AASDHPPT, GDPD5, PAR3, SOX6, GPC1 and a signal pathway of AKT1 were detected with the most extreme P-values. Further enrichment analyses indicated that these genes were associated with immune function, sensory organ development and neurogenesis, and may have experienced positive selection in chicken. Moreover, some of core regions exactly overlapped with genes excavated in our previous GWAS, suggesting that these genes have undergone positive selection may affect egg production. Findings in our study could draw a comparatively integrate genome-wide map of selection signature in the chicken genome, and would be worthy for explicating the genetic mechanisms of phenotypic diversity in poultry breeding.Chicken is recognized as an excellent model for studies of genetic mechanism of phenotypic and genomic evolution, with large effective population size and strong human-driven selection. In the present study, we performed Extended Haplotype Homozygosity (EHH) tests to identify significant core regions employing 600K SNP Chicken chip in an F2 population of 1,534 hens, which was derived from reciprocal crosses between White Leghorn and Dongxiang chicken. Results indicated that a total of 49,151 core regions with an average length of 9.79 Kb were identified, which occupied approximately 52.15% of genome across all autosomes, and 806 significant core regions attracted us mostly. Genes in candidate regions may experience positive selection and were considered to have possible influence on beneficial economic traits. A panel of genes including  The chicken have gone through intensive selection because of domestication and breeding, what has gave rise to various phenotypes when compared with their wild counterparts . SelectiRecently, with the development and application of high throughput and cost-effective genotyping techniques, the power of detecting selective signatures at genome level has experienced a major breakthrough. Varieties of methods are available to detect genome-wide selective signature using the DNA sequence data and were generally classified by Qanbari  in two mSo far, the EHH test has been applied to detect selective signatures of different animals in many researches ,14,15,16In the present study, we implemented a genome-wide detection of selective signatures using the EHH test with 600 K Affymetrix Chicken SNP array in an F2 population including 1,534 hens. It is a highlight that the high-density SNP chip has been used to detect selection signatures in a large-scale population. Furthermore, our previous GWAS results were incorporated with these detected core regions, what lead to a better understanding of some important biologic processes and causal variants related to crucial economic traits in chicken.All the blood samples were collected from brachial veins of chickens by standard venipuncture and the whole procedures were performed in accordance with the Guidelines for Experimental Animals established by the Ministry of Agriculture of China . The entire study was approved by the Animal Welfare Committee of China Agricultural University (permit number: SYXK 2007–0023).An F2 resource population was derived from reciprocal crossed between a popular commercial breed White Leghorn (WL) and a Chinese indigenous strain Dongxiang chickens (DX). We chose 1,534 hens as experimental samples, which was originating from 49 half-sib families and 550 full-sib families. And all the blood samples were collected from brachial veins of chickens by standard venipuncture. Genomic DNA was extracted by standard phenol-chloroform method and the eligible samples were genotyped with the 600 K Affymetrix Axiom Chicken Genotyping Array . All hens were caged individually and reared with feed and water ad libitum in the same environment (Farm of Jiangsu Institute of Poultry Science).http://affymetrix.com/). We kept the samples with dish quality control (DQC) > 0.82 and sample call rate >99% or SNP call rate >97%, only 532,299 SNPs remained for the following analyses. In addition, we deleted 26,656 SNPs on sex chromosomes and 302 SNPs on the two linkage groups. SNPs with minor allele frequency (MAF) less than 5% and those deviating from Hardy-Weinberg equilibrium (HWE) test  using the PLINK package [Before analyses, we first removed 7,883 SNPs with unknown chromosome and 112 SNPs with redundant genomic positions. Then we performed the quality control with Affymetrix Power Tools (APT) and R scripts according to the guidelines by Affymetrix ( package  were exc package  to imputhttp://www.broadinstitute.org/mpg/sweep/) [We firstly identified core regions, which were characterized by SNPs with strong linkage disequilibrium (LD) and consisted of some core haplotypes after the EHH test by the software Sweep v.1.1 (/sweep/)  in the w/sweep/) ,23. And /sweep/) .Which the Furthermore, we treated and ordered the frequency of all core haplotypes into 20 bins to compute the significance of the REHH values, obtaining P-values by log-transforming to reach normality and calculating the mean and standard deviation.http://www.animalgenome.org/cgi-bin/QTLdb/GG/index)[2 population using the function annotation and clustering tools in the Database for annotation, Visualization and Integrated Discovery (DAVID) [After performing EHH tests, regions with extreme REHH p-values were considered as candidates for selective sweeps, as proposed by Sabeti et al.. CompariGG/index), we firsGG/index), 26. We  (DAVID) .With an average neighbor marker distance of 1.79 kb, 580,961 SNPs were genotyped by using 600 K Affymetrix Axiom Chicken Genotyping Array. After the quality control and discarding the SNPs on two linkage groups and sex chromosome, 389,618 SNPs and 1,512 individuals were finally remained for the further analyses. For the SNPs analyzed in this study, a total of 49,151 core regions covering 479.51 Mbp (52.15%) of the genome were uncovered. The average length of core region was approximated as 9.76±13.11 Kb, while the maximum was of 965.16 Kb located in chromosome 5. For the every chromosome, the covered proportion of length by core regions with total length, as same as the number of SNPs, were also given in A total of 434,509 EHH tests were performed in 49,151 core regions and averaged out to 8.84 tests per core region. Core haplotypes under selection would have a relatively high frequency according to the selection signature theory. Hence, the core haplotypes with frequency below 25% were totally excluded. The distribution of remaining core haplotypes across the whole genome was visualized via a Manhattan plot in 10 of the REHH P-value distributed within each bin, which was partitioned by core haplotype frequency, was displayed in Core regions owning the significant P-values (P < 0.01) of REHH were explored to identify all overlaps with published QTLs in the chicken QTL database which was available online. The current release of the Chicken QTLdb contains 4,676 QTLs (319 different traits) from 224 publications. As a consequence, the overlapping core regions were detected to be mainly associated with production traits and immune function, and the lowest P-values (top six) were displayed in We further annotated overlapping chicken genes located in significant core regions based on Ensembl gene database. A summary of statistics was shown in AASDHPPT , GDPD5 (glycerophosphodiester phosphodiesterase domain containing 5), PARD3 (par-3 family cell polarity regulator) and SOX6 (SRY-box 6), were related to immune function and neurogenesis. Particularly, AKT1 and GPC1 participating in AKT signal pathway and Wnt signal pathway respectively, which played a key role in organogenesis and reproduction performance, were obtained.The subset of genes in all core regions with extreme REHH P-values (p < 0.001) were displayed in The annotations of genes and analysis of pathways were conducted using online DAVID software . The gen2 chicken population. Furthermore, we took fully advantage of high density SNP chips which contain much more information, so that to improve the accuracy of detection.Chicken are reported to have gone through strict artificial selection and breeding for multi-purpose as far as thousands years ago  and can P < 0.01) with the chicken QTL database. These positively selected regions were illustrated to be relevant to some important economic traits, like egg production, feed efficiency, body weight and immunity. These results were in accordance with the findings reported by Li DF et al. [The 806 significant core regions  identifiF et al. .NCAPG gene locating on chromosome 4 had been identified influencing both egg weight and body weight simultaneously in a pleiotropic manner [CLSPN gene on chromosome 23 displayed significant haplotype [To elaborate whether the selection signatures were correlative with phenotypic traits, we compared them with our previous genome-wide association studies. In our previous GWAS studies , 38, 39,c manner ; the CLSaplotype ,41. ThesAASDHPPT, GDPD5, PARD3, SOX6, AKT1 and GPC1, were testified to be able to exert influence on some biological process. Among them, the AASDHPPT gene was reported to have a role in either the adaptive or innate immune response [GDPD5 gene, sharing homology with glycerophosphodiester phosphodiesterase 2 (GDE2), is important to initiate neurogenesis and cellular proliferation and differentiation [PARD3. Additionally, the positive feedback loop between Sox2 and Sox6, whose functions were to inhibit premature neuronal differentiation, played a key role in maintaining the neural progenitor cells [GPC1 gene involved in Wnt pathway was of great importance in chick, which can regulate the signaling mechanisms in early formation of the trigeminal sensory system and cell proliferation hence affected reproduction performance [As shown in response . The GDPntiation . Accordintiation , the moror cells . The othor cells  and coulor cells . The GPCformance ,49.VSX2 and Bmp7, which were associated with cell growth [The significant GO terms were shown in table and the terms of biological process appealed to us mostly. The terms including ‘cell part morphogenesis’, ‘eye development’, ‘neuron projection morphogenesis’ and ‘sensory organ development’ were consistent with our previous result about the function of the extreme significant genes. Among our findings, genes participated in these terms that overlapping with positive selection core regions, like l growth  and sensl growth ,52, playl growth .In conclusion, 806 significant core regions were detected across the whole genome in chicken applying the EHH test together with certain bioinformatics analyses. Genes in these regions related to immune function, sensory organ development and neurogenesis may experience positive selection. Moreover, our results draw a comparatively integrated genome-wide map of selection signature in chicken and yielded valuable insight into the genetic basic of artificial selection in poultry breeding.S1 Fig(TIFF)Click here for additional data file.S2 Fig(TIF)Click here for additional data file.S1 TableIncluding traits information, QTL and the QTL IDs.(XLSX)Click here for additional data file.S2 TableIncluding the chromosome number, start position, end position, length and REHH P-value.(XLSX)Click here for additional data file.


