the candidate gene of GRMZM2G138949 , encoding a BTB/POZ domain-containing protein. On chromosome 10, the significantly associated SNP of S10_95231291 was located at the bin of 10.04, it had a MAF of 0.10, with an additive effect of −0.16, and this SNP was closely linked with the candidate gene of GRMZM2G131536 .On chromosome 4, the significantly associated SNP of S4_183913302 was located at the bin of 4.08, it had a MAF of 0.17, with an additive effect of 0.13, and this SNP was close to the candidate gene of The linkage mapping results of the DH1 population are presented in The significantly associated SNP of S4_183913302 identified by GWAS was closely linked with the QTL detected in DH1 on chromosome 4, it was flanked by the markers S4_184936775 and S4_186039203, and this QTL had a LOD score of 4.62 and a PVE value of 6.79%. However, the most significantly associated SNP of S1_278132829 identified by GWAS was not validated by the linkage mapping analysis. The major QTL on chromosome 7 detected from linkage mapping analysis was also not validated by the GWAS result.The GP accuracies estimated based on GBS SNPs were 0.61 and 0.51 in the DTMA panel and the DH1 population, respectively . The GP P-value of 3.70 × 10–6. Linkage mapping identified five QTL accounting for the total PVE value of 40.67%. These results provided new insight into the quantitative resistance of common rust, which implied that major QTL associated with quantitative resistance from various elite backgrounds are possible to be pyramided for improving common rust resistance, and the selection for multiple partial resistance alleles seems to be more promising than the MAS of the Rp genes in tropical maize germplasm.Common rust is a major disease of maize, causing 34% of the maize area to suffer economic losses . DevelopIn the GWAS, six SNPs distributed in bins 1.05, 1.10, 3.04, 3.05, 4.08, and 10.04 were associated with common rust resistance. Except for SNP of S10_95231291, all the SNPs were reported in previous GWAS and linkage mapping studies . The mosIn DH1, linkage mapping revealed five QTL distributed in bins 1.03, 2.06, 4.08, 7.03, and 9.00, respectively. Three of the five QTL were reported previously . The locJoint GWAS and linkage mapping can complement the advantages and disadvantages of each method . In thisGRMZM2G328500 encodes a UDP-glucose 6-dehydrogenase, which is involved in the nucleotide-sugar interconversion process  family protein, which is mainly involved in the regulation of plant growth, development, and tolerance to biotic and abiotic stresses (GRMZM2G138949 identified in bin 4.08 encodes a BTB/POZ domain-containing protein, which participates in a series of physiological and biochemical reactions and also plays an important role in resistance to plant disease (The candidate gene analysis can lead to a better understanding of the genetic basis of common rust resistance. According to the results of GWAS, six candidate genes were identified in this study, and the function of four candidate genes was annotated. These candidate genes were previously reported to play important roles in the response of stress resilience and the regulation of plant growth and development.  process . GRMZM2Gstresses . GRMZM2G disease . These rGenomic prediction and GS have been successfully used in several crops to accelerate genetic gain in breeding programs for improving complex traits, including resistance to major maize diseases . A studyhttps://hdl.handle.net/11529/10548575.The original contributions presented in the study are publicly available. These data can be found at the CIMMYT Research Data & Software Repository Network: BP, MO, TD, FS, and XZ conceived and designed the experiments. TD, FS, and XZ coordinated the phenotyping. XZ, BP, and MO coordinated the genotyping. JR, ZL, PW, AZ, YL, GH, SC, JQ, and HZ analyzed the data. JR, ZL, FS, and XZ drafted the manuscript. JR, ZL, MO, BP, FS, and XZ interpreted the results. All authors contributed to the article and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Volatile aroma compounds found in grapes and hops may be present as both free volatiles and bound glycosides. Glycosides found in the raw materials are transferred to their respective fermented beverages during production where the odorless compounds may act as a reservoir of free volatiles that may be perceived by the consumer if hydrolyzed. A review of the literature on grape and wine glycosides and the emerging literature for glycosides in hops is presented in order to demonstrate the depth of history in grape glycoside research and may help direct new research on hop glycosides. Focus is brought to the presence of glycosides in the raw materials, the effect that winemaking and brewing have on glycoside levels, and current methods for the analysis of glycosidically linked aroma compounds. Glycosidically bound aroma molecules are common secondary metabolites that have been identified in dozens of different plant families . GenotypFragaria x ananassa (strawberries) [Glycosides have been identified in almost every type of plant tissue including leaves, roots, stems, and reproductive organs  . The dirberries) ,11,12. Iberries) ,8,13. Inberries) .Plant UGTs are believed to be cytoplasmic enzymes where both endogenous and exogenous small molecules may be glycosylated . After gVitis vinifera and Humulus lupulus have extensive glycosylation patterns that allow for the accumulation of glycosides in the plant tissues that can be released in their respective beverage products [V. vinifera berries and H. lupulus hop cones contain their own unique glycotransferases within the VvGT and HlUGT protein families [V. vinifera berries have been investigated over the last few decades, while research into H. lupulus glycosides and their effects on beer aroma and flavor is an emerging field [Through their association with aroma molecule aglycones, glycosides contribute to the aroma profile of fermented beverages such as wine and beer. Several cultivars of products ,20. V. vfamilies ,22,23,24ng field ,20,25. Tng field ,27,28,29Grapes and hops have been found to contain several classes of volatile molecule aglycones that include: monoterpene alcohols, monoterpene polyols, norisoprenoids, sesquiterpenoids (not reported in hops), aliphatic alcohols, and volatile phenols ,31,32,33The extent of investigations into the glycoside composition of hop cones has focused largely on monoterpene alcohols, compounds often seen as driving factors for the aroma of several hop varieties, and norisoprenoids, such as β-damascenone ,38,39,40While there are several different aglycone classes found in grape and hops glycosides, the glycosylation pattern of glycosides follows specific patterns. In general, the first sugar bound to an aglycone by a UGT enzyme is a glucopyranose ,22,41,42Saccharomyces yeast as they are used for the majority of wine and beer fermentations. The production of wine and beer products with non-Saccharomyces microbes has been organized into its own section due to the large scope of yeast and bacteria that can be introduced into grape juice or wort systems. Outlines of winemaking and brewing processes with respect to glycoside hydrolysis may be found in Both winemaking and brewing processes have several production steps that may influence the glycosidic profile in the finished beverage. Within this section, winemaking and brewing processes are discussed in separate sections with emphasis on the process itself in regard to glycosides. Microbial considerations for the winemaking and brewing sections are limited to The factors that contribute to glycoside hydrolysis during winemaking are part of a complex system that includes endogenous grape enzymes, microbial enzymes, and a low pH. Hydrolytic enzymes found naturally within the berries are largely ineffective towards the hydrolysis of aroma glycosides after grape processing . EvidencSaccharomyces strains have been widely researched in the literature, but there is no definitive answer as to the extent that Saccharomyces yeasts may contribute to the hydrolysis of glycosides in winemaking. Surveys of oenological Saccharomyces glycosidases indicated that the enzymes can hydrolyze glycosidic linkages in model juice and some grape juice systems [Saccharomyces