For establishing marker-trait associations (MTAs), both genome-wide and candidate gene-sequencing based association mapping approaches were conducted using 1,872 markers  and phenotyping data mentioned above employing mixed linear model (MLM) analysis with optimum compression with P3D method and kinship matrix. As a result, 312 significant MTAs were identified and a maximum number of MTAs (70) was identified for 100-seed weight. A total of 18 SNPs from 5 genes  were significantly associated with different traits. This study provides significant MTAs for drought and heat tolerance in chickpea that can be used, after validation, in molecular breeding for developing superior varieties with enhanced drought and heat tolerance.To understand the genetic basis of tolerance to drought and heat stresses in chickpea, a comprehensive association mapping approach has been undertaken. Phenotypic data were generated on the reference set  for drought tolerance related root traits, heat tolerance, yield and yield component traits from 1–7 seasons and 1–3 locations in India  and three locations in Africa . Diversity Array Technology (DArT) markers equally distributed across chickpea genome were used to determine population structure and three sub-populations were identified using admixture model in STRUCTURE. The pairwise linkage disequilibrium (LD) estimated using the squared-allele frequency correlations (r Cicer arietinum L) is the second most important grain legume cultivated mostly on residual soil moisture in the arid and semi-arid regions of the world. It is a diploid member of family Leguminosae with basic chromosome number eight and genome size 738 Mb desi and kabuli. Globally about 80% of total production is contributed by desi cultivars. Further, among chickpea growing countries India alone contributes to 70% of the world's total production −1Chickpea (desi and kabuli chickpea genomes has been completed http://probes.pw.usda.gov:8080/chickpea/) was developed Fusarium wilt, Ascochyta blight and Botrytis gray mold Large scale genomic resources are essential for understanding the genetics of complex abiotic stresses like drought, heat tolerance etc. In the case of chickpea, during last five years >3,000 microsatellites or simple sequence repeats (SSRs) Despite the continuous efforts to enhance the productivity of chickpea, climate changes during past two decades had tremendous influence on the production and productivity MedicagoDuring last two decades, molecular markers have provided greater insights into complex traits in several crop species and the research endeavors in crop improvement shifted from quantitative to molecular genetics with emphasis on QTL identification and adoption of marker-assisted selection (MAS). However, only modest results have been witnessed due to several factors including absence of tight linkage between makers and QTLs, non-availability of mapping populations, and substantial time needed to develop such populations. Further, the QTL mapping approaches cannot make use of the huge variation present across the germplasm available in the genebanks. In addition, the resolution achieved through linkage mapping based on bi-parental mapping population is low compared to population linkage disequilibrium (LD) based association mapping. The genome-wide association study (GWAS) makes it possible to simultaneously screen a very large number of accessions for genetic variation underlying diverse complex traits. In fact, the association studies in other crop species especially in cereals In the present study, both genome-wide and candidate gene-sequencing based association mapping approaches were employed to understand genetics of the two most important complex abiotic stresses “drought” and “heat” and establish significant marker-trait associations (MTAs). For this the chickpea reference set/mini-core collection was genotyped for 1,813 marker loci  and phenotyped at three locations in Africa  and three locations in India . Besides significant MTAs, the present study also provides an in-depth understanding of the genetic diversity, population structure in the reference set and linkage disequilibrium (LD) in genome of chickpea.Cicer spp. including 293 accessions from C. arietinum, 4 accessions from C. reticulatum and 3 accessions from C. echinospermum  in the present study. A total of 917 alleles were detected by 35 SSR markers with an average of 26.2 alleles/marker locus and the polymorphism information content (PIC) values ranged from 0.48–0.96 . FurtherASR; CAP2 gene; ERECTA; sucrose synthase, SUSY; Sucrose phosphate synthase, SPS) were amplified on the reference set. The number of genotypes for which good quality sequences were obtained varied from 79 (ERECTA fragment obtained from 7f–5r primer pairs) to 236 genotypes (SPS gene) out of 300 genotypes attempted SUSY gene which was about 1,600 bp and the shortest sequence was obtained for SPS gene with 312 bp. Varying number of SNPs were identified in each gene using DIVEST tool. SNPs were inspected manually for possible sequencing errors and SNPs having clear peaks were considered as true SNPs. For instance, highest number of SNPs (34) was obtained for ASR gene while no SNPs were found in the case of CAP2 gene. In total, 33 SNPs were identified in case of ERECTA gene, of which 13 SNPs (9 transitions and 4 transversions) were in ERECTA (7f–5r) gene fragment and 20 SNPs (10 transitions and 10 transversions) in case of ERECTA (8f–8r) gene fragment. Only 1 indel was observed in case of ERECTA (7f–5r) gene fragment. One indel and 3 SNPs were observed in case of SPS gene sequence  were used for allele mining across mini-core collection (DREB) to 209 (AMADH). Highest number of SNPs (14) was obtained for DREB gene (8 transitions and 6 transversions). Apart from SNPs, 23 indels were also detected. AKIN gene was found to be most conserved with just 2 SNPs (transitions) and 2 indels. A total of 13 SNPs were identified (6 transitions and 7 transversions) in case of AMADH with 3 indels among 209 high quality sequences, while in case of DHN gene 7 SNPs (5 transitions and 2 transversions) were identified among 198 sequence analyzed. For Myb gene only 6 SNPs (1 transition and 5 transversions) were reported with 2 indels across Myb sequences. Average PIC value of SNPs ranged from 0 (CAP2 gene) to 0.43 (CAP2 promoter)  .In order to assess the population structure and number of sub-populations, 85 evenly distributed DArT loci on chickpea genome To understand the genetic diversity in the reference set neighbor joining (NJ) trees were constructed using allelic data from 35 SSRs, 1,156 DArT loci and 651 SNP markers independently as well as all markers combined together, which revealed three major clusters . However2 decays to 0.1–0.2 is considered to be the extent of LD in a species 2; when r2<0.20) was found to decay rapidly with the genetic distance of 5 cM  with optimum compression and P3D in TASSEL 2.01 software was employed for establishing MTAs. Further, to eliminate the false positives Bonferroni correction was used and as a result, a total of 312 highly significant MTAs were identified for 25 agronomically important traits . PhenotyDrought is the major limiting factor to crop production and especially chickpea experiences various kinds of drought stresses depending on the timing and intensity of the water stress relative to the reproductive stage of the crop. Terminal drought alone has been leading to more than 50% yield losses in chickpea. In the context of receding soil moisture, the breeding strategies should focus to enhance the maximum utilization of available soil moisture efficiently; hence breeding efforts should focus on improving root traits that enhance the efficient extraction of soil moisture. A total of 8 root traits were phenotyped for three seasons at Patancheru for gaining insights into the root system . AssociaAmong morphological traits, significant marker trait associations were identified only for plant height (PHT), shoot dry weight (SDW), apical secondary branches (ASB) and basal primary branches (BPB). A total of 47 significant MTAs were identified in the present study for the morphological traits that explained 7.77–38.01% phenotypic variation. Notably, 35 (74.49%) MTAs identified were for PHT . Among 3Three phenological traits days to 50% flowering , days to maturity (DM) and flowering days  were phenotyped for 7–11 seasons in 1–5 locations . The variation in crop duration is known to affect the seed yield under both drought and heat stresses −2, POD; Podsplant−1, PPP; Seedm−2, SPM, Seedpod−1, SPP; shoot day−1, PDS and Total dry matter weight, TDM) were studied in 1–7 seasons, 1–2 environments at two location in India (Patancheru and Kanpur) and three locations in Africa . A total of 221 significant MTAs were identified in the present study for the yield and yield component traits that explained 4.23–55.42% phenotypic variation. Notably, 70 (22.43%) MTAs identified were for 100SDW  and δ13C , were significantly associated with different traits. Interestingly all 11 SNPs in ERECTA gene were significantly associated with 100SDW. Furthermore, the sole SNP found in case of CAP2 promoter was detected be associated with 100SDW. AMADH192 gene-based SNP marker is significantly associated with 100SDW, explaining 25.71% phenotypic variation. The role of the gene AMADH, in response to stress caused by mechanical damage was evaluated by Petřivalský et al. AMADH in response to abiotic