GWAS using FarmCPU detected a stable quantitative trait nucleotide (QTN) for hilum color and seed color, which is consistent with the known loci or genes. Although no universal QTNs for flowering time and maturity were identified across all environments, a total of 30 consistent QTNs were detected for flowering time (R1) or maturity (R7 and R8) on 16 chromosomes, most of them were corresponding to known E1 to E4 genes or QTL region reported in SoyBase (soybase.org). Of 16 consistent QTNs for protein and oil contents, 11 QTNs were detected having antagonistic effects on protein and oil content, while 4 QTNs soly for oil content, and one QTN soly for protein content. The information gained in this study demonstrated that the usefulness of the medium-density SNP array in genotyping for genetic study and molecular breeding.Soybean was domesticated about 5,000 to 6,000 years ago in China. Although genotyping technologies such as genotyping by sequencing (GBS) and high-density array are available, it is convenient and economical to genotype cultivars or populations using medium-density SNP array in genetic study as well as in molecular breeding. In this study, 235 cultivars, collected from China, Japan, USA, Canada and some other countries, were genotyped using SoySNP8k iSelect BeadChip with 7,189 single nucleotide polymorphisms (SNPs). In total, 4,471 polymorphic SNP markers were used to analyze population structure and perform genome-wide association study (GWAS). The most likely K value was 7, indicating this population can be divided into 7 subpopulations, which is well in accordance with the geographic origins of cultivars or accession studied. The LD decay rate was estimated at 184 kb, where r Glycine max (L.) Merr.] is one of important crops worldwide, providing a sustainable source of high-quality protein feed and vegetable oil. Soybean was domesticated in China more than 5,000–6,000 years ago. Soybean can grow across a wide range of latitudes from 50°N to 35°S . Previous studies identified eleven major-effect loci affecting flowering and maturity in soybean, which have been designated as E1 to E10, and the J locus for “long juvenile period”  genes of GmPHYA3 and GmPHYA2 , which control flowering time through regulation of E1 expression  or quantitative trait nucleotide (QTN) related to soybean flowering time  and maturity have also been documented at SoyBase (http://soybase.org). Many genes or QTL might regulate flowering time through regulation of the expression of the E1 gene  and found 29 SNPs located on ten different chromosomes that are significantly associated with the seven seed composition traits, of which eight SNPs co-localized with QTLs previously uncovered in linkage or association mapping studies conducted with cultivated soybean samples . Cao et al. , LEAFY COTYLEDON1 (LEC1), and LEC2 are involved in the regulatory pathways modulating seed oil content in Arabidopsis. However, their homologs have been modified in the palaeopolyploid soybean, each exhibiting similar intensities of purifying selection to their respective duplicates since these pairs were formed by a 13 mya (million years ago) whole-genome duplication (WGD) event  takes advantage of more historic recombination events that have occurred within natural populations. GWAS has been widely applied to crop plants such as maize  was revealed. GWAS were performed to identify the QTN associated with flowering time and the protein/oil contents using FarmCPU. More than 30 QTN were identified under multiple environments for flowering time and maturity; while 16 consistent QTNs were detected for protein and oil contents.A set of 235 cultivars collected from China, Japan, USA, and Canada were mainly obtained from the Gene Resource Center of Jilin Academy of Agricultural Sciences, China. The origin and other traits for these cultivars are listed in Table Soybean accessions were evaluated for photoperiodic responses at six geographic locations: (1), Harbin (hereafter termed as HRB): Research field at the Campus of Northeast Institute of Geography and Agroecology, Harbin, Heilongjiang ; (2), Mudanjiang (hereafter termed as MDJ): Mudanjiang Research Station, Heilongjiang Academy of Agricultural Science ; (3) Gongzhuling (hereafter termed as GZL): Gongzhuling Research Station, Jilin Academy of Agricultural Science, Gongzhuling, Jilin ; (4) Jinan (JN): Campus of Shandong Normal University, Jinan, Shandong ; (5) Huaian (hereafter termed as HA): Huaiyin Research Station, Jiangsu Academy of Agricultural Science, Huaian, Jiangsu ; (6) Nanjing (hereafter termed as NJ): Luhe Research Station, Jiangsu Academy of Agricultural Science, Nanjing, Jiangsu . At least 15 plants for each cultivar or accession per geographic location were grown in a single row with 20 cm apart for phenotypic evaluation. Days from planting to flowering (R1) and maturity (R7 and R8) were recorded according to Fehr's description  were precluded for maturity and protein/oil content.Seed coat or hilum color were classified into four groups and coded as follows: (1) yellow or yellowish; (2) green or light brown; (3) brown; (4) black. Seed-weight (100-seedweight) was determined by weighing 3 different set of randomly selected 100 seeds for each cultivar or accession. Seed protein and oil contents of cultivars were measured using MATRIX-I FT-NIR spectrometer (Bruker). The protein or oil contents were measured three times using different bulk seeds of a given cultivar.The heritability estimates were calculated using variance components obtained by lme4 of R package Fehr, .ftp://159.226.208.134/public/SNP_data.zip (Data Sheet DNA was extracted from fresh leaves using the hexadecyltrimethylammonium bromide (CTAB) method with slight modification  using the stand-alone BLAST applications (BLAST+) (ftp://ftp.ncbi.nlm.nih.gov/blast/executables/blast+/LATEST) (Data Sheet ftp://159.226.208.134/public/SNP_data.zip). In order to delimit the influence of batch specific or biased SNP markers on GWAS and population structure analysis, we deleted 459 batch specific or biased SNPs. The unbiased SNP was defined as the frequency of two homogenous nucleotide identities  at a given locus in a batch was 0.85 or higher. An unbiased marker having the same two nucleotide identities in two batches were kept for further analysis. According to this threshold of 0.85, 4,471 polymorphic SNP markers were enclosed for population structure and GWAS analysis (Data Sheet ftp://159.226.208.134/public/SNP_data.zip).Of total 5,039 polymorphic SNP makers, 4,961 were mapped into 20 chromosome (Chr) and 31 scaffolds. Apart from unmapped 78 markers, 4,930 SNP markers were successfully mapped onto 20 chromosomes of the soybean genome . Also this trend was confirmed using GAPIT .In this study, a relatively complex population structure (K = 7) was revealed in comparison of previous reports in which population structures  were disclosed  gene has been proved to regulate the hilum color. The significant QTN overlapped a CHS gene clustered region in chromosome 8 , CHS3 , CHS4, CHS3, CHS9  of cultivars were presented in Table Statistical analysis Table  showed tAlthough phenotypic data for R7 and R8 were not conducted in all locations, the basic distributions were presented in Figure E1–E4, are controlling flowering time as well as maturity, we also enclosed R7 and R8 for GWAS.In order to analyze the relationship between R1 and R7/R8, the correlation coefficients matrix were generated and listed in Table Although no consistent QTNs for flowering time and maturity were identified across all environments, a total of 30 consistent QTNs were detected for flowering time (R1) or maturity (R7 and R8) on 16 chromosomes Figures . In TablE3 gene, encoding phytochrome A (PHYA), is located from 47633059 to 47641958. The QTN (Gm19_46611973_C_T-1_B_F_2179344248) at 46730237 were detected having four location with positive (suppressing flowering) effect , we detected a QTN at 45054578 with effect of 7.40 Table , which iE1 gene is located in the pericentromeric region from 20207253 to 20207829 , was reported to be less diversified among Chinese and American cultivars  about 89 kb from the QTN (Gm16_3598173_C_T-1_B_F_2179342018 with average effect of 3.09) detected (Table www.soybase.org) merits further investigation.On chromosome 6, a QTN (Gm06_10891060_T_C-1_B_F_2179335984) was detected at 10919417 with effect of 2.47 day. The ed Table . Other Qed Table . Among ted Table  also reped Table , whetherE1, E2, E3, and E4 of 180 cultivars revealed great allelic variations at E1 and E3 genes  , epistatic association between QTLs as well as environmental factors. High negative correlation coefficients between oil and protein content in soybean was revealed in this study, which is consistent with previous reports (Boydak et al., Overall, a large number of loci have been identified to underlie some important agronomic traits e.g., flowering time, maturity, oil and protein contents; however, a detailed study may only detect some of them. Ideally, a large numbers of natural population can be subtracted into a subpopulation each member of which carries higher or lower phenotypic values for a given trait; GWAS for the given trait can be performed using in this subpopulation (Yan et al., A large number of QTLs or loci underlying agronomically important traits have been identified by GWAS or linkage mapping, some of which were detected in different environments or in different populations while some are environmental or population specific. Although molecular identities of genes or QTL underlying some important agronomic traits e.g., maturity have been disclosed, vast of loci underlying quantitative traits like soybean seed protein /oil content are still largely unknown. GWAS in combination with biparental populations such as RIL, NIL, CSSL, is very powerful for QTL identification and their gene cloning. As high throughput sequencing data aggregate, the important QTL or QTN detected by traditional linkage mapping or GWAS will be verified and subsequently cloned. As most components of a molecular or signaling pathway have been identified (Gentzbittel et al., ZX conceived this project; YW, YL performed the most experiments in the laboratory; HW, BH, HZ, SL, XL, XC, HQ, JY, CZ, DH conducted field experiment and phenotypic observation; JZ, ZW, ZX: performed data analysis including GWAS; ZX, YW, and YL wrote the article; DW contributed to scientific discussions and critical revision of manuscript. All authors reviewed the final manuscript.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.