are largely extracellular cell wall modification enzymes [EXG1 gene associated with hydrolysis resulted in higher levels of free terpenes when compared to the control in both model solutions and grape must [Saccharomyces have been shown to be capable of hydrolysis, the extent that different yeast strains are able to affect the pool of glycosides is unclear as only 1 of 153 different Saccharomyces cerevisiae strains was shown to have any recognizable β-glucosidase activity in model systems [Saccharomyces yeast, which have been shown to have glycoside hydrolyzing capacities [Fermentations also offer the opportunity for microbial glycosidase enzymes to be released into grape must where there is a large potential for hydrolysis to occur during fermentation. The hydrolytic capabilities of  systems ,56,57,58 enzymes ,59. Fermape must . However systems . Due to pacities . Still, pacities ,58,62,63Glycoside abundances due to yeast glycosidase activity have been shown to have the greatest changes during the active growth phases of fermentation ,64. HoweSaccharomyces strains were able to influence the overall monoterpenol profile without affecting the abundances of total glycosides [Saccharomyces cerevisiae [Investigations into monoterpenol glycosides have revealed that free monoterpenols in finished wines may not be fully derived from grape precursors as some studies have shown that oenological ycosides ,29,54,58revisiae ,89,90. Trevisiae ,92,93,94Investigations into the release of aglycones through the secondary fermentation of sparkling wines found that trends in free volatile content did not always correlate to direct additions of glycosylated aroma precursors to the wines. The reason for a lack of correlation between exogenous glycoside additions and the free volatiles were attributed to complex interactions with the wine matrix and biotransformation of the free volatiles during the fermentation process . BecauseTraditionally, hop glycosides were perceived to have minimal impact on the aroma profile of beer; however, changes in consumer preferences towards citrus and tropical aromas has increased the dosage rate of hop material in beers ,96. AddiHydrolysis of hops glycosides has been observed in beer production. Increases in both linalool and β-damascenone have been reported during the boiling and fermentation of wort containing Saaz, Tettnang, and Hersbrucker hops . HoweverIn modern day brewing practices, hops may be added at several places throughout the brewing process. Hops are traditionally added during the boiling stage, but hops can also be added at the conclusion of the boil, during the whirlpool stage, and before, during, or after fermentation. There is a lack of information about how glycosides may behave when hops are added at each of the previously listed times in the brewing process. Each stage offers its own unique conditions that could affect glycosidic content differently. For example, glycosides present during the boiling process of wort experience an acidic solution  and are held at boiling temperatures for prolonged periods of time ranging from as low as 45 min to upwards of 90 min. Hop-derived isoprenoids, most notably β-damascenone, have been shown to increase during the boiling stage of the brewing process . A separSaccharomyces yeast. The timing of hop additions is important with respect to hop-derived compounds, such as monoterpenes, as they interact with several metabolic pathways of Saccharomyces yeast [Although hops added during the boiling process may not directly impact beers in the forms of glycosides, hop additions that extend beyond the boiling stage of brewing have a potential for glycoside-related impact. Once the boiling of wort has concluded, it is still possible to add hops during the whirlpool stage, as well as before, during, or after the primary fermentation with es yeast . There ies yeast . Direct es yeast ,102,103.Saccharomyces brewing strains showed that the yeasts have minimal β-D-glucosidase activity during beer fermentation, but some strains expressed exo-1,3-β-glucanase activity that has the capability to hydrolyze glycosides [A screening of 58 ycosides . Kanauchycosides  later reycosides  by findiycosides  found thycosides ,83. MuchEven if glycosides are hydrolyzed during fermentation and the released aglycones are not lost due to volatilization, the presence of free aglycones during active parts of fermentation may have unpredictable effects on the flavor outcome, as some hop-derived monoterpenols are subject to biotransformation, e.g., transformation of geraniol to citronellol ,104,105.Saccharomyces microbes. Commonly used organisms with observed hydrolytic activity can include yeasts such as Hanseniaspora or Brettanomyces in addition to lactic acid bacteria such as Oenococcus and Lactobacillus [Saccharomyces fermentations show that there is a higher potential for glycoside hydrolysis, but the increased hydrolytic capability accompanies several considerations such as the differences in the nature of the hydrolytic enzymes used by non-Saccharomyces organisms and the unpredictable nature of non-Saccharomyces microbes. For example, malolactic fermentation in red wines was shown to decrease the total monoterpene glycoside content of red and white wines when compared to controls without always seeing a corresponding effect on the free monoterpene profile [Both beer and wine production have products that may undergo fermentation with non-bacillus ,68,69,70 profile ,72,73.Oenococcus oeni glycosidases as cell membrane proteins or cytoplasmic proteins [Oenococcus oeni have the hydrolytic capacity to release aglycones from each compound class listed by Caffrey et al. [Oenococcus oeni found that factors such as ethanol content, pH, temperature, and residual sugars all impact the hydrolytic capacity of the bacteria in different strain-dependent ways [It is believed that the increased hydrolytic capabilities of different microbes do not always translate to an increase in free volatiles due to the cellular locations of the enzymes . An overproteins ,107,108.y et al. ,78. Howey et al. ,72,77. Lent ways ,76.Saccharomyces yeasts is not as prevalent as conventional Saccharomyces fermentations, there is increasing interest in “natural” fermentations in the wine and beer industries. The emergence of wines made through spontaneous fermentation with microbes indigenous to the winery have been shown to allow for a higher hydrolytic rates of glycosides compared to fermentations inoculated with industrial strains [Brettanomyces that carry hydrolytic capabilities [Saccharomyces yeast and bacteria, since many of these organisms are considered spoilage microbes within conventional wines and beer styles.Although the use of non- strains ,79,80. Abilities ,68. AlthAlthough it has been known for almost four decades that grapes and wines contain aroma-related glycosides, comprehensive studies on their dynamic abundances in fermented products have been limited by analytical technologies ,110. MosOften times, studies forgo measuring glycosides and instead analyze the effects of different experimental conditions, such as fermentation temperature or yeast strain selection, on the abundances of free volatiles . The infIn the absence of glycoside measurements, aroma molecules bound to glycosides may initially go undetected only to resurface during consumption. For example, glycosides have been found to alter the retronasal aroma of wines through in-mouth hydrolysis within two minutes of ingestion, even though the wines had little glycoside-related orthonasal aroma ,121. BecRecent advances in ultrahigh performance liquid chromatography mass spectrometry (UHPLC/MS) have allowed for the direct measurement of glycosides. Direct measurements allow for more in-depth and detailed understanding of how glycoside profiles change over time during crop development or winemaking and brewing processes. However, the results are limited in scope due to a lack of analytical standards leaving identification of molecules tentative and determination of concentrations semi-quantitative ,34,35,43Vitis vinifera and Humulus lupulus. The glycosylated molecules are of increasing interest due to their connection with free volatile molecules in wine and beer. Comparatively, there is a rich history of knowledge concerning the presence of glycosides and their respective aromas in grapes and wine, while the field of research is starting to develop for hops and beer. A large focus of winemaking has shown that microbial activity during the fermentation process is a key event in the hydrolysis of glycosides. Although much work has shown that microbial activity can trigger the hydrolytic release of glycosides from wine, there is still more work needed to understand how to control the process. Brewing is a complex process that offers several opportunities for hydrolysis such as the boiling and fermentation stages; however, the impact of glycosides from hops is frequently brought into question due to their low abundances. The development of liquid chromatography mass spectrometry methods has allowed for more in-depth and faster analysis of intact glycosides in both plant materials and fermented beverages when compared to traditional hydrolysis methods. More development is still needed for these methods as it can be difficult to distinguish isomeric aroma glycosides from one another through mass spectrometric methods alone.Glycosylated aroma molecules are common metabolites found in the plant families