stress is also confirmed during the present study. The AMADH gene is found to be associated with four stress related root traits, deciphering the importance of this gene in abiotic stress response.A number of genes involved in plant drought responses and tolerance have been identified in model crops ASR gene all three SNPs that had significant associations were in the CDS region. Two SNPs ASR_209 and ASR_261 were associated with δ13C altered the amino acid they code for. In the case of ASR_209, asparagine is changed to glutamic acid while in the case of ASR_261 valine is changed to lysine. Interestingly, asparagine synthase has been reported to be negatively correlated with most performance parameters under drought stress in case of rice http://www.echocommunity.org/resource/resmgr/a_to_z/azch3gra.htm). In addition lysine increases the chlorophyll content in leaves and thus enhances drought tolerance. Among the candidate genes used in the present study, genes like ERECTA and DREB were reported in the “QTL-hotspot” region on CaLG04 of chickpea ERECTA gene was also reported to enhance transpiration efficiency ArabidopsisDREB has been reported to play important role in abiotic stress response in MedicagoThe gene-based SNP markers with significant MTAs were used to identify the coordinates on the chickpea genome and the amino acid changes due the SNPs were identified . Among 1The ultimate aim of breeding is to obtain higher yields under stress conditions. In the present study, a total of 159 MTAs were identified with PVE >25% for 4 important traits. A total of 38 significant allele effects for these 8 traits were identified associated with 9 markers showing significant impact on these traits while 9 markers were found to be associated with multiple traits . All the2; when r2<0.20) was found to decay rapidly with the genetic distance of 5 cM  was identified for 100-seed weight. In the present study, the pairwise LD estimated using the squared-allele frequency correlations  defined based on molecular characterization of global composite collection A chickpea reference set, comprising of 300 accessions  and the PstI adapter was simultaneously ligated. One μl of restriction/ligation reaction served as a template in a 50 µl amplification reaction with PstI+0 primer. Adaptor and primer sequences and cycling conditions are given in the earlier study The reference set was genotyped with the 15,360-clone DArT arrays developed by Thudi et al. http://www.kbioscience.co.uk/reagents/KASP_manual.pdf and http://www.kbioscience.co.uk/download/KASP.swf. SNPViewer was employed for SNP calling.Based on high PIC values and distribution of SNPs on the chickpea genome, 651 KASPar assays for the targeted SNPs were selected from ASR), drought responsive element binding protein (DREB) gene, ERECTA, sucrose synthase (SuSy), sucrose phosphate synthase (SPS), dehydrin (DHN), aminoaldehyde dehydrogenase (AMADH), AKIN (SNF1 related protein kinase), MYB transcription factor, responsible for drought tolerance were identified based on prior information of involvement of the genes in drought tolerance mechanism in other crop species. In order to identify the above mentioned candidate genes in chickpea, the sequences were downloaded either from chickpea or from related legume species like Medicago, which is phylogenetically related to chickpea.The candidate drought responsive genes were genotyped either on reference set or on the mini-core collection . The canThe candidate genes were amplified on the reference/mini-core collection. The PCR amplicons were purified using Exonuclease I and 1 U of shrimp alkaline phosphatase (SAP) per 5 µl of PCR product. The Exo/SAP added PCR products were products were incubated for 45 min at 37°C followed by denaturing at 80°C for 15 min in the thermal cycler for deactivating unused Exonuclease enzyme. The Exo/SAP treated amplicons were mixed with 1 µl of BigDye Terminator V3.1 , 2 µl of 5× sequencing dilution buffer and 3.2 µM of primer (forward and reverse separately) and the volume was made to 10 µl. The sequencing PCR profile included an initial denaturation of 96°C for 10 sec, 50°C for 5 sec, and 60°C for 4 min. Before sequencing, the PCR products were treated with 2.5 µl of 125 mM EDTA and 25 µl of absolute ethanol and incubated for 15 min at room temperature to precipitate the DNA. The plate containing the PCR product was centrifuged at 4000 rpm for 30 min at 4°C. The Ethanol/EDTA mix was poured off by inverting the plate, without losing the pellet. To each well, 60 µl of 70% ethanol was added and again spun at 4000 rpm for 20 min at 4°C. The ethanol was poured off as earlier. The plate was air-dried and 10 µl of HiDi formamide  was added and the products were denatured  and sequenced using an ABI3700 automated sequencer . The large-scale sequencing of candidate genes across 300 genotypes of reference set was carried out at MACROGEN, Korea using BigDye terminator cycle sequencing chemistry.http://snpeff.sourceforge.net/).The gene sequences were subjected to BLAST against chickpea reference genome assembly In the present study, the reference set/mini-core collection was phenotyped for 34 traits in 5 locations  in three countries , in 1–5 environments  in 2–3 replications. The detailed information on the traits phenotyped, locations and seasons are provided in −1; root length density, RLD, cm-cm−3; root dry weight, RDW, g plant−1; rooting depth, RDp, cm; root surface area, RSA, cm2 plant−1; root volume, RV, cm3; ratio between RDW, and total dry weight, RTR, %; Average diameter, AVD, mm; projected area, PRA, cm2 plant−1) for three seasons  in cylinder culture in three replications using semi-automated high-throughput precise phenotyping facility at ICRISAT, Patancheru as described earlier The reference set was phenotyped for drought tolerance related root traits , for three seasons  in cylinder culture in three replications at Patancheru. In addition, morphological traits like plant height , plant width , apical primary branches (APB), apical secondary branches (ASB), basal primary branches (BPB), basal secondary branches (SBS), Tertiary branches (TB) in 1–5 locations and 1–13 seasons , days to maturity (DM), and flowering days (FD) were phenotyped on reference/mini-core collection in 1–5 locations and 1–7 seasons .−1 (SPD), pods plant−1 (PPP), 100-seed weight , yield , yield plant−1 , biomass , harvest index , seeds m−2 (SPM), total dry matter weight .The reference set/mini-core collection was phenotyped for yield and yield related traits like seeds pod13C), and SPAD chlorophyll meter reading (during 2004–05 and 2005–06) specific leaf area were also phenotyped  on a Vertisol at Patancheru and in an Inceptisol (sandy loam) at Kanpur. The alpha lattice design was adopted and evaluated in three replications at both the locations.For each trait, data was analyzed using analysis of variance (ANOVA) by using SAS General Linear Model (GLM) procedure http://darwin.cirad.fr/darwin). Neighbor joining trees were viewed employing Dendroscope ver. 3.2.2 http://ab.inf.uni-tuebingen.de/data/software/dendroscope3).The marker attributes like major allele frequency, gene diversity and PIC value for all markers was computed using PowerMarker ver. 3.25 http://pritch.bsd.uchicago.edu/structure.html) was employed. For this, the number of sub-populations (K) was presumed as 1 to 15, and each was repeated two times. For each run, burn-in and iterations were set to 1,00,000 and 2,00,000 respectively, and admixture and correlated allele frequencies was used. The run with maximum likelihood was used to assign individual genotypes into sub-population.A set of 85 DArT loci uniformly distributed across the chickpea genome http://www.maizegenetics.net). A Mixed Linear Model (MLM) analysis with optimum compression with P3D method The SNP, DArT and SSR markers data set from the reference set was used to generate a matrix of similarity between each pair of genotypes in the study (the K matrix) using the program TASSEL. The Q and K matrices were used to correct for the effects of population substructure in the association panel which can cause false positive associations. Using the Q and K matrices as a covariate, markers were tested for association with each phenotype using the program TASSEL  Structure of sub-populations at different K values ranging from 2–15 b) Comparison of genetic relationships revealed by SSR, DArT and SNP markers clearly indicated three major clusters.(TIF)Click here for additional data file.Table S1Information on the association panel used in the present study.(XLSX)Click here for additional data file.Table S2Summary of phenotyping data generated on reference set/mini-core collection.(XLSX)Click here for additional data file.Table S3Summary statistics of root traits, morphological, phenological, transpiration efficiency related traits, yield and yield component traits evaluated on reference set and/mini-core collection.(XLSX)Click here for additional data file.Table S4Major allele frequency, gene diversity and PIC content of 35 SSR loci used in the study.(XLSX)Click here for additional data file.Table S5Major allele frequency, gene diversity and PIC content of 1,156 DArT loci used in the study.(XLSX)Click here for additional data file.Table S6Number of alleles and PIC values of 381 SNP markers used in the study.(XLSX)Click here for additional data file.Table S7Candidate genes used for association mapping.(XLSX)Click here for additional data file.