Multiplicity of infection (MOI), also termed complexity of infection (COI), is defined as the number of genetically distinct parasite strains co-infecting a single host, which is an important indicator of malaria epidemiology. PCR-based genotyping often underestimates MOI. Next generation sequencing technologies provide much more accurate and genome-wide characterization of polyclonal infections. However, complete haplotype characterization of multiclonal infections remains a challenge due to PCR artifacts and sequencing errors, and requires efficient computational tools. In this review, the advantages and limitations of current molecular approaches to determine multiplicity of malaria parasite infection are discussed. Plasmodium vivax, multiclonal infections also can be caused by the relapse of a liver-stage hypnozoite during ongoing blood-stage infection of a genetically distinct clone. The number of co-infections within a host might be an important indicator of transmission intensity, though data to date are inconclusive [Individuals in malaria-endemic countries often carry several genetically distinct clones of the same parasite species. Multiplicity of infection (MOI), also termed complexity of infection (COI) is defined as the number of genetically distinct parasite strains co-infecting a single host . Multiclnclusive –9. For enclusive  and Papunclusive , while inclusive , 6. Sevenclusive –18 and onclusive –21. On tnclusive –24, whilnclusive . MOI cannclusive , 27, depnclusive . Neafseynclusive . Hence, nclusive . In drugnclusive , 32. AccPlasmodium infections have been published by typing size-polymorphic antigens or microsatellite markers [msp-1, msp-2, msp-3, glurp, csp and ama-1 in Plasmodium falciparum and P. vivax, can be amplified by PCR, and the size of the amplicon is determined by either gel or capillary electrophoresis. Protocols are comparably simple without requiring expensive equipment. In many populations, high diversity of size-polymorphic antigens has been observed in both P. falciparum [P. vivax [Over the past decade, hundreds of papers describing the multiplicity of  markers , 33–45. lciparum , 46–53 aP. vivax –63. AntiP. falciparum [P. vivax [Plasmodium malariae [P. vivax from 18 different global studies, Sutton et al. found that five  markers could be used as candidates for MOI studies [P. falciparum, there are 12 microsatellite markers  from 7 chromosomes that are frequently used for genetic diversity and multiplicity of infection studies [Microsatellite markers are highly polymorphic with tandem repeats of 2–6 bp. They are expected to be selection neutral with no phenotypic consequences –67. Micrlciparum , 69–71, P. vivax , 72–74, malariae , 76. Aft studies . However studies –83, the  studies , 65, 84.2 concentration and the minimum number of amplification cycles.The major limitation of agarose gel electrophoresis is its limited discrimination of alleles of similar sizes. It is unable to discriminate allele size difference less than 20 bp , resultiP. falciparum SNPs selected from over 112,000 SNPs of 18 parasite genomes [msp-1 and msp-2 [msp-1 and msp-2 genotyping because many more possible alleles exist among the 24 markers than length polymorphisms within these regions. In P. vivax, a panel of 42-SNP barcodes was developed based on clinical samples from parasite populations in South America , Africa (Ethiopia) and Asia (Sri Lanka) [As alternatives to size-polymorphic markers, several panels of genome-wide SNP makers have been identified and protocols for typing developed. One panel includes 24  genomes . Typing nd msp-2 , but thei Lanka) , and typi Lanka) . HRM genHigh-throughput SNP genotyping can also be done on the Sequenom MassARRAY iPLEX platform, which is capable of multiplexing up to 40 SNPs per single reaction. SNP allele is determined by variations of MALDI-TOF mass spectrometry at SNP site . The parP. falciparum from laboratory strains and clinical samples from East and West Africa, Southeast Asia, and Oceania [Large-scale SNP genotyping using a custom 384-SNP Illumina GoldenGate has been conducted in  Oceania . This ge Oceania .P. falciparum [−4. The clonal composition of parasite populations was measured by genotypic richness index (R) and the β-parameter of the Pareto distribution. R measures the proportion of unique genotypes (G) in the population and is estimated as: R = (G − 1)/(n − 1), which varies from 0 when all n samples in a population have the same genotype, to 1.0, when all samples have a different genotype [Nkhoma et al. used a set of 96 genome-wide SNPs and Illumina GoldenGate to examine changes in parasite population genetic parameters associated with decline in transmission in genotype . β-Paramgenotype . A minimPlasmodium infections have entered a new era. NGS is becoming the standard approach to generate population genomic data. Great progress has been made for NGS data analysis and MOI estimation with the development of various bioinformatic tools (Table pvmsp1 short amplicon (117 bp) deep sequencing, Lin et al. reported 67 unique haplotypes identified from 78 Cambodian P. vivax samples with an average of 3.6 MOI within each individual [P. falciparum amplicons of 321 bp (pf-csp), 305 bp (pf-ama1), and 306 bp (pf-k13) have been used to conduct ultra-deep sequencing, which can quantitatively detect unique haplotypes comprising as little as 2% of a polyclonal infection [With the advent of next-generation sequencing (NGS) technology, studies on population genetics, diversity, and multiplicity of ls Table . In highdividual . To incrnfection .Table 1TPlasmodium membrane protein (cpmp) was described as a highly polymorphic marker, and an assay was developed with 100% sensitivity to detect all clones present at a frequency of > 1% [Plasmodium species diversity and MOI in symptomatic Gabonese patients [Recently, the conserved  of > 1% . Ultra-dpatients . It is, patients  and Seekpatients .var2csa gene in P. falciparum infected placental samples and found that parasite haplotypes can be recovered if they account for as little as 5% of the mixed template [var2csa gene in P. falciparum was sequenced by PacBio deep sequencing and used to evaluate artificially created a mixture of laboratory strains [var2csa haplotypes. It however identified minor alleles only if present at frequencies > 23% in the mixture, due to low read depth and coverage and high sequencing errors in homopolymer stretches [Several studies have evaluated long amplicon deep sequencing to examine multiplicity of infection by sequencing mixtures of multiple lab strains. For example, Patel et al. performed amplicon deep sequencing of 1.6 kb ID1-DBL2× region of template . A long  strains . De novotretches . In ordetretches . The majtretches , 104. AfP. falciparum samples [Whole genome sequencing of field isolates is rapidly becoming common, and a number of software packages have been developed to identify multiclonal infections and reconstruct haplotypes. The estMOI software package can be used to analyse deep sequencing data for estimation of the presence of multiple genotypes, the number of infections, as well as proportions by phasing information of each allele . In othe samples .WS (similar to Wright’s inbreeding coefficient FIS) statistics derived from read count data of whole genome sequencing at 86,158 SNP positions had been used to explore the number of within-host clones, their relative proportions and genetic divergence in P. falciparum [WS values, where FWS = 1 − HW / HS, observed in blood samples provide a proxy indicator of inbreeding rates in the parasite population. The FWS ranges from 0 to 1, with 0 reflecting a mixture of highly unrelated clones and 1 reflecting a single clone. In other words, a low FWS reflects a low risk of inbreeding/high risk of out-crossing and thus high within-host diversity. A threshold of FWS ≥ 0.95 was suggested as indication of an essentially clonal infection. A significant negative correlation was observed between the PCR-based msp1 genotyping and FWS statistics for calculating MOI. Although the FWS can capture information of within-host heterozygosity relative to population diversity, it does not provide a direct estimate of MOI. To help make a connection of FWS with MOI estimates, O’Brien et al. presented several new approaches for inferring inbreeding coefficients using read counts from WGS, which can be implemented in the R package [The Flciparum , 109. Th package .DEploid, which can be used to analyse genome-wide SNP data for estimation of the number of strains, their relative proportions and the haplotypes present in a sample [P. falciparum [DEploid is the only software currently available that can reconstruct haplotypes as well as estimate COI using genome wide SNP data. The limitation is that it requires a panel of references, which should include enough different reference strains to cover all the haplotype structure representing the field population. However, this is not realistic for current computational technology.Zhu et al. developed a software package named a sample . The soflciparum . It is aFinally, sequencing coverage remains a main challenge when estimating MOI from whole genome sequencing data. By amplicon deep sequencing, a read depth of 1000–10,000× coverage is easily obtained, resulting in a significantly increased power of detection of minority clones. At this sequencing depth, minority clones at a frequency of 0.1% have been successfully identified in experimental mixtures of strains . A typicmsp-1, msp-2, and microsatellite markers often underestimate the MOI. Next-generation amplicon sequencing technology has largely increased the sensitivity in detection of polyclonal infections, but targets only partial genomic regions, which may not represent complete polymorphism in mixed infections. Whole genome sequencing yields complete information for parasite infections, but requires extensive and efficient computation algorithms and mathematical models to reconstruct the complete haplotypes, and sensitivity to detect minority clones is limited due to low reads coverage for minor strains. Therefore, there is a need to develop an accurate and cost-effective method for detection of minority clones at the current stage. As the cost of whole genome sequencing continues to decrease and bioinformatics tools mature, fine-scale investigation of multiplicity of Plasmodium infections becomes possible in the near future.Traditional standard PCR-based genotyping methods using


These two BSA-seq-derived loci were further validated with SSR markers and fine-mapped. qCC1 was mapped to a 30.7-kb region containing four annotated genes and qCC2 was mapped to a 67.7-kb region with nine genes. These two regions contained, respectively, genes D1 and D2, which had previously been identified by homology-based cloning as being associated with cotyledon color. Sequence analysis of the NGS data also identified a frameshift deletion in the coding region of D1. These results suggested that BSA-seq could accelerate the mapping of loci controlling qualitative traits, even if a trait is controlled by more than one locus.Next-generation sequencing (NGS)-based bulked-segregant analysis (BSA) approaches have been proven successful for rapidly mapping genes in plant species. However, most such methods are based on mutants and usually only one gene controlling the mutant phenotype is identified. In this study, NGS-based BSA was employed to map simultaneously two qualitative genes controlling cotyledon color of seed in soybean. Yellow-cotyledon (YC) and green-cotyledon (GC) bulks from progenies of a biparental population (Zhonghuang 30 × Jiyu 102) were sequenced. The SNP-index of each SNP locus in YC and GC bulks was calculated and two genomic regions on chromosomes 1 and 11 harboring, respectively, loci  Identifying a gene or locus conditioning a trait is one of the major tools for characterization of gene function and eventually for the improvement of agronomic traits in crops . ConventBulked segregant analysis (BSA) provides a simple approach for rapidly identifying molecular markers tightly linked to the causal gene underlying a given phenotype . StartinArabidopsis  genes by the homology-based cloning method  for soybean cotyledon color have been identified and classical genetics methods have revealed that several loci including is trait . Recentlg method .D1 gene was identified by analysis of whole genome sequencing data, indicating that BSA combined with high-throughput sequencing can be used for rapid mapping of qualitative traits, even if a trait is controlled by more than one locus.In the present study, a genome-wide NGS-based BSA mapping approach was implemented in a soybean biparental population for cotyledon color controlled by two genes. After progenies derived from the crossing of two soybean accessions with distinct cotyledon colors were phenotyped, yellow-cotyledon (YC) and green-cotyledon (GC) bulks were constructed and sequenced along with their parental lines. Associated regions were identified using the Δ(SNP-index) method after SNPs among parental lines and DNA bulks were called. Two associated loci were validated and