HBB) gene. Mass spectrometry of haemoglobin from animals with differing genotypes at this locus indicated that genotypes are associated with different forms of adult β-globin. Haemoglobin haplotypes have previously been associated with variation in several health-related traits in sheep and warrant further investigation regarding their role in tolerance to FE in sheep. We show a strategic approach to the identification of regions of importance for commercial breeding programs with a combination of discovery, statistical and biological validation. This study highlights the power of using increased density genotyping for the identification of influential genomic regions, combined with subsequent inclusion on lower density genotyping platforms.Facial eczema (FE) is a significant metabolic disease that affects New Zealand ruminants. Ingestion of the mycotoxin sporidesmin leads to liver and bile duct damage, which can result in photosensitisation, reduced productivity and death. Strategies used to manage the incidence and severity of the disease include breeding. In sheep, there is considerable genetic variation in the response to FE. A commercial testing program is available for ram breeders who aim to increase tolerance, determined by the concentration of the serum enzyme, gamma-glutamyltransferase 21 days after a measured sporidesmin challenge (GGT21). Genome-wide association studies were carried out to determine regions of the genome associated with GGT21. Two regions on chromosomes 15 and 24 are reported, which explain 5% and 1% of the phenotypic variance in the response to FE, respectively. The region on chromosome 15 contains the β-globin locus. Of the significant SNPs in the region, one is a missense variant within the haemoglobin subunit β ( Pseudopithomyces chartarum (formerly Pithomyces chartarum) [Pse. chartarum grows on the dead leaf matter at the base of pastures, and ingestion of the toxic fungal spores by ruminants leads to liver inflammation and bile duct blockage [Pse. chartarum in New Zealand, resulting in outbreaks of FE in currently unaffected regions [Facial eczema (FE) is a metabolic disease of major welfare concern and economic loss to farmers in New Zealand. The disease is caused by ingestion of the mycotoxin sporidesmin, which is found in the spores of the fungus artarum) ,2. Pse. blockage . While iblockage , productblockage  and loweblockage ,7,8 in bblockage ,10). Cliblockage . Althougblockage , South Ablockage , North ablockage ,15, Euroblockage  and Chinblockage . Outbreablockage ,19,20, a regions . 2) of 0.42 ± 0.09 [Current strategies for preventing the severity of FE include protecting animals through dosing with zinc compounds , sprayin2 ± 0.09 . Selecti2 ± 0.09 . A live-2 ± 0.09 , who sho2 ± 0.09 . A comme2 ± 0.09 . The res2 ± 0.09 .www.sheephapmap.org), facilitated significant increases in the number of genotyped animals, driving the development of genomic selection [Several approaches have been used to search for causative genes or loci underlying FE tolerance, including a candidate gene approach ,32, genoelection . As a reThe objective of our study was to determine genomic variants associated with FE in New Zealand sheep, through a combination of discovery, statistical and biological validation using cost-effective lower density genotyping platforms. www.sil.co.nz) database. The Ramguard™ programme [Pse. chartarum that is cultured in a laboratory to produce the mycotoxin sporidesmin. Animals are dosed with precise amounts of sporidesmin  of sporidesmin A) by intra-ruminal intubation at a volume that is dependent on the animal’s live weight (mg per kg live weight). Blood samples are taken for GGT testing before dosing (for a base level) and at 21 days after dosing and are processed through a commercial analytical laboratory. Sheep were selected for inclusion based on GGT21 records and genotype availability. All animals were from either industry or progeny test flocks and had been tested through the Ramguard™ programme. All data were obtained from the Sheep Improvement Limited  or the Illumina OvineSNP50 BeadChip  according to the manufacturer’s protocol. The 2532 animals genotyped using the 50K chip were a subset of the animals used to develop genomic selection for FE, as described in Phua et al. [The initial dataset consisted of 2669 animals with GGT21 records from 45 flocks, born between 1992 and 2012. Animals were either one of the main dual-purpose sheep breeds used in New Zealand, including Romney, Coopworth and Perendale, or a composite of dual-purpose breeds. Individuals had been genotyped with either the Ovine Infiniuma et al. .p < 1.04 × 10−7) from the initial ‘discovery’ analyses . Qualitn = 616), Perendale (n = 207) and Primera (n = 149) genotypes were used to calculate breed-specific imputation accuracies. Imputation accuracy was calculated using sets of 100 animals from each breed against the larger reference population, with HD genotypes masked, and the remaining 50k genotypes imputed to full density. Accuracy was then calculated for each dataset. Additionally, for the Romney animals within and across breed accuracy was also calculated, where 100 animals were imputed against a random sample of 500 of the remaining 516 purebred Romney, or 500 other animals comprised of the Coopworth, Corriedale, Perendale, Texel and Wiltshire breeds. To account for biases in imputation accuracy for SNP with low MAF, the correction proposed by Hayes et al. [For the 2532 animals genotyped using the 50K chip, imputation from 50K to HD genotypes for the discovery analyses was performed using Fimpute (v2.0) . The imps et al.  was applwww.goldenhelix.com (accessed on 20 March 2021)) using two methods. Firstly, an Efficient Mixed-Model Association eXpedited (EMMAX) analysis using a kinship matrix was performed on log-transformed serum GGT levels at 21 days after a measured sporidesmin challenge (GGT21). Contemporary group  was 1.04 × 10−7 and 3.39 × 10−6 for discovery and validation analyses, respectively.Both the discovery and validation genome-wide association studies (GWAS) was performed within SNP & Variation Suite v8.4.0  and the and mob  after fia = (AA − BB)/2 and d = AB − ((AA + BB)/2)), where AA, BB and AB are the predicted trait values for each genotype class. The proportion of additive genetic variance due to each SNP was estimated as (2pq (a + d(q − p))2)/AV, where p and q are the allelic frequencies at the SNP locus, and AV is the total additive genetic variance of the trait obtained when no SNP fixed effects are included in the model. To calculate the proportion of variance explained by each marker, SNP effects were estimated in ASReml by fitting the SNP as a fixed effect along with the contemporary group. The predicted trait values were converted to genotypic effects as follows n = 50 iterations) within SNP & Variation Suite v8.4.0 for genomic best linear unbiased prediction (GBLUP). Only animals that had been genotyped on either the Ovine Infinium® HD SNP BeadChip or the Illumina OvineLD BeadChip (n = 2999) were