Brassica napus is a globally important oilseed for which little is known about the genetics of drought adaptation. We previously mapped twelve quantitative trait loci (QTL) underlying drought-related traits in a biparental mapping population created from a cross between winter and spring B. napus cultivars. Here we resequence the genomes of the mapping population parents to identify genetic diversity across the genome and within QTL regions. We sequenced each parental cultivar on the Illumina HiSeq platform to a minimum depth of 23 × and performed a reference based assembly in order to describe the molecular variation differentiating them at the scale of the genome, QTL and gene. Genome-wide patterns of variation were characterized by an overall higher single nucleotide polymorphism (SNP) density in the A genome and a higher ratio of nonsynonymous to synonymous substitutions in the C genome. Nonsynonymous substitutions were used to categorize gene ontology terms differentiating the parent genomes along with a list of putative functional variants contained within each QTL. Marker assays were developed for several of the discovered polymorphisms within a pleiotropic QTL on chromosome A10. QTL analysis with the new, denser map showed the most associated marker to be that developed from an insertion/deletion polymorphism located in the candidate gene Bna.FLC.A10, and it was the only candidate within the QTL interval with observed polymorphism. Together, these results provide a glimpse of genome-wide variation differentiating annual and biennial B. napus ecotypes as well as a better understanding of the genetic basis of root and drought phenotypes. Brassica napus is an amphidiploid formed from recursive and independent hybridizations between the diploid species B. rapa (A genome) and B. oleracea (C genome)  and/or maximizing water uptake .Plants cope with drought through physiological plasticity and/or adaptive mechanisms under heritable genetic controls . DroughtRPF.dry1/DTF.dry2; hereafter referred to as QTL.A10). Conditional path analysis results suggest that it may impact both flowering time and root mass directly, indicating pleiotropy as the genetic mode of action. A deeper understanding of the genetic variation contained within QTL.A10 is required for insight into the genetics underlying this and the other QTL regions identified in the SE-1 mapping population.In a previous study, we mapped QTL for drought escape (flowering time), dehydration avoidance , and grain yield in the SE-1 doubled haploid (DH) population of 225 lines grown under wet and dry treatments . The QTLB. napus and its progenitors B. rapa and B. oleracea have become available recently  and prepped for an average insert size of 200 bp. DNA libraries of each parent were sequenced on one lane of an Illumina HiSequation 2000  sequencer to generate 2 × 100 paired-end reads.This study analyzed the canola cultivars IMC106RR  and Wichita  reference genomes was performed using SeqMan NGen v4 . The alignment was performed using default settings for read mapping: k-mer size: 21; Minimum aligned length: 35; Maximum gap size: 6; Minimum match percentage: 93; Match score: 10; Mismatch penalty: 20; Gap penalty: 30; Alignment cutoff: 200. The mapping took into account the paired-end nature of the reads, required an insert size of 0–525 bp, and correct orientation in order to be designated as “properly paired” in the SAM/BAM output. The SNP calling parameters were as follows: Minimum SNP percentage: 5; Minimum probability SNP different than reference: 10%; Minimum SNP count: 2; Minimum base quality score: 5; Minimum strand coverage: 0; Bases to mask at end of reads: 0.Mapping of the genomic sequencing data (fastq files) to the B. napus alignment, along with 455 SNPs called in candidate genes of the QTL.A10 interval, were manually reviewed using the Tablet  for further filtering. The final list of SNPs was generated using the following filter criteria: quality call score ≥ 30 , SNP frequency ≥ 5%, depth ≥ 5, and “p not ref” ≥ 90 (probability that the base is different than the reference base). Because SNPs were called relative to the reference genome, three lists were generated: SNPs differentiating both parents from the reference and SNPs present in only one of the parents (The Burrows-Wheeler Alignment (BWA) tool  was usedP-values were then calculated via permutation by randomly shuffling the numbers of nonsynonymous SNPs and codons across gene models while preserving GO annotations, and then asking whether the number of nonsynonymous SNPs per codon associated with the GO term in the permuted set was greater than or equal to the corresponding value from the true data set. 1000 such permutations were performed.Polymorphisms were recorded as noncoding if they did not appear in a coding region of any gene model. SNPs in coding regions were then classified as either synonymous or nonsynonymous. The length of aligned sequence was calculated using the alignment summary report generated from SeqMan NGen. SNP density was calculated as the number of SNPs existing within a particular length of aligned sequence. In order to test Gene Ontology (GO) terms for an excess of nonsynonymous polymorphism, the number of nonsynonymous SNPs and the total number of codons in each gene model was obtained. For each GO term, the total density of nonsynonymous SNPs per codon among all gene models annotated with that term was calculated. Enrichment To estimate pairwise B. napus reference and each cultivar was significantly different from that of equal divergence, a Chi-square test was performed using the number of observed SNPs  relative to the expectation of equal divergence .To test whether the divergence between the QTL genomic alignments were performed using the Mauve multiple genome alignment algorithm developed by B. rapa and B. oleracea references using BLAST  for the FLC insertion based on simple size polymorphism of the PCR amplicon.Primers used to amplify the KASP SNP genotyping assays  were developed for candidate SNPs discovered in comparison of the reference based assembly of the parents and used to genotype the original DH population. The parent genome alignments were used to design primer sequences for the KASP assays. A genetic linkage map was constructed which incorporated the molecular markers using JoinMap3  with a tGenome-wide QTL scans were performed using Haley-Knott Regression  in R/qtlThe raw sequence data used in this manuscript have been submitted to SRA (SRP065419).bzh’ reference genome  reference genomes, 82% of reads were mapped that summed to nearly 531 Mb of callable sequence aligning to the 642 Mb genome. The alignment made using the B. napus reference will hereafter be referred to as the “B. napus alignment” and the alignment made using the diploid progenitor references will hereafter be referred to as the “progenitor alignment.”Whole-genome sequencing of IMC106RR and Wichita produced paired-end read data sets summing to 300 million reads (27.8 Gb passed Illumina filter) and 349 million reads (32.2 Gb passed Illumina filter), respectively. More than 90% of reads in each data set were considered to be of high quality (Q-score ≥ 30), equating to an average high quality read coverage of 26.6 × for Wichita and 23 × for IMC106RR. 85% of reads mapped to the ‘Darmor-B. napus is an amphidiploid that carries an average of six homologous regions due to the hypothesized hexaploid ancestry of both B. rapa and B. oleracea  were confirmed to be accurate. A comparable accuracy rate (80.4%) was found in a similar examination of SNPs called in the progenitor alignment. The majority of the false positives were due to insufficient or no coverage in one parent, so that a well-supported SNP from the other parent appeared to be unique. Next, we searched each alignment for 100 SNPs randomly selected from the nearly 1200 used to construct the original linkage map. Of these, 81 were successfully identified in the B. napus alignment and 97 were identified in the progenitor alignment. The majority of the SNPs absent from the B. napus alignment blasted to the corresponding pseudochromosomes of the draft genome designated as “random.” These are a set of 19 pseudochromosomes constructed from scaffolds in which orientation could not be determined during assembly  in the progenitor alignment and 0.23% (1.14 million SNPs) in the B. napus alignment (P ≤ 0.01) in the A genome than the C genome in both alignments (-1) than in the C genome (513 kbp cM-1).Estimates of SNP density made using the progenitor and lignment . SNP denignments . We estiB. napus reference than they were to either of the diploid progenitor genomes (B. napus reference genome than IMC106RR (P < 0.0001). This result is perhaps not surprising given that Wichita and Darmor-bzh are both winter cultivars, and strong population structure differentiating winter and spring growth habit in B. napus is well established (P < 0.0001). The results reported from this point onwards will be based on the progenitor alignment, since the outputs of each alignment are in general agreement (Table S1) and because the progenitor reference genomes have already been annotated to include orthologous Arabidopsis genes, thus enabling assignment of functional annotation to polymorphic gene sequences.IMC106RR and Wichita were each more similar to the  genomes . A Chi-sP < 0.01). However, the nonsynonymous substitution rate in each genome was nearly identical so that the ratio Table S2). As expected, SNP density in coding