fine-mapped to 30.7 and 67.7-kb intervals by marker-based classical gene mapping. Two previously identified stay-green genes were located in fine-mapped regions and a sequence variant of the Glycine max cv. Zhonghuang 30 (ZH30), with yellow cotyledons, and Jiyu 102 (JY102), with green cotyledons, were obtained from the National Soybean Genebank, Institute of Crop Science, Chinese Academy of Agricultural Sciences. ZH30 and JY102 were crossed and confirmed F1 plants were self-fertilized to develop segregating populations. The cotyledon colors of all F1 and F2 seeds and selected F2:3 populations were recorded. The chi-square (χ2) test was used to evaluate the fit of observed to expected segregation ratios in all populations.TM 2500 platform.Genomic DNA was isolated from young leaves of soybean using a genomic DNA purification kit  according to the manufacturer’s protocol. DNA samples were quantified using a Quawell Q5000 spectrophotometer . Two bulks were generated by pooling equal amounts of DNA from 30 lines with green cotyledons and 30 with yellow cotyledons. About 5 μg of DNA from two bulks and two parental lines were used to construct paired-end sequencing libraries, which were sequenced on an Illumina HiSeq1. High-quality sequences were aligned and mapped to the Glycine max Wm82.a2.v1 reference genome from Phytozome2 using BWA with default parameters  were selected for SNP-index analysis. A SNP-index was calculated at each SNP position for both the YC and GC bulks using the base in JY102 as alternative base . Thus, tP-value < 0.05 were considered candidate genomic regions harboring a locus associated with cotyledon color.The average distributions of the SNP-index and Δ(SNP-index) were estimated in a given genomic interval by a sliding window approach with a 2-Mb window size and 10-kb step, and were plotted to generate SNP-index plots for all soybean chromosomes. Regions in which the average Δ(SNP-index) of a locus was significant larger than surrounding region and windows showed an average qCC1 and qCC2 (Supplementary Table S1). One hundred lines with cotyledon color segregation fitting a 3:1 ratio were selected for genotyping. These lines were classified into two groups after genotyping qCC1 and qCC2 loci, with cotyledon color of the members in one group only controlled by qCC1 and in the other only controlled by qCC2. Based on the difference between the genotypes as assessed using polymorphic markers, recombinants were identified in F2:3 populations and used to refine the locations of qCC1 and qCC2.BARCSOYSSR and SNP markers polymorphic between the two parental lines were developed in the predicted candidate regions of TM RT Reagent Kit  after removal of genomic DNA from the RNA. Gene-specific primers (Supplementary Table S1) were used to amplify the promoter and genomic sequences of Glyma.01g214600 and the coding sequence of Glyma.11g027400. PCR products were analyzed on 1% agarose gels by electrophoresis, followed by sequencing and alignment. The quantitative RT-PCR was carried out on an Applied Biosystems 7300 Real-Time PCR System, using SYBR Premix Ex Taq kit . The relative expression level of each gene was calculated using 2-ΔΔt method  according to the manufacturer’s protocol. The quality and quantity of RNA samples were assessed on a Quawell Q5000 spectrophotometer . cDNA was synthesized using the PrimerScript method .SRX2789685(ZH30), SRX2789686(JY102), SRX2789687(YC bulk), and SRX2789688(GC bulk).Whole-genome sequencing data for ZH30, JY102, and the two bulks are available in the NCBI Sequence Read Archive under accession numbers 1 plants segregated for cotyledon color. Detailed analysis revealed that the segregation of cotyledon color in seeds of F1 plants all fit a 15:1 (yellow:green) segregation ratio with χ2 value ranging from 0.0008 to 1.79, all well below 3.84, the critical value for a significant difference (Table 1). Moreover, a segregating F2 population (n = 495) was developed and phenotypic evaluation showed that F2:3 lines derived from all 32 plants with green cotyledon in the F2 generation showed green cotyledons. Lines with yellow cotyledon in the F2 produced 228 F2:3 lines with yellow cotyledon and 133 and 102 F2:3 lines segregating in ratios of about 3:1 and 15:1, respectively . All of these results suggested that cotyledon color in this cross was controlled by two genes and that the green cotyledon trait carried by JY102 was recessive.To investigate the inheritance of cotyledon color in soybean, two soybean cultivars, Zhonghuang 30 (ZH30) with yellow cotyledon and Jiyu 102 (JY102) with green cotyledon were used to develop segregating populations. All the hybrid seeds with ZH30 as maternal donor showed the YC phenotype and the seeds of FTM 2500 platform. After filtering, 144.6 Gb of clean data were obtained with average Q20 of 91.8% and Q30 of 85.2% (Table 2), indicating the high quality of the sequencing data.Based on the phenotypic investigation, DNA from 30 individuals with yellow or green cotyledon each was pooled separately into a YC bulk and a GC bulk. DNA of each parental line isolated from leaves of 10 plants was also prepared for sequencing. These four DNA samples were used to construct libraries and subjected to whole-genome sequencing on the Illumina HiSeqAlignment with the Williams 82 reference genome allowed 93.8–94.8% of the clean reads to be mapped. The average sequencing depths for DNA bulks were 53–59 and those of the parental lines were 9–12. More than 89.7% of the genome had at least 1× coverage in all four samples and at least 80.0% had at least 10× coverage in the bulks. SNPs and small indels were first called between parental lines and the Williams 82 reference genome and then putative variations between parental lines were identified by selection SNPs or small indels that were unique to a single parent. Finally, a total of 1,084,921 SNPs and 157,839 small indels were identified between parental lines ZH30 and JY102.Supplementary Figure S1). Fisher’s exact test was also performed for the YC and GC bulks at each SNP locus and the average P-values for SNPs located in each sliding window were calculated. Although many peaks were identified in SNP-index plotting of the YC and GC bulks, only two major peaks with statistical significance were identified in Δ(SNP-index) association analysis and were assigned as the candidate regions of the two genes controlling cotyledon color in this population . These two candidate regions (designed as qCC1 and qCC2) were located in the intervals 54.15–56.83 Mb on chromosome 1 and 0–2.68 Mb on chromosome 11, displaying an average SNP-index > 0.9 in the GC bulk and an average P-value < 0.05 . For the candidate region of qCC1, 2,843 SNPs between parental lines were identified and 2,284 of them had a SNP-index of 1.0 in the GC bulk (indicating that the entire short sequence reads contained genomic fragments derived from JY102). Of all these SNPs, 251 could result in changes in coding sequences (Supplementary Table S2). The candidate region of qCC2 contained 1,237 high-quality SNPs between parental lines, of which 870 SNPs had a SNP-index of 1.0 in the GC bulk and 102 could result in changes in coding sequences (Supplementary Table S3).To identify markers associated with cotyledon color, SNP-index of each SNP locus in YC and GC bulks was calculated using high-quality SNPs, those with quality score ≥ 100 and read depth ≥ 10. The average SNP-index in YC and GC bulks and Δ(SNP-index) between the GC and YC bulks across a 2-Mb genomic interval were measured using a 10-kb sliding window and plotted for all 20 chromosomes of the soybean genome , validating the accuracy of the NGS mapping result.To validate the candidate regions identified by BSA mapping, 11 SSR markers polymorphic between two parental lines in candidate regions were used for identifying genotypes in the FqCC1 locus, 70 randomly selected seeds with green cotyledons and 20 seeds with yellow cotyledons were used for identification of recombinants between markers BARCSOYSSR_01_1599 and 01_1633. The result showed that 19 plants had recombinant exchanges at either end of the qCC1 region. Six polymorphic markers were then developed between these two markers and subsequent marker–phenotype analysis allowed progressive refinement of the qCC1 region into a 30.7-kb region between markers BARCSOYSSR_01_1606 and 01_1609 . Four genes were annotated in the candidate region of qCC1 according to the Wm82.a2.v1 gene set of the soybean reference genome. The candidate region contained 39 SNPs, of which three were located in exons (two synonymous and one non-synonymous variant) of two genes and 18 in introns or UTRs of three genes. The candidate region contained 15 small indels of size 1–10 bp, with two of them located in genes (including an exon of Glyma.01g214600 and an intron of Glyma.01g214700) (Supplementary Table S4).To further delineate the qCC2 locus, 100 randomly selected seeds with green and four seeds with yellow cotyledons were used for identification of recombinants using markers BARCSOYSSR_11_0091 and 11_0130. The identified recombinants were genotyped with eight polymorphic SSR markers and qCC2 was mapped to the interval between BARCSOYSSR_11_0103 and 11_0108. Another three recombinants were identified from other lines and seven SNP markers were used for fine-mapping the qCC2 locus. Finally, qCC2 was mapped between SNP16 and SNP23 in a physical interval of 67.7 kb . This region contained nine annotated genes, in which 15 SNPs and one small indel were identified between parental lines. None of these SNPs altered amino acid sequences of encoded proteins. Although a small indel resulted in a frameshift of the Glyma.11G027800 gene, this gene may not be the candidate gene because this alteration occurs in the non-mutated line ZH30 but not the mutated line JY102 (Supplementary Table S5).For the D1 and D2 genes involved in the stay-green phenotype of soybean are orthologs of STAY-GREEN (SGR) genes from Arabidopsis (qCC1 contained Glyma.01g214600 (D1) and qCC2 region contained Glyma.11g027400 (D2), indicating the accuracy of our results from mapping by sequencing. According to the SNP and indel set between two parental lines, the deletion of T at position 54,555,967 of chromosome 1 led to a frameshift and premature stop codon in Glyma.01g214600, in agreement with the mutation of D1 in Harosoy near isogenic lines . Primers were designed for sequencing the promoter and genic region of Glyma.01g214600 and the result revealed that a total of 11 SNPs and a 10-bp indel were identified in the promoter region of Glyma.01g214600 in JY102. Gene expression analysis also showed that the expression level of Glyma.01g214600 was significantly lower in JY102 than in ZH30 . For the qCC2 locus, amplification of the coding sequence of Glyma.11g027400 revealed that the coding sequence in JY102 was longer than that in ZH30, in consequence of a 322-bp sequence repetition , leading to a premature stop codon. This sequence variation is also in agreement with the mutation of D2 gene identified previously.The bidopsis . Our finArabidopsis and rice  governing cotyledon color. The major advantage of this method is that it can quickly associate loci with candidate genomic regions, greatly reducing workload and time. Together with the rapid identification of genomic regions, a large number of SNPs and small indels have also been identified between parental lines. Causal mutations of candidate genes may also be identified by detailed analysis of SNPs and indels in candidate genomic regions after fine mapping. In our study, two previously reported stay-green genes isolated by homology-based gene cloning appeared in regions identified by fine mapping, further demonstration of the mapping and polymorphism-identification methodology.Although reverse-genetic approaches have become increasingly popular in some species in the last few decades, map-based cloning is still the main approach for identifying and isolating candidate genes for many crops. However, the labor-intensive, time-consuming, and costly development of massive marker sets reduces the application and effectiveness of conventional mapping . In compqCC1 (Glyma.01g214600), leading to frameshift mutation and loss of function, has also been identified from analysis of the sequencing data. The direct discovery of causal variants of genes in fine-mapping regions dramatically accelerates the speed of candidate gene cloning. However, large-fragment insertions or deletions such as the variant of the D2 gene still cannot be identified directly from the sequencing data, but must be discovered by sequencing of candidate genes in the mapping regions and further validated.The physical sizes of two mapping regions, of 2.68 Mb in both chromosomes 1 and 11, are still very large even with >50× genome coverage for two bulks, a result consistent with those of other reports in soybean . These gYG and L-JQ conceived and designed the experiments. JS, ZLi, and ZLiu performed the experiments. YG and JS analyzed data, YG, JS, and L-JQ wrote the manuscript. All authors read and approved the manuscript.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.