used for the analysis, and fixed effect coefficients included contemporary group. K-fold cross-validation was run including all markers , and after removing markers in the regions of interest on chromosomes 15 and 24 . Accuracies were calculated by dividing the Pearson product-moment correlation coefficient by the square root of the heritability.To quantify the predictive ability of markers of interest, 5-fold cross-validation was performed with random sampling , blood was collected from 12 animals with different genotypes in the β-globin locus, using SNPs rs405755938 and rs402069107 . ProteomMAF), was consistent among the datasets tested. Romney, which was the largest of the purebred groups (n = 616) had a marginally higher accuracy of 0.987, compared to the smaller group of purebred Perendale . Primera (n = 149), which can be considered a composite breed and therefore more closely reflects the entire reference set, had the highest imputation accuracy of 0.989. When the 100 Romney animals were imputed against a reference of 500 of the remaining Romney, or 500 purebred animals (excluding Romney and composite breeds), IAMAF reduced to 0.962 and 0.932, respectively.Imputation accuracy, corrected for MAF  for additive GWAS analysis . In both® HD SNP BeadChip or the Illumina OvineLD BeadChip  were used to repeat the EMMAX analysis (2) between markers (Low-density genotyping (Illumina OvineLD BeadChip) of additional animals with FE phenotypes confirmed the QTL on chromosome 15, using both EMMAX and association analyses C,D. The analysis A, calcul markers B and all markers C.A total of 11 SNPs, all on chromosome 15, reached genome-wide significance in all analyses . In addiHBB gene, with an A > G change at position 359 of the coding sequence resulting in a change from Asparagine (N) to serine (S) at amino acid 120. In sheep, the repetitive β-globin locus has two different haplotypes, the longer haplotype A and the shorter haplotype B. While both haplotypes A and B contain the adult β-globin gene, HBB, the two are considered allelic variants (βA and βB) [The significant region on chromosome 15  ,43 diffe and βB) ,45,46. V and βB) . There wA). At both positions, the predicted GGT21 values from ASReml were lower for the haplotype A animals  at the β-globin locus predicted β-globin peptides  for 11 of the 12 animals, with one animal, predicted to be Hb-B, 5745, also showing polymorphism two of the seven variable peptides . Two QTL on chromosomes 15 and 24, were discovered using imputed high-density genotypes. The significant SNPs were subsequently included on a bespoke low-density ovine SNP chip for validation using industry-phenotyped animals. This study highlights the power of using increased density genotyping for the identification of influential genomic regions. SNPs from the Illumina OvineSNP50 BeadChip were not associated with the QTL on chromosome 15, likely due to the high levels of diversity in the breeds represented in this study, and the resulting reduced linkage disequilibrium between markers . When thThe 13 SNPs examined had largely additive effects, although there was some evidence for dominance. If these genotypes are also used in genomic selection, they may increase breeding value accuracy, although this is largely captured by GBLUP. Given the subclinical manifestation of the disease, further work is needed to understand the impact of these effects on the phenotype.p < 1.04 × 10−7) on chromosome 24 from the analysis using the high-density genotypes is approximately 230 Kb. Of the SNPs that reached significance in the high-density SNP analysis, two are missense variants within the PTX4 gene. PTX4 is reported to be widely expressed at low levels, with the highest levels in the liver in mice, and the small intestine and testes in humans [PTX4 belongs to the pentraxin (PTX) superfamily of multifunctional conserved proteins; some PTXs are part of the humoral arm of innate immunity and behave as functional ancestors of antibodies by mediating agglutination and complement activation, however, PTX4 mRNA expression is distinct from other PTX family members [PTX4 in innate immunity [The significant region , pre-adult (βC) and the adult (βA) [F and βA, with the foetal cluster containing an additional β-like pseudogene [C. While both haplotypes A and B contain the adult β-globin gene, HBB, the two are considered allelic variants (βA and βB) [2β2A) and B  is distinguishable electrophoretically [The significant region on chromosome 15 contains the β-globin locus. In ruminants, the β-globin locus was formed by the duplication of an ancestral four-gene set, ε-ε-ψ-β, consisting of two embryonic-like genes (ε), a pseudogene (ψ) and a β (β) globin gene . Goats hult (βA) . In catteudogene . In sheeeudogene ,43,55,56 and βB) ,43 diffe and βB) ,45,46. Hetically .The OARv3.1 (GCA_000298735.2) sheep genome contains the shorter haplotype B , either 2β2A) to the juvenile haemoglobin C  in response to hypoxia [Ovis ammon), suggesting the importance of the locus for sheep living in high altitude and low oxygen environments [Various studies have shown variation in the frequency of haemoglobin A and B, indicating that there are advantages to both types . Hb-A ha hypoxia ,64, anae hypoxia ,66,67 or hypoxia . It has  hypoxia . The β-gronments .B) appeared to also have a shortened form of the βA-globin peptide. It appears that haplotype A  animals are more tolerant to FE. The overall frequency of haplotype A in the animals genotyped in this study, however, is 0.5, indicating that the locus may be under balancing selection in these populations, and warrants further investigation. In crossbred ewes, Hb-A has been associated with beneficial effects for several health-related traits, including mastitis and parasite resistance, whereas Hb-B was associated with increased fertility [Nathercium ossifragum than Hb-B animals [Tribulus terrestris in South Africa. While the pathogenetic mechanisms underlying geeldikkop and FE differ, both affect the biliary tree, resulting in the accumulation of phylloerythrin [Our results indicate that the β-globin locus haplotype, and therefore ovine haemoglobin type, can be estimated using SNPs within the β-globin locus, although one animal that was hypothesised to be homozygous for haplotype B  between the resistant and susceptible line, however, there was no evidence of linkage in outcross pedigrees, and it was concluded that the effect of catalase was probably recessive or minor [The underlying role of haemoglobin type in the response to FE is not straightforward. The mechanism of toxicity of sporidesmin, the causative mycotoxin, is believed to be through the generation of reactive oxygen species including superoxide radicals, leading to liver damage . Other mll lines . Antioxill lines . Analysior minor . Of inteWhile the frequency of the haemoglobin haplotypes in the FE selection lines is unknown, in an analysis of 120 New Zealand Romney ewes of known haemoglobin type, Hb-B sheep had significantly higher levels of plasma copper, whole-blood selenium, and erythrocytic GPx than Hb-A sheep . PreviouTwo loci for tolerance to facial eczema in sheep are reported on chromosomes 15 and 24. Markers at each locus explain 5% and 1% of the phenotypic variance in GGT21 levels after a sporidesmin challenge, respectively. The significant region on chromosome 15 overlaps the β-globin locus; mass spectrometry of haemoglobin from animals with differing genotypes in this region indicated that genotypes are associated with different forms of adult β-globin. Haplotype A animals appear to be more tolerant to FE. Given the importance of the β-globin region and observed intermediate haplotype frequencies of the animals in this study, the region may be under balancing selection. Further work is needed to explore this in a larger dataset. Haemoglobin haplotypes have previously been associated with variation in several health-related traits in sheep, and therefore warrant further investigation regarding their role in tolerance to facial eczema in sheep. This study highlights the power of using increased density genotyping for the identification of influential genomic regions, combined with subsequent inclusion on lower density genotyping platforms for rapid validation and industry uptake.