regions was lower than noncoding regions in both the A and C subgenomes . The synelection . While pelection .P < 0.01 (Table S3). Of these, four were related to transcriptional regulation and the second lowest P-value was annotated as “response to gibberellin stimulus.” Both of these gene classes are well documented as being involved in the vernalization and flowering pathways so the enriched nonsynonymous variation in these GO terms may well describe some of the fundamental networks underlying differences in the growth habit of these cultivars . Ultimately, the final list of candidate genes carrying nonsynonymous SNPs was narrowed down to 58 flowering genes and 19 root genes. Alignments comparing the progenitor and B. napus reference QTL demonstrate a noticeable difference in synteny among homologous genomes, where the A genome is more conserved than the C . The disagreement is largely in the location of genes within each reference as the majority of genes are present in both QTL intervals, although in different positions.We focused on differences in our five previously identified QTL intervals . The average SNP density across these 42 gene regions was 0.24% and ranged from 0–0.83%. Almost half of this initial list (19) showed no molecular variation and only 11 were deemed as candidate polymorphisms underlying QTL . Across these 11 genes, a total of 37 nonsynonymous polymorphisms were discovered. Bra009156 had a SNP density of 0.83%, the highest of any of the candidates analyzed. Finally, 12 consecutive candidate genes spanning from Bra008955 to Bra009327 showed no genetic variation, including the notable candidate gene FLC.We chose to conduct a more detailed analysis of QTL.A10  and B. napus (BnaA10g22080D), the insertion was located in the first exon  that lacked markers in our original map. Twelve loci passed the criteria for suitable primer and SNP marker development and were used to genotype the DH population. Nine SNP markers ended up passing the primer design criteria and segregated at the expected 1:1 ratio for each parental allele. We also genotyped the DH population for the FLC insertion. We reconstructed the genetic map and validated the location of these markers as all of them mapped to the predicted chromosome A10, and all recombination fractions were consistent with the order based on their physical locations  for a nonsynonymous SNP in every gene within the ocations . The oriignments , suggestQTL.A10 was selected as the focal QTL for this study due to its impact on all measured traits, along with the possibility that it is directly involved in the control of root mass. This conclusion is based on QTL scans that determined this locus to be significant after conditioning upon flowering time, a genetically correlated trait (rg = 0.48). Genome-wide QTL scans were reperformed using the new map to determine if the additional markers increased the QTL model fit parameters, relative to the original QTL results. In the original analyses, the most highly correlated SNP (BSNP3510) was located in the gene Bra008726 at position 13,586,650; however, the new QTL mapping places the most significant marker in FLC (FLC was the only remaining candidate gene with observed polymorphism (Bna.FLC.A10 for four of the five traits (Table S6). RPF in the wet environment (RPF.wet3) was the only trait in which Bna.FLC.A10 was not the nearest marker. Instead, it was the adjacent SNP located at 13,897,294 (near Bra009063), an upstream distance of only 41 kbp.As mentioned, r in FLC . Furthermorphism . For allFLC insertion is associated with flowering time and ecotype, we scored 20 spring and winter type lines for the insertion (Table S7). There was a significant relationship between insertion presence and ecotype (P = 0.01), with the majority of spring types tested having the insertion. We also looked for the insertion in spring flowering lines of the parental species B. rapa to see if the origin of the insertion predates the allopolyploid speciation of B. napus. We did find the insertion in B. rapa, though at a lower frequency than in spring B. napus . This is indicative of a higher rate of purifying selection in the A genome  of B. rapa and B. oleracea at the creation of B. napus. Increased Ne results in an improvement in the efficiency of selection so the patterns observed in this study could have been the result of elevated B. rapa Ne relative to B. oleracea. Of course, such a scenario would require many hybridization events among multiple genotypes of each species for patterns recognized at the level of the population to be captured in the allopolyploid descendants. The hybridization of the A and C genomes appears to have occurred on more than one occasion but the extent of its recursivity remains unclear , a negative regulator of flowering  contained within the QTL on C02 has been shown to have a direct role in the regulation of flowering time  is characterized by significant decreases in seedling root growth rate (P = 0.53), indicating that this locus most likely is not impacting root growth during this early phase of development. Of the list of 13 root related genes identified in the pleiotropic QTL on C02, Bol007157 (At5g18560: PUCHI) is an intriguing candidate due to its demonstrated role in both floral . We extended our search 200 bp beyond the putative 1 kb coding region upstream of FLC to look for the presence of a transposable element associated with the vernalization response in different B. napus genotypes, as reported by FLC in the IMC106RR spring parent creating a truncated protein.The resequencing data identified no molecular variation in the coding region of Bna.FLC.A10 to be the most highly correlated polymorphism on the A10 linkage group. FLC is a MADS-box transcription factor that has been identified as an important regulator of the vernalization and autonomous flowering pathways in Brassica  categories annotated with stress response and abiotic stimulus  located within the QTL.A10 interval. Here, we acknowledge that additional research  will be required to confirm the Bna.FLC.A10 polymorphism as the causal factor of both the flowering time and root mass phenotypes. If true, the major challenge thereafter will be elucidating whether the pleiotropic action is the direct result of Bna.FLC.A10 or simply a consequence of its upstream impact on flowering time (QTL scans showed the indel discovered in Brassica . It has


We further found a SNP upstream of FGF-7 that appears to contribute to the divergence signature. First, the SNP occurred at an extremely conserved site. Second, the SNP showed an increasing allele frequency with the elevated altitude in Nepalese sheep. Third, the electrophoretic mobility shift assays (EMSA) analysis using human lung cancer cells revealed the allele-specific DNA-protein interactions. We thus hypothesized that FGF-7 gene potentially enhances lung function by regulating its expression level in high-altitude sheep through altering its binding of specific transcription factors. Especially, FGF-7 gene was not implicated in previous studies of other high-altitude species, suggesting a potential novel adaptive mechanism to high altitude in sheep at the Himalayas.Sheep has successfully adapted to the extreme high-altitude Himalayan region. To identify genes underlying such adaptation, we genotyped genome-wide single nucleotide polymorphisms (SNPs) of four major sheep breeds living at different altitudes in Nepal and downloaded SNP array data from additional Asian and Middle East breeds. Using a  Ovis aries) is one of the first domesticated livestock species whose ancestors mainly roosted in the Fertile Crescent regions of Southwest Asia around 10,000 years ago1Sheep  and EGLN1 . Both candidates are the key genes functioning at the upstream of the hypoxia inducible factor (HIF) pathway and the functional mutations of these two genes have been documented29EPAS1 gene shared by Tibetans141621221215The adaptation to high-altitude environment is believed to be a result of advantageous genetic mutations and selective pressure. For identifying the alleles that have been subject to selection, a variety of statistical methods have been developed, mainly based on (i) the high frequency of derived alleles and the consequences of hitchhiking within population, such as Fay and Wu’s ovine 50KSNP Beadchip. We then downloaded the publicly available SNP beadchip data from the other two Tibetan-lineage sheep (Tibetan and Changthangi sheep) as well as 15 other breeds from Asian and Middle East. After merging with our data, we conducted a phylogenetic analysis and a genomic scan for signatures of directional selection in high-altitude sheep. Re-sequencing data of the candidate locus was analyzed to map the major variant and Nepalese sheep individuals were further screened for the variant.To delve into these issues, we genotyped the four major Nepalese sheep breeds comprising of two high-altitude breeds , and two lowland breeds (Kage and Lampuchhre) using Illumina Ovine SNP50 beadchip array in a panel of 96 Nepalese sheep including two high-altitude breeds  and two low-land breeds (Lampuchhre and Kage), with each breed containing 24 individuals. To better understand the evolution of the sheep breeds at the Himalayan region in the context of their geographic neighbors, the SNP data of 454 sheep individuals from eight Asian and nine Middle East breeds were merged with our data, producing a common data set of 47,415 genotyped SNPs in 550 individuals  based on the pruned genotype