In this study, we examined parts of six growth genes  as specific primers for two rabbit lines  using nucleotide sequence analysis, to investigate association between detecting single nucleotide polymorphism (SNP) of these genes and body weight (BW) at market.Each line kits were grouped into high and low weight rabbits to identify DNA markers useful for association studies with high BW. DNA from blood samples of each group was extracted to amplify the six growth genes. SNP technique was used to study the associate polymorphism in the six growth genes and marketing BW (at 63 days) in the two rabbit lines. The purified polymerase chain reaction products were sequenced in those had the highest and lowest BW in each line.Alignment of sequence data from each group revealed the following SNPs: At nucleotide 23 (A-C) and nucleotide 35 (T-G) in MC4R gene (sense mutation) of Alexandria and V-line high BW. Furthermore, we detected the following SNPs variation between the two lines: A SNP (T-C) at nucleotide 27 was identified by MC4R gene (sense mutation) and another one (A-C) at nucleotide 14 was identified by GHR gene (nonsense mutation) of Alexandria line. The results of individual BW at market (63 days) indicated that Alexandria rabbits had significantly higher BW compared with V-line rabbits. MC4R polymorphism showed significant association with high BW in rabbits.The results of polymorphism demonstrate the possibility to detect an association between BW in rabbits and the efficiency of the used primers to predict through the genetic specificity using the SNP of MC4R. Rabbit has its importance as supplier of meat and it has become one of the necessary sources of food in some country. Molecular techniques in rabbit’s field become very important tools for the detection of markers linked to economical traits . In breeLittle researches have been made to improve rabbit’s genetic potentiality for high body weight (BW) using genotype based on single nucleotide polymorphisms (SNPs) of candidate genes. The candidate gene method is a powerful process for finding specific loci responsible for genetic variation related with productive traits of interest in animal species . Some ofThe present investigation had been carried out to detect the genetic variation within six growth genes  in two rabbit lines namely V-line and Alexandria line using nucleotide sequence analysis, to investigate association between detecting SNP of these genes and BW at market which can be used as specific markers in marker assisted selection (MAS) for high BW in rabbits.The approval from the Institutional Animal Ethics Committee to carry out this study was not required as no invasive technique was used.® Scientific Corporation, EU) included three main steps: Initial denaturation at 95°C for 5 min, followed by 40 cycles, denaturation at 95°C for 30 s; annealing at 45°C for 30 s and lasted by extension at 72°C for 1 min then an extension cycle at 72°C for 8 min. Amplicones were separated on 1.5% agarose gel, stained with ethidium bromide and visualized under UV transilluminator. The determined DNA bands on the agarose gel were processed for data analysis using GelAnalyser software [MSP1. The restriction fragment length polymorphism (RFLP) was carried out on PCR product according to Zhou et al. [http://www.codon-code.com/aligner).V-line and Alexandria line rabbits have been used from the stocks available at rabbitry of the Poultry Research Center of Alexandria University, Egypt, during season 2015/2016. V-line is a synthetic maternal line originated at the Department of Animal Science, Politecnica University, Valencia, Spain . Rabbitssoftware . The ampu et al. . The purData of individual BW (g) at 63 days of age (market age) are expressed as least square means ± standard error of the mean (SEM). Statistics differences between/within the two lines were determined by ANOVA followed by Duncan’s multiple range test using Statistical Package for the Social Sciences (SPSS) statistical program . The effijkl = µ + Li + Gj + BWk + eijklYWhere, Y is the dependent variable, µ is the overall mean of observations, L is the fixed line effect, is the fixed genotype effect, BW  is the covariate, and e is the residual error.MSP1 did not produce restriction fragments. The purified PCR products were sequenced in those had the highest and lowest BW in each line and between lines. Alignment of sequence data of 20 rabbits from each group revealed the following SNPs: At nucleotide 23 (A-C) and nucleotide 35 (T-G) in MC4R gene (sense mutation) of Alexandria and V-line high BW. Furthermore, we detected the following SNPs variation between the two lines: A SNP (T-C) at nucleotide 27 was identified by MC4R gene (sense mutation) and another one (A-C) at nucleotide 14 was identified by GHR gene (nonsense mutation) of Alexandria line. Least square means and standard errors (LSM ± SEM) of individual BW at market (63 days) of V-line and Alexandria line rabbits shown in This study aimed to detect genetic polymorphism associated to high BW at market in two rabbit lines named V-line and Alexandria using SNP analysis. We investigated parts of six growth genes  for rabbit. All primers were amplified and yielded distinct polymorphic PCR profiles at MW ranged from 200 to 400 bp . RFLP anet al. [The use of genotype information as an aid to selection can be a rapid and accurate way to improve the genetic progress in rabbits, especially in the case of high-cost phenotypic recording for many economic traits in rabbit. This study aims to enhance selection efficiency on growth performance of rabbits. We used candidate genes approach to identify DNA markers whose variability could be associated with high BW at market. We examined parts of six growth genes GH, MC4R, GHR, PGAM, MSTN, and FGF for two rabbit lines named Alexandria and V-line. From the nucleotide sequence analysis of the whole amplified fragments and the analysis of the least squares of individual BW at 63 days, we observed a significant association between the MC4R SNPs and high BW of rabbits at market (63 days of age). The two MC4R SNPs detected in high BW rabbits within the two lines may induce the change of the activity or function of MC4R gene in rabbit. These base mutations are sense mutations and lead to transforming amino acids: Valine to leucine and leucine to arginine. These results were in agreement with Fontanesi et al.  who idenMoreover, the important role of MC4R gene is focused on the biological regulation which is based on epigenetic mechanisms. The rabbit MC4R gene has been already included on chromosome 9 of the Orycun 2.0 rabbit genome version. It is mainly expressed in the hypothalamus (MC4R), in which plays an area responsible for controlling appetite (feed intake) and satiety, which in turn effect on BW. Mutations in MC4R are the most common genetic cause of reduce gene function and some eliminate function altogether.et al. [et al. [et al. [et al. [In addition, the results declared that all band sequences resulting from the six primers possess 100% identity between the two lines using SNP analysis except MC4R and GHR which indicated differences. Sense mutation of MC4R leaded the change of arginine to lysine. Nonsense mutation of GHR did not result amino acids change. The premier step during the biological process of GH is binding with the GHR, then activating the expression of insulin-like growth factor 1 which influences the growth of animal . The GHR [et al.  observed [et al.  indicate [et al.  reportedAccording to the results of current research, we found a significant association between MC4R gene polymorphism and high BW at market in rabbits. The results of SNP demonstrate the efficiency of used associated genes to predict through the genetic specificity using the SNPs of MC4R. Moreover, the results are effective in rabbit selection for high BW at market.KE and SAA carried out the experiment design participated in practical work and wrote the manuscript. KE had the primary responsibility for the content of the manuscript. All authors read and approved this manuscript.