Solanum tuberosum L.) and sweetpotato (Ipomoea batatas L.), which are nutritionally and commercially important tuberous crops, possess a perplexing heredity because of their autopolyploid genomes. To reduce cross‐breeding efforts for selecting superior cultivars from progenies with innumerable combinations of traits, DNA markers tightly linked to agronomical traits are required. To develop DNA markers, we developed a method for quantitative trait loci (QTL) mapping using whole‐genome next‐generation sequencing (NGS) in autopolyploid crops. To apply the NGS‐based bulked segregant method, QTL‐seq was modified. (1) Single parent‐specific simplex (unique for one homologous chromosome) single‐nucleotide polymorphisms (SNPs), which present a simple segregation ratio in the progenies, were exploited by filtering SNPs by SNP index . (2) Clusters of SNPs, which were inherited unevenly between bulked progenies with opposite phenotypes, especially those with an SNP index of 0 for the bulk that did not display the phenotypes of interest, were explored. These modifications allowed for separate tracking of alleles located on each of the multiple homologous chromosomes. By applying this method, clusters of SNPs linked to the potato cyst nematode resistance H1 gene and storage root anthocyanin (AN) content were identified in tetraploid potato and hexaploid sweetpotato, respectively, and completely linked DNA markers were developed at the site of the presented SNPs. Thus, polyploid QTL‐seq is a versatile method that is free from specialized manipulation for sequencing and construction of elaborate linkage maps and facilitates rapid development of tightly linked DNA markers in autopolyploid crops, such as potato and sweetpotato.Potato ( Solanum tuberosum L.) and sweetpotato (Ipomoea batatas L.) are important crops not only for serving as staple foods in developing countries but also in a variety of cuisines worldwide. However, the clonal propagation of these crops occasionally spreads their pathogens and pests rapidly, leading to devastating damage to their production areas. Therefore, breeders are confronting pressure to continuously develop new cultivars with enhanced resistance to them  for each of 12 chromosomes], without considering crossovers among homologous chromosomes. Assuming that each individual has a different combination of chromosomes, a few hectares of paddy fields are sufficient for planting all combinations. In contrast, the number of combinations of tetraploid potato and hexaploid sweetpotato F1 progenies after crossing is 4.7 × 1018 and 1.1 × 1039, which were calculated by (4C2 × 4C2)12 and (6C3 × 6C3)15, respectively, where nCr means combination in case that r chromosomes were chosen from n different homologous chromosomes of one parent and transmitted to progenies. Those would require thousands of the earth’s surfaces to cultivate all the combinations. Therefore, DNA marker‐assisted selection is imperative in selecting superior individuals from such innumerable combinations efficiently before planting to fields.Considering the breeding of these tuberous crops, their heterologous autopolyploidy, in which each homologous chromosome has different origin by outcross with different sequences, poses an inevitable issue of complicated heredity. In the case of a diploid crop, rice, the combination of 12 chromosomes in the FH1, are widely incorporated in current breeding programmes  or duplex:simplex:nulliplex = 1:2:1 (in cases where both parents have the same nucleotide simplex). Therefore, accurate discrimination of simplex polymorphism is a prerequisite for mapping QTLs in crops with polyploid genomes. To accurately distinguish simplex from duplex, many sequence reads are necessary for each SNP site. RenSeq/GenSeq and RAD‐seq, which provide extensive sequencing of selected regions of the genome, generate a large number of reads for the selected SNP sites, allowing discrimination of simplex with sufficient accuracy. However, they fail to detect a large proportion of SNPs, which omits them for sequencing. In contrast, the QTL‐seq method developed by Takagi et al.  is intenH1 gene in potato and tuber AN content in sweetpotato as model cases. We propose the polyploid QTL‐seq method as a versatile tool for rapid development of tightly linked DNA markers.Recently, the cost of whole‐genome sequencing has been tremendously reduced, and the performance of computers continuously increases. Therefore, analyses with massive data of whole‐genome sequences are feasible as was the case of an allopolyploid crop, peanut  required. In tetraploid species such as potato, the frequencies of simplex, duplex and triplex SNPs were selected and read by random shotgun sequencing, namely SNP indices, converged to 0.25, 0.50 and 0.75, respectively, as read depth increased. Simulation of 10,000 replications revealed that a depth of 40 is sufficient to distinguish simplex from duplex with 95% confidence by extracting SNPs with SNP index ranges of 0.10 to 0.36 Figure . In hexaFollowing the above simulation, the QTL‐seq algorithm was modified with three filtering strategies to adapt to the polyploid genome Figure . QTL‐seqh1 and sweetpotato low AN bulks in this study), were counted on the list of P2‐specific simplex SNPs, and their distribution throughout the genome is shown as a histogram.Assuming that the trait of interest is regulated by an allele located on a single homologous chromosome, SNPs presenting linkage to the phenotype are limited to those located on the chromosome where the causal allele resides , corresponding to 49.2, 50.6, 47.9 and 48.8 times the coverage of the potato genome, respectively, yielded 100 240 simplex SNPs that were specific for the H1‐harbouring cultivar, Sayaka. Polyploid QTL‐seq with the simplex SNPs detected an increased number of SNPs that were skewed over the 95% statistical confidence in the region of chromosome 5, 47–54 Mb  was manifest around 51.9–52.8 Mb , and they were located sparsely throughout the genome , the polyploid QTL‐seq procedure was used to detect a trait‐controlling locus efficiently in the tetraploid crop, potato.As proof of principle, the above polyploid QTL‐seq algorithm was validated with a tetraploid potato FMb Table . Meanwhie Figure , chromose Figure , confirmH1 and h1 bulks as well as those of both parents were reduced by half (52.5 Gb), the H1 locus could be identified ˜47–54 Mb of chromosome 5  decreased the number of available Sayaka‐specific simplex SNPs to 33 609, approximately one third of the number in the original analysis with full reads, and the candidate peak on chromosome 5 was dampened to a level indistinguishable from other background peaks. Therefore, at least 50 Gb reads are necessary to detect the H1 locus in potato.To reduce the sequencing cost for polyploid QTL‐seq, the effect of reduction in input read numbers on the efficiency of QTL detection was examined. As the numbers of parent and bulk reads were reduced to a half (52.5 Gb) and a quarter (26.25 Gb) of the original analysis, the thresholds of the read depth to select simplex SNPs were decreased from the original parameter, ≥40, to ≥25 and ≥15 to retain a large proportion of SNPs available for the analysis, respectively. Even though the reads of 5 Figure , althoug1 progenies were subjected to quantification of AN content  and low to undetectable levels (A530 < 0.5), respectively. In the present study, QTL‐seq was conducted with the AN content‐segregating F1 population. In this crossing, cv. Akemurasaki is known as a donor of the AN‐accumulating phenotype; thus, Akemurasaki reads were mapped against the reference augmented with SNPs from cv. Konaishin. Mapping with 103.1 Gb, 97.7 Gb, 100.0 Gb and 114.4 Gb reads of Akemurasaki, Konaishin, high AN bulk and low AN bulk , corresponding to 50.3, 52.3, 52.5 and 52.5 times the coverage of the sweetpotato genome, respectively, yielded 15 430 Akemurasaki‐specific simplex SNPs. Although polyploid QTL‐seq with the simplex SNPs gave no SNPs beyond the thresholds of 95% statistical confidence, a portion of SNPs in the region of chromosome 12, 16–23 Mb presented SNP indices close to 0 for the low AN bulk read, and they skewed over 90% statistical confidence, whereas that of the high AN bulk read gave no such clustered SNPs with skewed distribution , compared to those detected with Konaishin‐specific simplex SNPs (5) , the polyploid QTL‐seq procedure was confirmed