data of 36,711 SNPs from 21 Asian sheep breeds (n = 550) including nine Middle East, six Asian, one Tibetan, one Changthangi and four Nepalese sheep populations . The firA neighbor-joining tree using the same 21 Asian breeds agreed with the PCA analysis on the following aspects, including the evolutionary divergence between populations from Asia to Middle East, the close but different background between two high-altitude sheep groups, as well as the close relationship of Lampuchhre with the Indian group . FurtherTo better understand the population variation, we performed Linkage Disequilibrium (LD) decay analysis, which can be informative for population demography. When we combined all our breeds, LD declined most rapidly , consistST value at genome-wide level. The FST values for comparisons between the four high-altitude breeds  and the eight low-land breeds were 0.0809-0.1347, 0.0914-0.1426, 0.0805-0.133 and 0.1479-0.1991, respectively  were shared by the three Tibetan-group breeds, consistent with the fact that these breeds have similar genetic background . Althougkeratinocyte growth factor 7 (FGF-7) and galactokinase 2 (GALK2). The four candidate SNPs in the Tibetan group with the exception of the SNP OAR7_63692612.1 in one breed (at the 90th ranking), were all among the top 10 ranking SNPs with the highest di values  of the four candidate SNPs mentioned above and defined a shorter region spanning 51.6-kb  that only contained the FGF-7 gene. Additionally, the selection signal on chromosome 7 was the strongest across genome among three Tibetan group populations . In Bhyai values . While tFGF-7 gene region for evidence of selection, we retrieved the International Sheep Genome Consortium (ISGC) re-sequencing data (http://projects.ensembl.org/nextgen/) of the most evident gene, FGF-7, from 12 sheep individuals comprising of two Tibetan, two Changthangi and eight other Asian sheep individuals  to align with the above 21 SNPs showing significant genetic differentiation between the high and lowland sheep. Interestingly, we found that one SNP, located at the position of chromosome 7: 57,843,681, was overlapped with one extremely evolutionary conserved element  with intermediate allele frequency in Baruwal , and rare in lowland populations   . The numhttp://tfbind.hgc.jp/), which detected a remarkable change in the putative transcription factor (TF) binding sites caused by this substitution  with nuclear extracts from human alveolar epithelial cells was performed. Interestingly, the results revealed specific DNA-protein interaction, providing support to the bioinformatics prediction . The bioFGF-7. Despite no previous association of FGF-7 gene with high-altitude adaptation, its protection role in lung injury was well established. FGF-7, as a member of the fibroblast growth factor family with predominant expression in epithelial cells3536FGF-7 expression reduces pulmonary edema, permeability, hypoxia, and epithelial injury in various rodent models38in vitro modelFGF-7 was considered as an intervention to reduce epithelial injury and improve recovery in the acute respiratory distress syndromeFGF-7 as a susceptibility locus for chronic obstructive pulmonary diseaseFGF-7 could be a good candidate for the prevention of pulmonary injury caused by high-altitude environment, such as high-altitude pulmonary edema (HAPE).By our genome-wide scan of four major Nepalese sheep breeds combined with other 17 downloaded Asian sheep populations, we identified the major selective sweeps for high-altitude adaptation in sheep at the Himalayas. Based on two distinct high-altitude sheep groups, we then mapped the common selective sweep to the most evident candidate FGF-7 locus identified by the di analysis were more likely attributed to the directional selection rather than the genetic drift or formation of breeds.The phylogeographic analyses based on PCA, phylogenetic tree, and LD decay, confirmed that both genetic background and breeding history of the four Nepalese sheep breeds were distinct, specifically in the two high-altitude groups, namely, the Tibetan group consisting of Bhyanglung , and the Himalayan group consisting of Baruwal . This obFGF-7 is probably the target of the high-altitude selection. First, the SNP displayed a marked allele frequency divergence between the analyzed high- and low-altitude sheep individuals  located at different ranges of altitudes were collected from multiple flocks in order to capture representative samples for the within-breed genetic diversity . We usedAll the animal experimental procedures were approved by and performed according to the guidelines for the care and use of experimental animals established by the Ministry of Agriculture of People’s Republic of China and Institute of Animal Science, Chinese Academy of Agricultural Sciences.Ovine SNP50 beadchip array, which included 59,454 SNPs, according to the manufacturer’s protocols. We obtained the genotyped SNP data of 71 breeds of sheep  from the Sheep Genomic Consortium project All genomic DNA samples from 96 sheep were genotyped using the Illumina 2 values within each populations were calculated with parameter –r2 –ld-window 99999 –ld-window-r2 0 in PLINKA pruned data set of 550 sheep containing 36,711 SNPs which excluded SNPs in LD  were used to investigate the genetic structure. PCA were performed with the ACTG softwareFST as described by Weir et al.di statistic at each SNP marker for each high-altitude population, which is particularly well suited for detecting lineage-specific selective events, was calculated for each SNP to retrieve candidate SNPs under selection as described by Akey et al.i high-altitude populations and denote the expected value and standard deviation of FST between i and jth subpopulation as To identify the genomic selective signatures related to altitude adaptation in highland breeds , we performed four separated analyses for these breeds by using a total of 43,835 SNPs (MAF < 0.05 among Asian populations). Each breed was compared with other eight lowland breeds found in Asia based on the results of the genetic structure. The unbiased estimate of pairwise di (PE) was calculated as the proportion of di values that were greater than or equal to the observed di value as following formula:The empirical p-value for di value was determined as ones falling to 99.5% percentile of the empirical distribution (PE < 0.005). To account for stochasticity in single SNPs, we then clustered all significant SNPs within 500 kb of each other into single highly differentiated reigons.The significance of http://projects.ensembl.org/nextgen/). The sequence mutations of the most promising candidate gene, FGF-7  spanning 5 kb upstream of the initial codon or downstream of the terminal codon of the gene) were then extracted by using a custom Perl script. Fisher’s exact test was carried out by testing each position against their population grouping. SNPs with P value < 0.001 were considered as the interesting SNPs. The levels of conservation of these SNPs (Constrained elements/score for 23 amniota vertebrates Pecan), measured by Genomic Evolutionary Rate Profiling (GERP) scoreThe downloaded genomic variations stored in Variant Call Format (VCF) files for two Tibetan, two Changthangi and eight other Asian sheep  were genFGF-7. After PCR amplification, Sanger’s sequencing technology was employed to genotype the SNP in the additional Nepalese sheep samples from the different altitudes, including 15 Bhyanglung, 31 Baruwal, 28 Kage, and 20 Lampuchhre sheep individuals.Polymerase chain reaction primers  were designed for sequencing the promising causal SNP in the upstream of A549 cells were obtained from JK green and were propagated in Dulbecco’s Modified Eagle’s Medium (DMEM) supplemented with 10% heat-inactivated fetal bovine serum and penicillin (0.2 U/ml)/streptomycin (0.2 μg/ml)/L-glutamine (0.2 μg/ml) (Gibco). The nuclear proteins from A549 cells were extracted according to instructions of nuclear and cytoplasmic extraction reagents kit .FGF-7 was functionally assessed using EMSA to reveal its potential to affect DNA-protein interaction. The probes used were as follows: FGF-7 wt, 5′-aggtggtgcacgTaaaccaa-3′; FGF-7 mut, 5′-aggtggtgcacgAaaaccaa-3′. The probes were purchased 5′-Biotin labelled from Invitrogen. Single-stranded complementary oligos were annealed in 1X NEB2 buffer (New England Biolabs) at 2 min at each degree from 95 °C to 25 °C to produce double-stranded probes. A total of 5 μg A549 nuclear extracts was preincubated on ice for 20 min in binding buffer ). Competition reactions were supplemented with 20 pmol (100-fold molar excess) unlabeled ds-oligonucleotide. After the addition of 200 fmol 5′-Biotin labeled ds-oligonucleotide, reactions were incubated at RT for 30 min. The protein–DNA complexes were separated on a 6% polyacrylamide gel (JKgreen) run in 0.5 × TBE at 100 V for 2:30 h in RT. Transfer to IMMOBILON NY + INYC00010 nylon membranes (Millipore) was carried out in 0.5 × TBE at 300 mA, 4 °C for 30 min. The DNA was crosslinked for 60 sec on a transilluminator with 254 nm bulbs and 120 mJ/cm2, and blocked with the blocking reagent. The membrane was then detected using streptavidin-horseradish peroxidase conjugate and ECL chemiluminescent detection kit (JKgreen).The SNP upstream of sheep How to cite this article: Gorkhali, N. A. et al. Genomic analysis identified a potential novel molecular mechanism for high-altitude adaptation in sheep at the Himalayas. Sci. Rep.6, 29963; doi: 10.1038/srep29963 (2016).