Oryza sativa L.) is the only cereal crop that possesses the ability to germinate under flooded or other oxygen-deficient conditions. Rapid elongation of the coleoptile is a perfect response to flooding during germination, with coleoptile length differing among various rice varieties. Despite multiple studies have uncovered valuable information concerning this trait by focusing on the physiological metabolism of oxygen stress, the underlying genetic mechanism still remains unknown. In the present study, we screened coleoptile lengths of 432 indica varieties germinated in two environments  and found more variation existing in flooded coleoptile length (FCL) rather than in normal coleoptile length (NCL). With the phenotypic data of NCL, FCL and FTI (flooding tolerance index), a genome-wide association study was performed by using 5291 single nucleotide polymorphism (SNP) markers. We detected 2, 11, and 9 significant SNPs under a mixed linear mode for NCL, FCL, and FTI, respectively. Of these SNPs, five were shared by FCL and FTI. Haplotype and phenotype effect analysis on the highest ranking locus indicated one of the two haplotypes contributed to coleoptile elongation remarkably. To better understand the controlling gene of this locus, reported expression profile data was applied. We focused on LOC_Os06g03520, a candidate gene which was highly induced by anoxia (∼507 fold). Sequence analysis in 51 varieties demonstrated Hap.2 associated perfectly with flooding tolerance. Further studies on this gene may help explore the molecular mechanism of rice flooding tolerance during germination. We believe our discoveries may conduce to isolating major genes and aid the improvement of flooding tolerance in modern breeding programs.Rice ( Oryza sativa L.) production in rain-fed lowlands areas of south and south-east Asia  genes at Sub1 locus, enables mature rice plants to survive 10–14 days under complete submergence stress  and SLR1 Like-1 (SLRL1) to suppress the GA-mediated growth regulation and diminishes other GA induced genes expression . Germination was allowed to proceed at 30°C in darkness for 10 days in an artificial climate chamber. Three replicated experiments were implemented for both control and flooded germination. After germination, coleoptile length was measured and data was accurate to millimeter.On the basis of a previous study, we selected 432 ed array  (Supplemed array . Rice seThe flooding tolerance index (FTI) was calculated as formula: flooded coleoptile length (FCL)/normal coleoptile length (NCL). Descriptive statistical analysis was performed in Microsoft Office Excel 2007. The analysis of variation (ANOVA) was carried out to evaluate the effects of genotype (G), and genotype × environment (G × E) using general linear model under SAS 9.0 . Variation and correlation analysis (SAS 9.0) were performed respectively to evaluate the coefficient of variation (CV) and estimate the relationship between NCL and FCL.Principal component analysis was performed based on Nei’s genetic distance using Powermarker (ver. 3.25)  and NTSYy = Xα+ Pβ + Kμ + e, while X and y were vectors of genotype and phenotype data, α was the SNP effects, β represented the effects of population structure, μ was the vector of kinship background effects, e corresponded to residual effects, P was the PCA matrix relating y to β, K represented the related kinship matrix  database2. For qRT-PCR module, total RNA was extracted from rice coleoptiles after germination for 2 or 6 days using a MiniBEST Plant RNA Extraction kit . First-strand cDNA was synthesized using PrimeScript RT Master Mix . To ensure cDNA quality following reverse transcription, a pair of primers straddling intron was used to detect genomic DNA. Then qPCR was performed in a two-step reaction using SYBR Premix Ex TaqII (Tli RNase Plus)  on an Applied Biosystems 7500 Real-Time PCR system  with a pair of specific primers .The LD heatmap around the significant SNPs in GWAS was constructed using the R package “LDheatmap”  and the r2 > 0.6 . CandidaThe full length of candidate gene and its promoter region were amplified by polymerase chain reaction (PCR) with two pairs of specific primers . Then PCR productions were purified and sequenced. All the sequences were edited and assembled using SeqMan in the package DNASTAR Lasergene . Sequence analysis was performed using software MEGA6  and the indica varieties were screened for NCL and FCL (Supplementary Table Table 1). Moreover, the CV was 0.22 for NCL and 0.52 for FCL, revealing that more abundant variation existed in FCL. The correlation analysis indicated that there was merely low relationship between NCL and FCL . The FTI was calculated according to the ratio of phenotypic data of two environments. The results presented in Table 1 showed that FTI ranged from 0 to 1.59, with a mean value of 0.65. The CV was 0.54, which was even higher than that of FCL.Totally, 432 indica varieties and lead to false associations, we performed PCA based on Nei’s genetic distances  in bar chart. Most PCs explained very small proportion of genetic variation except the first one, suggesting a weak genetic structure of our population. According to our calculations, most varieties had no meaningful relationship except 2.5% of varieties possessed the kinship value larger than 0.5, which indicated the relative kinship of our population might have no strong influence on GWAS . The analysis of genetic structure and relative kinship made it clear that our indica population was qualified for GWAS.Considering that population structure may still exist in P = 0.001, we detected totally 22 significant SNP loci associated with NCL, FCL, and FTI . Only two SNPs were detected under NCL, which were much less than the others. Moreover, none of these two SNPs were detected in the other two associations. These results confirmed that no correlation existed between the coleoptile length observed in control and flooded environments. The comparison of association between FCL and FTI identified five common SNPs, including two SNPs on chromosome 6, two SNPs on chromosome 7 and one SNP on chromosome 11 . These consistent loci revealed the similar contribution of FCL and FTI. The physical distance of two significant SNPs on chromosome 6 was only 211-kb, which suggested that they might belong to the same genetic loci. Considering its contribution to P-values and the steady identification by both FCL and FTI, this locus should be regarded as a major controller of rice flooded germination tolerance.Association mapping was performed under MLM with PC matrix and kinship matrix as covariates. At the thresholds of Table 2). The significant SNP seq-rs3210 on chromosome 7 in our study was found to locate closely to a genetic locus which was detected in another association study . This result demonstrated the close linkage of these two significant SNPs. Subsequent calculation indicated phenotypic effect of these two haplotypes differed significantly. As was shown in Figure 3B, Hap.B associated with tolerant types significantly (with larger FCL and FTI). To accurately estimate the linkage region on the genomic map, pairwise LD correlations were calculated. With r2 = 0.6 as the threshold, we obtained a 875-kb candidate block containing seq-rs2699 and seq-rs2701 . According to the reference genome, there were 158 genes in this region enabling us to isolate the most likely candidate gene.The highest ranking of SNP, seq-rs2699 on the beginning position of chromosome 6, was firstly found to control rice FCL. In our study, this SNP was detected under both FCL and FTI. Detailed analysis on this locus was conducted to investigate the genetic mechanism behind. There were two significant SNPs on this locus which suggested the theoretic haplotypes should be at least four types. However, statistical analysis indicated that only two haplotypes accounted for most varieties  . The results observed in both four indica varieties, that LOC_Os06g03520 was highly expressed in flooded coleoptiles but not in normal coleoptiles after germination for 2 or 6 days, suggested its potential possibility for controlling rice FCL .To narrow down the candidate gene numbers, we took advantage of a previously reported expression profile which was obtained from a mination . With thFigure 4A). Haplotype analysis revealed 11 haplotypes among these accessions . To our surprise, almost every variety of Hap.2 was defined as tolerant type (with larger FCL and FTI) , we concluded that both FCL and FTI could equally represent rice response level to flooding.Coleoptile elongation has been regarded as a major response to anaerobic stress . But fewOsRAMY3D. The major function of OsRAMY3D is to promote starch amylolysis under energy deficiency  close to OsTPP7 at a distance of 1.4 MB. Because many SNPs around this gene were still not detected, we inferred that local LD might cause the misleading assocation and lead to the large distance.According to the results of GWAS, we have detected some SNPs close to known genes. For example, seq-rs3970 was localized close to the widely known oxygen-deficiency related gene – ficiency . The posjaponica cultivar, the significant difference was also detected in four indica cultivars . The subsequent analysis demonstrated our inference to be correct. The consistence of gene sequence and phenotype confrimed LOC_Os06g03520 was the most likely candidate. Unlike most genes controlling anerobic germnation was annoated to involve in energy metabolism pathway (OsTPP7 and OsCIPK15), LOC_Os06g03520 was annoated as DUF domain containing protein. Untill now, more experiential evidence was required to verify its molecular function.The differential expression profile strategy was an important approach to study genes associated with a given trait . We tookSince its first report, GWAS has been widely carried out in rice genetic researches. Most studies were unable to identify candidate genes because of the large LD decay distance in rice. In our study, we used a gene chip carried 5291 SNP markers which was much less than those in other researches. Based on the consequence of MZ, QL, and XW: designed the research experiments. MZ, QL, and XN: performed the phenotyping. QX, YF, and CW: carried out the genetic studies. WW, XY, HY, and YW: managed the materials. XW: designed the overall project. MZ and QL: analyzed the data and drafted the manuscript. SW and CW: helped to revise the manuscript. All of the authors read and approved the final manuscript.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.