to detect a trait‐controlling locus efficiently also in the hexaploid crop, sweetpotato.The adaptability of polyploid QTL‐seq to crops with more complex genomes was verified using hexaploid and sweetpotato. In a previous study, a purple‐fleshed cultivar, Akemurasaki, was crossed with a beige‐fleshed cultivar, Konaishin, and the storage roots of their Fn Figure . Since nn Figure , Chromosn Figure , confirmH1 locus in potato and storage root AN content‐controlling locus in sweetpotato, linked PCR markers were created at SNP sites, where SNP indices were 0 for the h1 or low AN bulks  resulted in loss of amplification. Thus, the genotypes of the Chr12:20608324/G marker completely matched with high AN content as well as purplish root colour.The sweetpotato storage root AN content‐linked marker, Chr12:20608324/G, was used to detect the Akemurasaki‐specific C to G SNP, which is located on 20 608 324 bp of chromosome 12. Although control amplification by a n Figure . When 94Accordingly, in both cases, completely linked DNA markers were created by exploiting SNPs in the region suggested by polyploid QTL‐seq.H1 and storage root AN content were identified in potato and sweetpotato, respectively . Therefore, it was revealed that the linked marker was created fairly in the vicinity of H1. For sweetpotato storage root AN content, the completely linked marker was generated at chromosome 12, 20 608 324 bp, when the alignment was performed with the publicly available I. trifida v3 (NSP306) reference genome. AN biosynthesis in storage roots has been reported to be regulated by the Myb transcription factor IbMYB1 , its closest homologues, itf12g04080.t1 and itf12g04100.t1 , were located on chromosome 12, 2.4–2.5 Mb in the I. trifida v3 (NSP306) reference genome, which is distant from the QTL detected in the present study. Several other genes encoding Myb domain proteins reside in the QTL candidate region, chromosome 12, 16–24 Mb. An Myb‐related gene or an unidentified gene might be the causative gene for AN content. Alternatively, MYB1 might not exist in the genome of I. trifida, which does not accumulate AN in the root; thus, it would not be included within the candidate region of the reference genome.In the present study, a new method, designated polyploid QTL‐seq, was established to identify SNPs tightly linked to agronomic traits in autopolyploid crops such as potato and sweetpotato by whole‐genome sequencing. The method is based on a bulked segregant analytical method, QTL‐seq , was developed  Although a reference genome is necessary, complicated procedures for the preparation of sequencing libraries for hundreds of progenies and the construction of elaborate genetic linkage maps are not required. Therefore, polyploid QTL‐seq can be easily applied to new progenies developed by crossing unexplored materials. (2) Alignment with whole‐genome sequence reads yields profiles of up to 100 240 and 15 430 simplex SNPs throughout the genome without any biased selection for the present potato and sweetpotato materials, which enables the creation of tightly linked DNA markers in the vicinity of the causative allele. (3) Since many linked SNPs are present in a given candidate region, there would be a choice of several SNPs for the arrangement of DNA markers. Thus, even if cultivars other than those used to establish DNA markers are subjected to breeding and do not show polymorphism for the SNP used as the DNA marker, it would be possible to exploit the other neighbouring SNPs for preparation of available DNA markers, as long as the same donor parent is used.In conclusion, polyploid QTL‐seq is a versatile method that is free from specialized manipulation for sequencing and the construction of elaborate linkage maps and enables rapid development of tightly linked DNA markers in the autopolyploid crops, potato and sweetpotato. This method might be applicable to other autopolyploid crops such as blueberries, sugarcane and roses. In the present study, tightly linked DNA markers were created in cases of traits regulated by a single locus. However, the ability to detect QTLs in cases of multiple gene‐regulated traits remains to be verified. Further validation with agronomically important traits, such as disease resistance and tuber quality, which are thought to be under the control of multiple genes, is anticipated.H1‐segragating populations were generated by crossing S. tuberosum cv. Hokkaikogane , and cv. Sayaka , herein designated as P1 and P2, respectively. F1 seeds were sown on horticultural soil  and grown in an air‐conditioned greenhouse. The leaves of 82 progenies and their parents were used for the extraction of genomic DNA.Potato 1 progenies generated by crosses of I. batatas cv. Konaishin  and cv. Akemurasaki , herein designated P1 and P2, respectively, were grown in plastic pots in a greenhouse. The storage roots of 94 progenies and their parents were harvested, and their AN content was determined as reported previously  containing 1× GoTaq Colorless Master Mix , 10 ng of genomic DNA, and 3 pmol each of forward and reverse primers of H1‐N146, H1‐N195 and GBSS   genotypes, bulked DNA samples were prepared.Potato genomic DNA was extracted using a DNeasy Plant Mini Kit . To detect the l) Table  were use1 individuals with high (7.44 to 15.43 absorbance at 530 nm) and low (0.11 to 0.25 absorbance at 530 nm) AN content according to AN quantity determined in a previous study . By combining an equal amount of DNA from each of 22 FThe sequencing library was constructed using a TruSeq DNA PCR‐Free Sample Preparation Kit  and subjected to 150 bp paired end sequencing using an Illumina NovaSeq6000 DNA sequencer. The FASTQ sequence data are available from the DDBJ Sequence Read Archive under the following accession numbers: DRA011095 and DRA011096 for potato, and DRA011381 for sweetpotato.http://genome‐e.ibrc.or.jp/home/bioinformatics‐team/mutmap), using an iMac Pro computer equipped with 36‐thread Intel Xeon W processor, 128 GB RAM, 4 TB HDD. The following programmes were installed to run the pipeline: Perl v5.28.2, R version 4.0.2, BWA version 0.7.17‐r1188, SAMtools version 0.1.8 and FASTX Toolkit version 0.0.14. The schematics are shown in Figure S. tuberosum group Phureja DM 1‐3 v6.1   of SNPs linked to the phenotype of interest, SNPs whose SNP indices of the To evaluate the efficacy of the developed method, the same analysis was conducted with P1 and P2 parents exchanged, and a chi‐square test was performed for the number of SNPs presented as candidates.H1‐linked DNA marker, PCR mixtures  containing 1× GoTaq Colorless Master Mix, 10 ng of genomic DNA of each F1 individual, and 3 pmol each of forward and reverse primers of Chr5:52358101/T and GBSS   Table  were useThe FASTQ sequence data are available from the DDBJ Sequence Read Archive under accession numbers DRA011095, DRA011096 and DRA011381.The authors declare no conflict of interests.1 population and quantified the AN content in their storage roots. KA, ES, KA, SO, KK and ST created the potato F1 population. HY conducted polyploid QTL‐seq and linked DNA marker analyses and wrote the manuscript.HY and HT conceived the research project and designed the experiments. EH and MT created the sweetpotato FFigure S1 Simulation test for obtaining a 95% confidence interval assuming simplex SNPs.Figure S2 Plots of SNP index, ΔSNP index and ratio of SNPs out of confidence intervals generated by polyploid QTL‐seq analysis with an H1‐segregating potato F1 population.Figure S3 Cluster plot of polyploid QTL‐seq analysis using the Sayaka reference sequence and Hokkaikogane‐specific simplex SNPs.Figure S4 Identification of a genomic region corresponding to the potato H1 locus by cluster analysis using polyploid QTL‐seq data with a reduced number of reads.Figure S5 Plots of SNP index, ΔSNP index, and ratio of SNPs out of confidence intervals generated by polyploid QTL‐seq analysis with the AN‐segregating sweetpotato F1 population.Figure S6 Cluster plot of QTL‐seq analysis using the Akemurasaki reference sequence and Konaishin‐specific simplex SNPs.Click here for additional data file.Table S1 Sayaka‐specific simplex SNPs identified by the polyploid QTL‐seq analysis of potato H1‐segregating progeny.Table S2 Akemurasaki‐specific simplex SNPs identified by the polyploid QTL‐seq analysis of sweetpotato AN‐segregating progeny.Table S3 Primers used in the present study.Click here for additional data file.