Sample-tagging is designed for identification of accidental sample mix-up, which is a major issue in re-sequencing studies. In this work, we develop a model to measure the information content of SNPs, so that we can optimize a panel of SNPs that approach the maximal information for discrimination. The analysis shows that as low as 60 optimized SNPs can differentiate the individuals in a population as large as the present world, and only 30 optimized SNPs are in practice sufficient in labeling up to 100 thousand individuals. In the simulated populations of 100 thousand individuals, the average Hamming distances, generated by the optimized set of 30 SNPs are larger than 18, and the duality frequency, is lower than 1 in 10 thousand. This strategy of sample discrimination is proved robust in large sample size and different datasets. The optimized sets of SNPs are designed for Whole Exome Sequencing, and a program is provided for SNP selection, allowing for customized SNP numbers and interested genes. The sample-tagging plan based on this framework will improve re-sequencing projects in terms of reliability and cost-effectiveness. Next-generation-sequencing (NGS) has gained its ground in medical research in recent years, and NGS-based re-sequencing has become a prevalent procedure in revealing causative variants13467891011781113147811137811Any sample-tagging plan usually utilizes polymorphic markers, especially SNPs. The human genome is a hive of SNPs which accumulate in the long history of human evolution, diversifying individual genomes. There are more than 10 million SNPs in the human genome33 = 8.5 billion). In the same way, suppose the markers with three genotypes, e.g., homozygous wild-type, homozygous mutant, and heterozygous mutant, are used to label, the least number of markers is 21 (321 = 10.4 billion). It is obvious that these plans can work only if the markers have the two characteristics: 1. each genotype of a marker should be equally frequent; 2. the genotypes of one marker should be statistically independent of all other markers. Since we know that these conditions do not hold in reality, there are definitely more markers needed. According to information theory, the information contained in the aforementioned 33 markers and 21 markers are identical, equal to 33 bits of Shannon-entropySuppose the present world population is 7 billion and the two-state markers are used to label each individual, then there are at least 33 markers needed . On the other hand, among these 265 individuals, there are 14 trios  and 4 quartets . The genotypes of these 265 individuals are extracted from the WES results, and the procedures of variant calling and genotyping are described previously2Only the coding SNPs on the autosomal chromosomes  are considered in this work. There are 472,514, 1,093,534, 9,472 and 14,521 suitable SNPs collected from 1000 Genomes Project, ESP, HGDP, and HapMap, respectively.In order to test the possible sample overlap between 1000 Genomes Project, HapMap, and HGDP, 2,236 coding SNPs shared by these three datasets are used to compare the individual genotypes mutually. Individuals with >95% identity in genotype are regarded as sample overlap. This test shows there is no sample overlap existing between 1000 Genomes Project, HapMap, and HGDP.In order to test the model, the simulated genotypes of four major ethnic groups, i.e., African (AFR), European (EUR), East-Asian (ASN), and American (AMR), are generated according to the allele frequencies drawn from 1000 Genomes Project and HapMap. The simulation size for each ethnic group is 100 thousand individuals, and the simulated genotypes obey the Hardy-Weinberg equilibrium.When evaluating the performance of the optimized SNP sets in differentiating individuals, random SNP sets with SNP numbers from 20 to 50 are each generated by 1,000 times. The random SNPs are all located on the autosomal chromosomes, with MAF between 0.4 and 0.5 according to 1000 Genomes Project and HapMap. These random SNP sets stand for an intuitive design which is expected to reach suboptimal performance, much better than random SNP sets without MAF restriction while inferior to the optimized SNP sets.The information content, or Shannon-entropy of single SNP in 1000 Genomes Project, ESP, HGDP, and HapMap is calculated according to the variability of its three genotypes . The information content is ranging from almost zero to as high as 1.585, which is the expected maximal entropy for a single SNP . Since 1To refine SNPs by entropy consistency, SNPs are regarded as qualified if their entropies do not fluctuate in different datasets. Pairwise bootstrap tests are conducted to identify the qualified SNPs among 1000 Genomes Project, HGDP and HapMap, for the three datasets have different composition of ethnic groups. Only SNPs shared by 1000 Genomes Project, HGDP, and HapMap, which are equal in entropy in the 95% bootstrap percentile confidence interval, are remained. And SNPs whose mappability are not 1In order to measure the genotype dependency between SNP pairs, the mutual information of each pair of the qualified SNPs is computed, based on the combined genotypes of 1000 Genomes Project, HGDP, and HapMap, altogether 3,250 individuals . Large vN > 3) SNPs is difficult to calculate, but the upper and lower bounds can be estimated as HU and HL. For the sets of SNPs with given number from 1 to 100 , the joint entropies are calculated for the maximal values, according to the aforementioned recursive algorithm. The gap between HU and HL grows as the number of SNPs increases , European (EUR), East-Asian (ASN), and American (AMR). In order to compare with the optimized SNP sets regarding the performance, for each of SNP numbers from 20 to 50, there are 1,000 random sets of SNPs generated. These random SNPs are chosen when their MAFs in 1000 Genomes Project and HapMap are between 0.4 and 0.5 and they are all located on the autosomal chromosomes. This reflects an intuitive plan which is supposed to reach suboptimal performance. According to the calculated Hamming distances in these simulated populations, the optimized SNP sets show superiority over the random SNP sets. For example, the average Hamming distance of an optimized set of given-number SNPs is larger than the random set by about 2. For example, an optimized set of 30 SNPs gives rise to the average Hamming distance of more than 18 in the simulation populations, while its random counterpart generates the average Hamming distance of around 16. And the duality frequencies of the optimized SNP sets are also much lower than the random ones, e.g., an optimized set of 30 SNPs can achieve very low duality frequency, which, even in the worst case of ASN, can be still less than 1 in 10 thousand .According to both the optimized set of SNPs and the random sets of SNPs, the Hamming distances are generated for 1000 Genomes Project, HGDP, HapMap, and MPIMG265 . The difA software program called SNP_Tagger has been developed for implementing the aforementioned algorithm. Users can download SNP_Tagger from https://sourceforge.net/projects/merap/files/SNP_Tagger20131220/. The software provides the 2,236 qualified SNPs as the starting menu, which have high and consistent information content in different datasets, and users can define the favorable SNP numbers and the gene list of interest. The default gene list includes all RefSeq genes on the autosomal chromosomes, assuming that most users will use WES. However, there are some users, especially clinicians, who may favor diagnostic panels including only part of the human gene repertoire, which thus can be defined as a curtailed gene list used by the program SNP_Tagger.Re-sequencing is entering the clinical research as a routine diagnostic method, while the large amount of samples processed will unavoidably bring about the problem of sample mix-up. The manual re-identification at the end of each re-sequencing project by Sanger sequencing is laborious and not cost-effective. Therefore, it is worthwhile to label samples before entering the diagnostic pipeline. The intuitive and cost-effective solution is tagging samples with a set of SNPs, which are abundant in the human genome and can distinguish individual genomes. However, it is the skewed allele frequency which makes the bulk of SNPs unsuitable as tagging markers, then how to define a set of competent SNPs in terms of differentiating capability, and consistence in different sample sources, is of major concern. Our motivation to design the algorithm is to create the set of SNPs with minimal numbers while with highest differentiating capacity. The information content, or entropy of SNP set, measures the variability of the SNP combinations, thus correlated with its competence in labeling individuals. To the limit of our knowledge, this is the first attempt by using information theory to select a set of qualified SNPs for sample-tagging in re-sequencing studies. We confirm the optimized SNP sets outperform the random ones, and also offer software by which the users can generate the SNP sets of their interest, so as to develop the specific diagnostic panels