The genetic structure analysis revealed that these sesame germplasm accessions were structured primarily along the basis of their geographic collection, and that an extensive admixture occurred in the panel. The genome-wide linkage disequilibrium (LD) analysis showed that an average LD extended up to ∼99 kb. The genetic diversity and population structure revealed in this study should provide guidance to the future design of association studies and the systematic utilization of the genetic variation characterizing the sesame panel.The characterization of genetic diversity and population structure can be used in tandem to detect reliable phenotype–genotype associations. In the present study, we genotyped a set of 366 sesame germplasm accessions by using 89,924 single-nucleotide polymorphisms (SNPs). The number of SNPs on each chromosome was consistent with the physical length of the respective chromosome, and the average marker density was approximately 2.67 kb/SNP. The genetic diversity analysis showed that the average nucleotide diversity of the panel was 1.1 × 10 Sesamum indicum L., 2n = 26), a member of the Pedaliaceae family, is one of the most ancient oil crops and it is grown widely in both tropical and subtropical areas (Arachis hypogaea), soybean (Glycine max), oilseed rape (Brassica napus), sunflower (Helianthus annuus L.) and other oilseed crops, sesame seeds innately contain a higher oil content (∼55% of dry seed), in which the oleic acid (18:1) (32∼46%) and linoleic acid (18:2) (42∼59%) components follow a near 1:1 ratio  based on next generation sequencing (NGS) are more useful than conventional markers. This is because SNPs are extremely abundant, and the number of unbiased SNPs can nowadays easily been obtained for non-model species . With thIn this study, 366 elite germplasm accessions were selected from the HSRC-HAAS primary core collection and genotyped using the SLAF-seq method to evaluate their usability as an association-mapping panel. Our objectives were as follows: (a) to assess the genetic diversity as represented by the 366 sesame germplasm accessions; (b) to calculate the characteristics of the population structure; and (c) to estimate the linkage disequilibrium (LD) patterns occurring in the sample of sesame germplasm.To assemble the association-mapping panel consisting of 366 germplasm accessions (taken from the HSRC-HAAS collection), we used 329 accessions coming from 18 provinces in China, plus another 37 accessions that were introduced from 11 countries , United States) and Hpy166II , were used to digest the genomic DNA. Pooled samples were separated by 2% agarose gel electrophoresis, and those fragments ranging from 264 to 364 base pairs (with indexes and adaptors) in size were excised and purified using a QIAquick gel extraction kit . The gel-purified products were diluted and subjected to pair-end sequencing on an Illumina HiSeq 2000 platform  at Beijing Biomarker Technologies Corporation1.Genomic DNA was extracted from the young leaves of each accession by using the CTAB method  with sliThe high-quality paired-end reads were aligned with an improved Zhongzi No. 13 genome assembly  coupled to Burrows–Wheeler Aligner (BWA) software  that wasr2) threshold to 0.1 for the SNP pairs in a sliding window of 50 SNPs—by using PLINK v.1.07 (K) set from 1 to 10. The burn-in time and the Bayesian Markov Chain Monte Carlo (MCMC) replication number were both set to 100,000 for each run. The most likely K value was determined by the log likelihood of the data [LnP(D)] and an ad hoc statistic, ΔK, in the program Structure Harvester  were calculated by using the software Arlequin v.3.52  of the selected SNPs was performed with the EIGENSOFT software . To consn v.3.52 . AccordiT > 0.25 .r2. For all pairs of SNPs, the r2 was calculated using the PopLDdecay v.3.26 program2.Genome-wide LD was estimated in the total panel and for each subgroup (as determined by the population structure) by pairwise comparisons among the 44,109 SNP markers [missing rates <0.30 and minor allele frequency (MAF) ≥0.05] using A total of 81.2 Gb of sequence data, including 902.36 million pair-end reads, were generated by the SLAF-seq when applied to the 366 sesame germplasm accessions. The Q30 ratio and guanine-cytosine (GC) content were 84.82% and 39.47%, respectively . The largest number of SNPs was found on LG5  followed by LG3 , whereas the smallest number of SNPs occurred on LG4 . The number of SNPs on each chromosome was consistent with the physical length of the respective chromosome. The average marker density was approximately 2.67 kb/SNP. LG6 had the lowest SNP marker density (3.96 kb/SNP), and LG3 had the highest marker density (1.70 kb/SNP).The 89,924 high-quality SNPs covered all 13 linkage groups (LGs) ] increased continuously and an inflection was evident when K increased numerically from 1 to 2 . Thus, the most likely numerical value of K was 2. The number of subgroups (K) was further validated by the second-order statistics of ΔK. The ΔK value showed a peak at K = 2 , which supported the classification of the panel into two major subgroups .A subset of 12,178 SNPs was selected for analysis of the population structure. The hierarchical population structure was determined for the entire panel via the model-based STRUCTURE program, but without providing any information per se on the population structure. As n = 2), Japan (1), India (2), Burma (2), Thailand (1), Arab Emirates (2), Mexico (1), Mozambique (2), and Guinea (1). The remainder of Pop 1 comprised 12 provinces in China (eight Southern provinces and four Northern provinces). The accessions of Pop 2 were mainly collected from the Northern areas in China; specifically, just three accessions came from South Korea, Japan, and United States, whereas 108 accessions came from nine provinces in China (eight Northern provinces and one Southern province). For the Mixed group, its accessions were collected from both the Southern (23 from five provinces) and Northern areas (68 from five provinces) in China, along with 20 accessions coming from Japan (12), Burma (3), Mozambique (1), Arab Emirates (1), United States (2), and Thailand (1).When using a probability of membership threshold of 70%, 144 and 111 accessions were respectively assigned into the two subgroups, Pop 1 and Pop 2, while the remaining 111 accessions were classified into a mixed subgroup (Mixed) . Therefore, the sesame panel along the PC1 axis could be separated into Type 1 (a Southern-area subgroup) and Type 2 (a Northern-area subgroup) subpopulations  corresponding to the three subgroups  as inferred by using STRUCTURE.The PCA was done to further assess the population subdivisions. PC1 explained 10.10% of the genetic variation found, while PC2 and PC3 explained 6.77 and 4.40% of the variation, respectively. There was a significant correlation between PC1 and distributional latitudes of the accessions  while those accessions belonging to the Mixed group were distributed across the whole phylogenetic tree .The neighbor-joining phylogenetic tree was built to search for genetic relationships among the sesame accessions in the panel. This panel was grouped into two recognizable clusters—blue vs. green/red clusters shown in FST, among the subgroups was 0.15 highly significant at P < 0.0001. The corresponding FST values between the subgroups ranged from moderate, for Pop 1 vs. Mixed  and Pop 2 vs. Mixed , to very strong  for that of Pop 1 vs. Pop 2.The genome-wide data set of 89,924 SNP markers with an MAF ≥ 0.01 was used to estimate the genetic differentiation for the panel. The AMOVA results indicated that 14.92% of the total genetic variation occurred among the subgroups. While a larger amount of variation (63.06%) was among individuals within the populations, 22.03% of variation was found within the individuals. The measure of population differentiation, Table 1). The ranges for the estimates of gene diversity in Pop1, Pop2, and the Mixed were 0–0.66, 0–0.66, and 0–0.67, respectively, and their corresponding averages were 0.17, 0.11, and 0.17. On the basis of these SNP data, the sequence diversity (π) was estimated as 1.1 × 10-3 for all of the sesame accessions, and as 1.0 × 10-4, 2.7 × 10-4, and 3.6 × 10-4 for Pop 1, Pop 2, and the Mixed group, respectively.A total of 89,924 SNPs were used to study the genetic diversity of the sesame panel. The average gene diversity of the panel was 0.17; the highest was 0.25 on LG9, followed by LG4 (0.20), with the lowest value found (0.13) on LG1 and LG10 , followed by Pop 2 (290), while the Mixed accessions had the smallest number of group-specific SNPs (157). In the pairwise comparisons of Pop 1, Pop 2, and the Mixed group, the number of SNPs unique to the Pop 1  vastly exceeded those unique to the Pop 2 , though the number of group-specific SNPs  in Pop1 was just a little more than twice that in the Mixed group , whereas the number of SNPs unique to the Mixed group  was far greater than those unique to the Pop 2 .To reveal the genetic differences among the different groups of the sesame germplasm, a comparative analysis of their allele frequencies was performed. The Pop 1 accessions had the largest number of group-specific SNPs (The low level of heterozygous genotypes (heterozygosity = 0.05) was consistent with the inbreeding nature of sesame. The average heterozygosity was higher in Pop 2 (= 0.05) than in Pop 1 (= 0.04). The genetic distance among the 366 sesame accessions averaged 0.17, with a range of 0.01–0.42. Nonetheless, Pop 2, which included accessions collected from the Northern areas in China, had the lowest average genetic distance, at 0.11, as well as the smallest range, from 0.01 (C020 and C022) to 0.16 (C027 and C125). By contrast, Pop 1 and the Mixed group had a similar average genetic distance , which ranged from 0.01 (C150 and C255) to 0.40 (C278 and 377), and from 0.05 (C245 and C247) to 0.30 (C076 and C181), respectively.r2 decreased to 0.37 from the initial value of 0.74 was ∼99 kb, such that the r2 dropped to 0.2 at ∼234 kb and to 0.1 at ∼467 kb . To obtain more details of this LD behavior, the chromosome-wise LD between the SNP pairs was also calculated. The highest maximum average LD (r2 < 0.80) was found for LG1, and the lowest maximum average LD (r2 = 0.66) was found for LG7. Except for the latter, all of the other LGs showed a maximum average LD that exceeded 0.70. The highest LD decay was ∼255 kb for LG6, followed by that seen for LG7 (∼107 kb), whereas the lowest LD decay (∼66 kb) was observed for LG5 . When the population-wise LD was calculated, the maximum average LD (r2 = 0.75) occurred in Pop 2, for which the LD decay was ∼83 kb. However, the minimum average LD was found to be very low (0.04) for Pop 2. The maximum average LD for the Mixed group was comparatively lower (0.72), with the decay to half to its initial value occurring at ∼86 kb. The average minimum LD was also very low (0.04) for the Mixed subgroup. The maximum and minimum average LD values for Pop 1 were 0.74 and 0.06, respectively. Compared with the LD decays of the two other subgroups, Pop 1 had the highest rate of LD decay, at ∼100 kb .In this panel, the genome-wide LD decay at which The genetic diversity housed in germplasm collections is an important foundation for crop improvement and a key component of plant conservation and breeding strategies . For ses-3, a value similar to that reported by both -3) of sesame was lower than that of rice  we examined amounted to ∼68.04%, which was similar to those reported for the genomes of Arabidopsis and Alfalfa . The higSesame is a mostly self-pollinated plant, and hence it is likely that all of the sesame accessions in the present study had been held for many generations via self-pollination. Their genomes are thus expected to be mostly homozygous. In line with this expectation, the average heterozygosity in the sesame panel was 0.046; this suggests that the accessions we used were quite close to being inbred lines. Hence, the accessions selected from the HSRC-HAAS core collection are useful and suitable for investigating multiple phenotypic traits in a multi-plot field test over several years and to also carry out GWAS.K value of 2 was determined by both the LnP(D) and ΔK. By using a 70% probability of membership threshold, the panel was successfully divided into three subgroups . Most of the accessions belonging to Pop 1 came from Southern areas whereas the accessions of Pop 2 came from Northern areas in China. The remaining 111 accessions were all collected from both Southern and Northern areas, thus showing the substantial exchange of germplasm that has occurred in sesame.The complex breeding history of many important crops and the limited gene flow in most wild plant populations have created complex structures within their germplasms . DetaileK = 2. However, the PCA was unable to capture much variance in the PC1 axis, which suggests that the sesame panel contained a number of admixed lines and a low layer of population structure.The PCA was performed to examine the population structure of the sesame panel. The significant correlation found between PC1 and the latitudinal distribution of the sesame accessions—similar to findings by FST value, calculated here as 0.15, which demonstrates the moderate genetic differentiation characterizing this panel. The latter finding should favor the detection of gene effects on the power of structure-based association studies.According to the AMOVA results, 14.92% of the marker variation was explained by the population structure of the sesame panel. This result suggests the absence of a complicated population structure in our association-mapping panel. The differentiation between the subgroups was further validated by the r2 dropped to 0.37 from its initial value of 0.74—and much lower than that found by The decay of LD over a known genetic distance is an important parameter for determining the number and density of molecular markers deemed appropriate for GWAS and selection strategies . Sesame,The sesame accessions used in this study were selected from the HSRC-HAAS core collection and so they mainly encompassed landraces and cultivars. All the accessions displayed good adaptation to most of the current growing conditions in China; this means that field experiments could be robustly conducted to test multiple phenotypic traits in different places throughout China. With the aim of evaluating the potential of a panel for an association analysis, we studied the population diversity and structure of a sesame panel consisting of 366 accessions. Our results show that this sesame panel is a representative sample, and one therefore suitable for further association mapping, given the modest level of nucleotide diversity and the slight population stratification that favors GWAS and a false association control.HM and YL developed the association-mapping panel; CC and HM performed the data analysis; CC wrote the manuscript, HM revised the manuscript; HZ and YZ designed and supervised the study. All authors read and approved the final manuscript.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.