MYOG, CHI3L1, CHIT1 (BTA16), TIMELESS, APOF, OR10P1, OR6C4, OR2AP1, OR6C2, OR6C68, CACNG2 (BTA5), COL5A2 and COL3A1 (BTA2)). In all breeds, we found the largest proportion of homozygous by descent segments to be those that represent inbreeding events that occurred around 32 generations ago, with Tuscan breeds also having a significant proportion of segments relating to more recent inbreeding.In the present study, GeneSeek GGP-LDv4 33k single nucleotide polymorphism chip was used to detect runs of homozygosity (ROH) in eight Italian beef cattle breeds, six breeds with distribution limited to Tuscany  or Sardinia  and two cosmopolitan breeds (Charolais and Limousine). ROH detection analyses were used to estimate autozygosity and inbreeding and to identify genomic regions with high frequency of ROH, which might reflect selection signatures. Comparative analysis among breeds revealed differences in length and distribution of ROH and inbreeding levels. The Charolais, Limousine, Sarda, and Sardo Bruna breeds were found to have a high frequency of short ROH (~ 15.000); Calvana and Mucca Pisana presented also runs longer than 16 Mbp. The highest level of average genomic inbreeding was observed in Tuscan breeds, around 0.3, while Sardinian and cosmopolitan breeds showed values around 0.2. The population structure and genetic distances were analyzed through principal component and multidimensional scaling analyses, and resulted in a clear separation among the breeds, with clusters related to productive purposes. The frequency of ROH occurrence revealed eight breed-specific genomic regions where genes of potential selective and conservative interest are located (e.g.  Runs of homozygosity (ROH) consist of contiguous regions of the genome where an individual is homozygous in all sites . This ocThe inbreeding coefficient of an individual (F) is defined as the probability that two randomly chosen alleles at a specific locus within an individual are identical by descent (IBD) . HomozygROH) has been considered a powerful and accurate method of detecting inbreeding effects [The estimation of the inbreeding coefficient from the proportion of the genome covered by ROH  for each population, which links the genomic homozygous segments to the time of living of the most recent common ancestor, is the Homozygous-Identical by Descent (HBD) state probabilities. Druet and Gautier [However, the use of ROH as markers for the identification of genomic regions potentially subjected to non-recent evolutionary events is not straightforward. It requires that homozygous segments have been inherited from old ancestors and were not caused by recent demographic events . A furth Gautier  presente Gautier  implemen Gautier .Assessment of genetic diversity and population structure is an important task to understand the evolutionary history of the breeds, but also to provide important information for the conservation and management of biodiversity . Italy hROH) within breeds were calculated; the HDB state probabilities have been used to estimate global inbreeding (FG) and to investigate its change across generations, in order to describe the demographic history of the populations.The aim of this study was to assess genome-wide autozygosity in eight Italian beef breeds, six at critical risk of extinction, namely Calvana (CAL), Mucca Pisana (MUP), Pontremolese (PON), mainly reared in Tuscany, Sardo Bruna (SAB), Sardo Modicana (SAM), Sarda (SAR) reared in Sardinia. The cosmopolitan breeds, i.e. Charolais (CHA) and Limousine (LIM), were included in the analysis to compare results and to highlight the differences between local breeds and two of the most widespread breeds reared in Italy. ROH distribution and characterization have been investigated across the genome, and consequently, the inbreeding coefficients  single nucleotide polymorphism (SNP) DNA chip. Sampled animals for the three Tuscan breeds were 179, 190 and 45 for CAL, MUP and PON, respectively. The limited number of alive animals of these breeds restricted the total samples. Also, for SAM, being at risk of extinction, only 101 genotypes have been recovered. Samples of SAR (n = 199) and SAB (n = 194) were animals born from 2005 and 2000, respectively. CHA and LIM samples consisted of cattle born from 2015 to 2019 (200 samples for each breed). Genotype quality control and data filtering were performed with PLINK v1.9  and weremds-plot flag, which were estimated on the matrix of genome wide pairwise Identical by State (IBS) distances [A multidimensional scaling plot analysis (MDS) was performed to investigate the population structure between the eight breeds based on genetic distances. The first three dimensions were obtained with PLINK v1.9  using thistances . Resultsistances .detectRUNS v. 0.9.5 [Analysis of runs of homozygosity was conducted with the R package v. 0.9.5 . The folv. 0.9.5 .A principal component analysis (PCA) was conducted on the number of ROH per chromosome for each breed, to infer the similarities between populations based on ROH chromosomic distribution. All ROH were classified into five classes of length as suggested by Kirin et al. , and FerROH) was calculated as suggested by McQuillan et al. [The genomic inbreeding  of NCBI  to identify genes. The UMD 3.1 assembly was used for mapping.In order to investigate the selection signatures in the eight cattle breeds, the occurrences of ROH across genome was explored. The SNPs frequencies (%) in detected ROH were evaluated for each breed and plotted against the position of the SNP across autosomes. The threshold considered was the 80% of ROH occurrence for each breed, which were filtered taking only the genomic regions containing a minimum number of 15 SNPs. These genomic regions were analyzed and overlapped to Genome Data Viewer (RZooROH R package [G) calculation. Each class (K) has its own rate parameter, RK, which indicates the length of the segments for its respective class. The length of HBD classes is exponentially distributed with rate RK, which is double the number of generations to the common ancestor of the respective class. The length of the HBD segment is 1/RK Morgans, indicating high rates associated with shorter segments. The study focused on <16 Mbp ROH length, so the model applied was six HBD classes with respective rates  and one non-HBD with an RK rate of 26, so that 32 generations (generation = R/2) and short HBD segments from 1.5 Mbp (1/26) of length were included in the analysis. The rate of the non-HBD class was fixed as the most ancient class.The hidden Markov model (HMM)-based approach was used to scan the individual genome for the HBD segments as described in Solé et al. . The ana package . The HBDK rate higher than a threshold T as unrelated. The corresponding genomic inbreeding coefficient (FG−T) was then estimated, with RK ≤ T averaged over the whole genome .In total, 28,178 SNPs were divided into 5 classes of MAF  while 11In order to investigate the presence of common SNPs between breeds, pairs comparisons have been performed. The monomorphic SNPs distribution across autosomes has been investigated and it is showed in The MDS plot evidenced clustering of breeds .MUP and CAL were extremely distant from each other and from the other six breeds, which grouped in a unique large cluster. Only a small group of MUP samples were nearer to the third cluster (Sardinian and Cosmopolitan breeds). Both LIM and CHA showed extremely compact clusters, suggesting as expected, a low genetic variability within each breed, and also close to each other, underlining their different genetic background compared to local breeds. SAM and SAB individuals were more scattered than SAR and PON which, however, overlapped with these former.Bos taurus autosome (BTA) 1 had consistently the highest ROH number in all breeds, except for MUP, where BTA2 had the highest number of ROH. The average length was found to be higher in Tuscan breeds (ranging from 3.06 to 3.68 Mbp). The Sardinian breeds had intermediate values (2.20–2.24 Mbp) while the average ROH length for the cosmopolitan breeds was 2.14 Mbp for CHA and 2.09 Mbp for LIM. PCA analysis on the number of ROH per chromosome revealed groups among breeds , whereas SAM and PON showed the lowest values, 22,758 and 10,044, respectively. However, the average number per individuals was almost the same in each breed, ranging from 224 in SAB to 266 in SAR. For each chromosome the number of ROH per breed  has beeng breeds . PC1 cleFive classes of length were considered in order to investigate the ROH pattern. The ROH distribution by length and number for each breed were reported in The majority of ROH detected belonged to the first two classes (<2 and 2–4 Mbp) for all breeds. LIM and SAR had ~34,000 ROH with length less than 2 Mbp, followed by CHA and SAB with ~32,000. PON