which harbor only a fraction of the human genes. In the same principle, this SNP-tagging plan also has potential application in forensic science, in order to match the SNP fingerprints of the concerned subjects.per se. This is the reason why we postulate that only 60 SNPs can label a population of 7 billion individuals, and we recommend using 30 SNPs to label the thousands of individuals in a typical re-sequencing project. The tests in our study also show that by using the optimized set of 30 SNPs, the Hamming distances in pairs are large enough to distinguish members of the same family. We hope that the implementation of this algorithm will result in an enhanced reliability and validity of re-sequencing projects, relieving the practitioners from the consequences of inadvertent sample mix-ups.In practice, the more SNPs are employed in tagging samples, the higher is the differentiating capability. The cost of SNP-tagging should be less than the re-sequencing cost times the sample mix-up rate, otherwise it will also make sense to re-identify the mixed samples by re-sequencing X is given by:H(X) is the information contained in a single SNP X, and p(x) is the probability that the single SNP X has a specific genotype x, which belongs to The Shannon-entropy, or information content, of a single SNP, is calculated in this work according to the variability of its three genotypes, i.e., homozygous wild-type, homozygous mutant, and heterozygous mutant. The information content of a single SNP Obviously, the maximal information content of a single SNP is 1.585 bits, which is achieved when the three genotypes are equiprobable. For a SNP with MAF = 0.5, when it has reached the Hardy-Weinberg equilibrium in a population, its information content is 1.5 bits.n and m, respectively, the bootstrap procedures are:n with replacement from the first source and a re-sample of size m with replacement from the second source. Compute the entropy difference (ΔH) between the first re-sample and the second re-sample, regarding the single SNP.Draw a re-sample of size Repeat this re-sampling process 1,000 times.ΔH.Construct a bootstrap distribution of the 1,000 ΔH is 0, assuming the genotype of a single SNP is identical in the two sources. A single SNP is regarded as consistent if the observed ΔH is located in the 95% bootstrap percentile confidence interval.The expected value of In order to evaluate the consistency of information content in different sample sources, the bootstrap, namely, random sampling with replacement from the original dataset, is employedFurthermore, in order to prevent the unsatisfactory performance of SNP detection in a variety of platforms, two factors have been considered for refining SNPs, i.e., mappability and GC-content. The mappability of a SNP measures the possibility of short-read alignment ambiguity due to homologous sequences, and mappability of 1 indicates no alignment ambiguityI) is used. The definition of mutual information between two SNPs X and Y is:X and Y regarding their three genotypes, and p(x) p(x) and p(y) are the marginal probability densities of X and Y, respectively, regarding their three genotypes. The higher the mutual information between X and Y, the more redundance in the information sum of X and Y.Genotypes of any two SNPs in the human genome are in many cases not independent, because there might be linkage disequilibrium among their alleles, which thus reduces the cumulative discriminative capability of multiple SNPs. To measure the mutual dependency between SNP pairs regarding their genotypes, the mutual information and the upper bound (HU)of the joint entropy of N SNPs can be defined as:Therefore, the lower bound  are defined in the 1,000 SNP pairs from the previous step. Take the leading 1,000 3-SNP sets which have the largest joint entropies.Step 3: whenk: when N = k, calculate the joint entropyN = k- 1. Take the leading 1,000k-SNP sets which have the largest joint entropies.Step The optimized SNP sets should differentiate individuals most efficiently. This can be evaluated by the mismatches between sample pairs regarding the SNP sets. In information theory, the Hamming distance between two strings of equal length is the number of positions where the corresponding symbols are differentH.H., H.H.R. and T.F.W. initiated and coordinated this work. H.H. designed the scheme and collected the samples. L.X. and H.H. constructed and tested the mathematical models. J.W. provided the plan of assessing the model robustness in the different datasets. H.H., L.X. and T.F.W. wrote the manuscript.How to cite this article: Hu, H. et al. Evaluating information content of SNPs for sample tagging in re-sequencing projects. Sci. Rep.5, 10247; doi: 10.1038/srep10247 (2015).


Zea mays) is the most widely grown crop species in the world and a classical model organism for plant research. The completion of a high-quality reference genome sequence and the advent of high-throughput sequencing have greatly empowered re-sequencing studies in maize. In this study, plants of maize inbred line B73 descended from two different sets of seed material grown for several generations either in the field or in the greenhouse were found to show a different growth phenotype and ionome under phosphate starvation conditions and moreover a different responsiveness towards mycorrhizal fungi of the species Glomus intraradices (syn: Rhizophagus irregularis). Whole genome re-sequencing of individuals from both sets and comparison to the B73 reference sequence revealed three cryptic introgressions on chromosomes 1, 5 and 10 in the line grown in the greenhouse summing up to a total of 5,257 single-nucleotide polymorphisms (SNPs). Transcriptome sequencing of three individuals from each set lent further support to the location of the introgression intervals and confirmed them to be fixed in all sequenced individuals. Moreover, we identified >120 genes differentially expressed between the two B73 lines. We thus have found a nearly-isogenic line (NIL) of maize inbred line B73 that is characterized by an altered growth phenotype under phosphate starvation conditions and an improved responsiveness towards symbiosis with mycorrhizal fungi. Through next-generation sequencing of the genomes and transcriptomes we were able to delineate exact introgression intervals. Putative de novo mutations appeared approximately uniformly distributed along the ten maize chromosomes mainly representing G:C -> A:T transitions. The plant material described in this study will be a valuable tool both for functional studies of genes differentially expressed in both B73 lines and for research on growth behavior especially in response to symbiosis between maize and mycorrhizal fungi.Maize ( It is an extremely diverse species whose genome abounds with single-nucleotide polymorphisms (SNPs) Maize  fungi. These soil-born fungi are obligate symbionts that colonize plant roots and assist their hosts in the uptake of water and nutrients, in particular phosphate (Pi), while obtaining plant carbohydrates in exchange  harbors three well-delineated segments from a different maize genotype in a B73 background summing up to a total of 5,257 SNPs. De novo mutation were found throughout the genome mostly representing G:C -> A:T transitions. Finally, we report and discuss >120 genes differently expressed between the two B73 strains potentially associated with the above described growth phenotypes.Here, we describe an inbred line of maize that is nearly isogenic to B73 used to generate the reference genome. This line was propagated for more than 20 years exclusively in the greenhouse without intended exposure to the mycorrhizal fungus In the course of a project that involved the collection of transcriptome, ionome and metabolome data from a large number of maize inbred lines In the course of our experiments, plants from both sets together with other inbred lines were grown in a bicompartmented system including or lacking arbuscular mycorrhizal fungi . One compartment was supplemented with Pi and closed by a hyphae-permeable membrane  as already described G. intraradices when access to Pi was limited to the mycorrhizal uptake pathway. Under high Pi conditions (plants have been grown at the universities of Hamburg and Regensburg in semi-sterilized soil supplemented with fertilizer) a significant difference in plant growth behavior was not observed between sets A and B (data not shown).As shown in The measurement of the total elemental composition (ionomics) by inductively-coupled plasma mass spectrometry (ICP-MS) in source leaves of maize plants underlined the observed growth differences between plants of set A and B under +AM and –AM conditions . A princIn silico detection of single nucleotide polymorphisms (SNPs) was performed using the SAMtools pipeline. The maize reference is characterized by a high content of repetitive elements and highly similar copies of genes as a consequence of recent allopolyploidy. Likewise, the maize reference sequence is not to be considered a finished reference sequence as the sequence has only been placed to BAC-level resolution (100–200 kb), but