Salmonella enterica serovar Typhimurium and its monophasic variants during a 7-month surveillance period in Denmark. We reanalyzed and defined 8 previously characterized outbreaks from the phylogenetic relatedness of the isolates, epidemiologic data, and food traceback investigations. All outbreaks were identified, and we were able to exclude unrelated and include additional related human cases. We were furthermore able to link possible food and veterinary sources to the outbreaks. Isolates clustered according to sequence types (STs) 19, 34, and 36. Our study shows that core-genome single-nucleotide polymorphism analysis is suitable for surveillance and outbreak investigation for Salmonella Typhimurium (ST19 and ST36), but whole genome–wide analysis may be required for the tight genetic clone of monophasic variants (ST34).Whole-genome sequencing is rapidly replacing current molecular typing methods for surveillance purposes. Our study evaluates core-genome single-nucleotide polymorphism analysis for outbreak detection and linking of sources of  Salmonella is responsible for tens of millions of human infections worldwide each year –based WGS analysis was suitable for surveillance purposes (Whole-genome sequencing (WGS) is a widely used technique for molecular subtyping of bacteria, and it is replacing the more laborious current molecular typing methods. The vast amount of data provided by this method not only enables high-resolution typing for surveillance but also provides valuable additional data regarding further characterization of emerging clones based on genetic differences and evolutionary studies. Several studies have proven WGS-based typing to have an enhanced discriminatory power in comparison to current molecular typing methods used for Salmonella Typhimurium and its monophasic variants is conducted at Statens Serum Institut  and the National Food Institute (food and veterinary isolates). Surveillance is based on serotyping, drug-susceptibility testing, and multilocus variable-number tandem-repeat analysis (MLVA). The aim of our study was to evaluate WGS as a typing method for routine surveillance of Salmonella Typhimurium and its monophasic variants. To do so, we selected an already typed collection of strains from 2013 and 2014 and reanalyzed them to redefine outbreaks and detect outbreak sources based on core-genome SNP analysis.In Denmark, surveillance of Salmonella Typhimurium and its monophasic variants for this study . The food and veterinary isolates were isolated from swine, poultry, cattle, and feed in 2010, 2013, and 2014.We analyzed all human isolates by using WGS at Statens Serum Institut’s Department of Microbiology and Infection Control and sequenced food and veterinary isolates at the Technical University of Denmark’s National Food Institute. We sequenced isolates by using an Illumina Miseq . All sequences were de novo assembled and sequence type (ST) determined. We identified core-genome SNPs by using an in-house SNP pipeline, and we then analyzed a selected subgroup of isolates by using the SNP pipelines NASP . We asseSalmonella Typhimurium 14028S (ST19) as the reference genome. The SNP analysis resulted in 14,326 SNPs. We constructed a maximum-parsimony tree from the core-genome SNPs and observed 3 ST-specific groups  compatibility gene related to plasmids, 2 genes associated with plasmid regulation (copG and rop), and 1 hypothetical protein (99% protein similarity with predicted plasmid protein identified in an enteropathogenic Escherichia coli 0119:H6 strain [GenBank accession no. AP014807.1]). The region was located on an entire single contig with a higher average read coverage and ≈10% lower GC-content than the average of the genome. The nearest neighbor isolates considered for inclusion in the outbreak did not harbor the plasmid-related region and were not included in the final definition (A large outbreak (outbreak E) was detected in 2014; our collection included 24 isolates from that outbreak. Of the 24 isolates, 23 clustered with few SNP differences . Becausefinition .Our results showed that all 19 isolates defined in 2014 as part of outbreak F clustered with 0 to 3 SNPs differences. Three additional human isolates  clustered with 0 SNPs to cases and were included in the new cluster definition. The nearest neighbor isolate was located 20 SNPs away, and the outbreak could clearly be defined. Outbreak and food traceback investigations identified beef as the most likely source. We confirmed 2 samples from companies with 0 to 3 SNPs differences with human cases ; Table 2Likewise, outbreak G was clearly defined by the SNP analysis. Nine human isolates clustered with 0 to 2 SNPs and separated from nearest neighbor with 31 SNPs. Five isolates previously included were distantly related to the cluster and not included in the new outbreak definition ; Table 2Last, all 5 isolates previously defined in outbreak H clustered with 0 SNPs. Consumption of a specific swine product was reported by 2 case-patients, and a food sample from the specific product clustered with 0 SNPs ; Table 2We examined the influence of the choice of reference genome used in the SNP analysis on the cluster formation. As mentioned previously, we analyzed each ST group and each outbreak separately by using an internal de novo assembled reference genome for each ST group and outbreak. We evaluated the size of core-genome used, percentage of reference genome used, and number of called SNPs . Our resSalmonella Typhimurium and its monophasic variants. We were able to recover the 8 previously defined outbreaks based on the SNP analysis, epidemiologic data, and food traceback investigations. With the analysis, we could exclude unrelated human isolates and include related isolates not previously defined in the outbreaks based on MLVA. Furthermore, we were able to link possible sources to the outbreaks and reject previously suspected food and veterinary sources. In 4 out of the 8 outbreaks, we could identify the likely source of the outbreak as related to swine. In 1 outbreak, consumption of beef product was confirmed. The remaining 3 outbreaks were not linked to any known sources. In Denmark, Salmonella Typhimurium and its monophasic variants are commonly isolated from swine or pork , whereas ST36 is located in eBG138, having 3 out of 7 alleles identical with eBG1 , choice of reference genome, and sequence quality need to be addressed and taken into account when setting up a workflow. The reference genome used and the group of isolates analyzed had some, although mostly minor, effect on the SNPs called. However, in general, the choice of reference genome and the selection of isolates analyzed did not change our outbreak definitions. For ST19 and ST34, an overall reference genome, either ST19 or ST34, could be applied. A few extra SNPs were added within outbreaks when analyzing smaller clusters of isolates or outbreaks with an internal reference genome. However, within the tight ST34 group, the few extra SNPs within outbreaks might help in defining some outbreaks more clearly. We observed the largest differences when analyzing the ST36 isolates with a close ST36 reference genome compared with results using an ST19 reference genome. This analysis identified an extra 259 SNPs, and because the ST36 group is distantly related to ST19 and ST34, we recommend using an ST36 reference genome for this group of isolates. Our results show that the SNP analysis is a suitable typing method in relation to surveillance of The parameter that did affect the outcome considerably was the quality of the genomes used. We excluded 6 genomes out of 372 isolates because of poor quality. Including these in the analysis resulted in ≈29% fewer SNPs; therefore, we recommend quality assessment of the genomes before analysis. Last, SNP analysis does not give a static value easily communicated between institutions. Adding new isolates to the analysis results in new calculations and a new core-genome. Potential output differences might occur when different pipelines are used; however, the 3 SNP pipelines used in this study did not result in any major differences in phylogeny and had no influence on the outbreak investigations. A clear consensus of the workflow, quality criteria, and the bioinformatics tools used would resolve practical issues regarding the method. Alternatively, the widely used gene-by-gene approach is faster in real-time surveillance and more easily comparable between laboratories provided identical schemes are used. However, this approach requires expensive software (unless data are uploaded to public repositories) and agreement on schemes and curation of allele databases.Salmonella serovars using SNP analysis for outbreak investigation and detection revealed a variable number of SNP cutoff values for defining outbreak clusters, ranging from 0 to 30 SNPs (Salmonella Typhimurium and its monophasic variants in Denmark.Studies on other Salmonella Typhimurium and its monophasic variants used in an outbreak investigation of Salmonella Typhimurium and its monophasic variants, Denmark.Description of 372 isolates of Salmonella Typhimurium and its monophasic variants, Denmark.Description of sequencing procedures and sequence analysis used in an outbreak investigation of