and SAM were the two breeds with a lower number of short ROH detected . The second class of ROH length (2–4 Mbp) maintained similar pattern, with LIM, CHA, SAB and SAR having the higher number of ROH. Regarding the classes of longer length (>4Mbp), CAL and MUP had the highest number of ROH, even in the class of >16 Mbp . The other six breeds in this last class showed 510, 392, 306, 169, 146 and 108 long ROH, for SAR, SAB, PON, SAM, CHA and LIM, respectively.ROH were reported in Descriptive statistics for FROH for LIM, CHA, SAB and SAR was identical (~0.22), while the lowest average inbreeding coefficient was found in SAM (0.20). Tuscan and cosmopolitan breeds presented minimum values close to 0, instead Sardinian breeds had a minimum FROH near to 0.14.Tuscan breeds presented the highest level of average genomic inbreeding coefficients  with maximum values that exceeded 0.5. FROH was calculated for the previous classes of length , MKRN3 (Makorin finger protein 3), MAGEL2 (MAGE family member L2) and NDN (Necdin), located upstream of the Prader-Willi syndrome (PWS) region.One genomic region on BTA21 was found in common in several breeds starting at 83,766 Mbp with BovineHD2100000012. The region was almost identical for CHA, PON, SAB with a length of ~1.70 Mbp; this run contained 18 SNPs for CHA and PON, 17 for SAB. This ROH was present also in LIM, MUP, SAM, and SAR starting at the same SNP but finishing with different markers . For the aforementioned breeds, the number of SNPs within this region ranged from 25 (LIM) to 20 (Sardinian). Within this shared run, four genes were detected: From the 35 ROH detected, within-breed specific regions containing a minimum number of 15 SNPs were selected to investigate the genes . The lisMYOG (Myogenin) (a muscle-specific transcription factor), CHI3L1 (Chitinase 3 Like 1) and CHIT1 (Chitinase 1) genes on BTA16, both of these are involved in inflammatory processes. On BTA5, for CHA we found the largest number of genes (n = 57). These were linked to cell survival after damage or stress , transport and/or esterification of cholesterol , growth regulation, development and differentiation  and olfactory receptors . For LIM, the most interesting detected genes were located on BTA2: CYP27C1 (encoding a member of the cytochrome P450 superfamily), MSTN (Myostatin) and genes encoding collagen chains . MUP and PON shared CACNG2 , which is on BTA5; It is a gene involved in synaptic plasticity, learning and memory.Among genes presented in K equal to 64. Tuscan breeds showed a greater proportion of HBD genome also for RK ranged from 4 to 8, while CAL is the breed with higher proportion of HBD segments when RK was 16.The percentage of non-HBD segments was higher in SAB (42.7%), followed by SAM, SAR and MUP (~ 39%), while CHA, LIM and PON showed values around 32.5%. The HBD segments identified  belongedG-T) was calculated respect to different base populations , and only Tuscan breeds have been involved in new consistent inbreeding events, approximately 2–4 generations ago. Results were similar when inbreeding  explained a smaller HBD proportion: the average inbreeding coefficient was close to 0 in cosmopolitan and Sardinian breeds, while for PON and MUP FG was slightly lower than 0.1 from classes with 8 ≤ RK ≤ 32, while CAL didn’t exceed 0.05. The inbreeding coefficient associated with common ancestors tracing back up to approximately two generations ago (corresponding to HBD-classes with RK ≤ 4) tended to 0 in all Tuscan breeds.The FPartitioning of individual genomes in different HBD classes was also performed and K = 4 and 8, respectively). CAL also showed traces of ancestors from 8 generation ago in several individuals. Sardinian and cosmopolitan breeds showed lower levels of inbreeding and HBD segments derived from ancient ancestors.The results confirmed that all breeds acquired the majority of their inbreeding load derived from ancient ancestors (32 generations ago), but once again, the Tuscan breeds appeared with a different demographic historical structure compared to Sardinian and cosmopolitan breeds; the level of inbreeding was higher in Tuscan and deriving both from past and recent phenomena. Indeed, PON and MUP were affected also by inbreeding acquired in recent generations  and the partitioning of homozygous identical by descent (HBD) segments across generations.The advent of high-throughput genotyping arrays facilitated the study of genetic diversity and population structure in cattle , but lochttp://www.anacli.it/), while Calvana and Mucca Pisana samples amounted to a few hundred [MDS approach has been preferred to Principal component analysis (PCA) because it detects meaningful dimensions that explain observed genetic distance, i.e. pairwise IBS distance, while PCA method calculates the population structure based on genetic correlations among individuals . The gen hundred . CHA andhttp://www.anacli.it). The growing interest in selection programs have probably led to a slight increase in inbreeding compared to other European Limousine and Charolais populations; indeed, Szmatola et al. [ROH in Polish Limousine ranging from 0.059 (>1Mbp) to 0.011 (>16 Mbp), while, Polish Charolaise showed values from 0.065 to 0.009. In this study FROH decreased in both selected breeds from 0.22 to 0.02 for the aforementioned classes of length.The total number of ROH detected in each breed, was higher than what found in other studies focusing on cattle , 35, 36.a et al.  describeROH in SAR, SAB and SAM has been maintained near 0.05  for the investigation of selection signatures, led to the identification of a common run between breeds. It is located on BTA21, starting from 83.766 kbp to 1,786.020 kbp.MYOG and FMOD genes were located. MYOG is related with MSTN gene which regulates muscle mass. The different myogenin genotypes are related to the variation in the number of muscle fibers and the growth rate, which lead to a variation in the muscle mass [MYOG in marker-assisted selection for improving the growth trait in chicken [An interesting genomic region with an occurrence of more than 80%, has been found in CAL on BTA16 , where cle mass . Indeed, chicken .FMOD plays an important role in the maintenance of mature tissues and has been discovered that reduces scar formation without diminishing the tensile strength in adult wound models  [TIMELESS, APON, APOF, STAT2, IL23A and PAN2; the second one contained several genes of Olfactory Receptor Families.and pig) . It mighAPOF and APON are apolipoproteins which are component of lipoproteins and it has been showed that overexpression of Apolipoprotein F in mice reduced HDL cholesterol levels by 20–25% by accelerating clearance from the circulation [culation .ORs) are essential for mammals to avoid dangers and search food [ORs and intake-related traits of livestock. Magalhães et al. [GDP (guanosine diphosphate) to GTP (guanosine triphosphate). Other explanations for the effect of ORs on meat traits are their action by promoting the absorption of fatty acids and by differentiating adipocyte, that leads to an increase in accumulation of fat; in addition, their known role to increase the search of food improve the weight gain.Olfactory receptors  [QTL:11644), meat percentage  and meat weight (QTL:11694) . Previous studies identified MSTN within ROH island in Limousine cattle, highlighting that MSTN is a gene under selective pressure for the phenotypic features in Limousine breed, indeed, MSTN has a strong positive effect on muscling and it is negative correlated with fat deposition [This cluster of genes found in CHA, is in line with artificial selection purposes, as for LIM, which included in the first significant run , the presence of as MYOG) ; three tposition , 49.CACNG2 gene. Interestingly, this gene was found to be associated with milk protein percentage QTL , which is probably because these two breeds originate from several past crossbreeding events including with Holstein and Schwyz (MUP) [http://www.anacli.it).MUP and PON presented consecutive genomic regions on BTA5, sharing the yz (MUP)  and ReggK equal to 2, 4, 8, 16, 32, 64, which correspond to 1, 2, 4, 8, 16 and 32 generations ago. FG values are higher than FROH observing the equivalent length of segments, and this could be because the algorithm has difficulties to detect very short ROH when a low and/or medium density chips have used [G values higher than FROH values. However, within 32 generations, no pronounced differences in FG levels have been found by Solè et al. [K is 64) compared to cosmopolitan, suggesting that for these breeds, RK should be increased in order to detect shorter HBD segments which have been found during ROH analysis Click here for additional data file.S2 Table(DOCX)Click here for additional data file.S3 Table(DOCX)Click here for additional data file.