sequence contigs have not been ordered within single BACs. These factors caused some obstacles to accurate read mapping and variant calling from short read NGS data. We therefore focused our attention on SNPs that had sufficient (≥10x) read coverage in both samples and which were called as “homozygous alternative” in one sample and “homozygous reference” in the other. A total of 5,615 SNPs met our criteria. Of these, 358 had the non-B73 allele in plant A and 5,257 had the non-B73 allele in plant B. We calculated the number of SNPs in non-overlapping 100 kb bins. SNPs were distributed uniformly across the genome in set B  sequencing of a single plant from both set A and set B (plant A and plant B), respectively. We sequenced both plants to ∼15x whole genome coverage using the Illumina sequencing-by-synthesis platform . Sequencin set B . While tin set B , Table 2in set B . While tTo validate our SNP calls, we compared them against the maize HapMap version 2, which includes the genotypes of 103 lines of domesticated maize and teosinte at 55 million SNP positions. Only 24 (6.7%) of the 358 SNPs between the B73 reference and plant A were present in HapMap2. By contrast, 3,962 (70.5%) of 5,257 SNP of plant B were polymorphic in the HapMap panel. This led us to the conclusion, that plant B most likely carries an introgression from a different maize genotype in a B73 background, whereas the genome of plant A is almost identical to the B73 reference, with most SNPs attributable to errors in read mapping or SNP calling. We tried to find a possible donor genotype of the introgressed segment among the 103 HapMap lines. However, a HapMap genotype that is highly similar to plant B could not be identified.As we only sequenced one individual plant grown from each set A and B, respectively, we could not rule out the possibility that further introgressions are present in other plants from set B. We therefore performed transcriptome sequencing of plants grown under the same conditions from three randomly selected seeds of both sets . RNA-seqWe also determined the number of SNPs called across all six samples and differentiating between set A and B  without making use of SNP positions discovered in the WGS data. Visualization of the number of these SNPs in 100 kb bins identified the same intervals of increased SNP density . The agrtrans-acting factors influencing the expression of transcripts from loci in other genomic regions. Transcripts >10 times up-regulated in set A compared with set B include three genes involved in the anthocyanin biosynthesis pathway: the Anthocyanin regulatory C1 protein (GRMZM2G005066), a Dihydroflavonol-4-reductase (GRMZM2G026930) and an Anthocyanidin 3-O-glucosyltransferase (GRMZM2G165390). Activation of the anthocyanin pathway is a well-known reaction of plants to Pi stress We used the RNA-seq data collected from the six samples to analyze differential transcript abundance between plants from set A and set B. Overall 121 genes  were fouArabidopsis thalianaCaenorhabditis elegans−9 to 10−8 per site per generation. We mined our genomic variants calls for SNPs to the B73 reference sequence that occur either in set A or set B plants (but not in both) and are not part of HapMap2, and considered these “de novo SNPs” to be the result of spontaneous mutations that occurred during propagation of the seed stock. A total of 358 and 883 de novo SNPs were found in the genome of set A and set B plants, respectively. These putative de novo mutations were located approximately uniformly along the ten maize chromosomes  de novo mutations was largely similar between set A and B plants . The difference in transcript abundance of this gene was statistically significant (p = 0.003), but the log2 fold change was only 0.77. Given the absence of a functional annotation, we did not consider this gene as a good candidate for a causal gene underlying the described growth and ionome phenotypes.Resequencing studies in several model organisms, such as omosomes . Taking B plants . The majWe have described here a nearly isogenic line (NIL) of maize inbred line B73. The NIL was found by chance when we compared the response of field and green house grown plants to Pi starvation and AM fungi in a wider panel of maize inbred lines. Phenotypic changes were accompanied by alterations in transcript levels and the ionomic composition of leaves and seeds. Through whole-genome and transcriptome sequencing, we could ascertain that the NIL is pure and harbors three small introgression intervals on chromosome 1, 5 and 10 in a B73 genomic background. The analysis of differential gene expression revealed consistent differences in transcript abundance between both sets of plants.We were not able to trace back the origin of this NIL. It could be speculated that the introgression was introduced by unintentional and unnoticed cross-fertilization with an unknown parent during seed propagation at the maize stock center. Several rounds of backcrossing to “genuine” B73 may have decreased the size of the genomic regions inherited by the non-B73 parent. Subsequent rounds of self-fertilization may subsequently have led to fixation of the non-B73 allele in a homozygous state in three introgression intervals. This theory requires that in the “development” of the NIL backcrossing by unsupervised pollination in a plot of supposedly homogeneous B73 plants was supplanted with artificial self-pollination. Another possible explanation would be that B73 is not an entirely homogeneous inbred line, but to some extent a “fluid concept”: B73 lines maintained at different locations (or at the same location at different time points) exhibit slight differences in sequence composition that result only in minor phenotypic alterations, which in most cases escape notice. It is thus likely that more B73 NILs are around and are used by various labs and plant breeders.Whatever the exact crossing scheme may be, the NIL seems now to be maintained as an  pure (i.e. (nearly) homozygous) line at the University of Regensburg as plants grown from different B73 seed stocks showed an altered growth phenotype under low Pi conditions and between +AM and –AM treatment. Likewise, sequencing of RNA samples of three B73 plants randomly selected at Regensburg (set B plants) revealed the same three genomic intervals in all three samples and in the plant sequenced by whole-genome shotgun sequencing. As only controlled self-pollination has been carried out in the greenhouse and the number of SNPs is very small, it is unlikely that cross-pollination occurred at the Universities of Hamburg and Regensburg after seeds had been obtained from the stock center. The combined size of all three introgression intervals is about 4.6 Mb, i.e. ∼0.2% of the entire maize genome. The introgressed segments are located in highly recombinogenic subtelomeric regions of the long arms of the respective chromosomes. The recombination rate at these regions is approximately 3 cM per Mb This finding raises some concerns about the homogeneity of material from different sources that is supposed to have the same genetic background. Cryptic introgressions in the genomes of presumably identical individuals may result in phenotypic differences that become evident only under very special growth conditions. If slightly divergent material is inadvertently used in line development (e.g. in map-based cloning projects), mutant phenotypes may be obfuscated or, in the worst case, even emulated by variation in diverging chromosomal regions. In view of this worrying perspective, we advise that the identity of plant material, in particular when originating from different laboratories, should always be double-checked. Other reports corroborate our concerns about the purity of seed material. A small segment (1.5 Mb) that differs between different B73 lines has been reported previously de novo SNPs of the two lines relative to the B73 reference genome. Only one of these was located in the coding exon of a differentially expressed gene and this gene was not an obvious causal candidate underlying the growth phenotype. It is possible that, for example, a complex structural rearrangement such as a transposon insertion into the regulatory sequence of a gene residing outside the putative introgressions has affected the expression of one or several genes, which subsequently brought about the differential growth response to Pi starvation in the presence or absence of AM fungi. We can also not rule out epigenetic differences between both lines, though these have to be stably inherited as we saw consistent differences between all plants of the two B73 lines.We are fully aware that the data reported in this study does not constitute a definite proof that a gene or sequence variant underlying the different growth phenotype is located in the three introgressions intervals delineated by high-throughput sequencing. We have found several hundred putative We moreover cannot exclude that overall growth responses could be influenced by the different initial nutrient content in the two seed batches . This maAs gene expression analysis has been conducted with young seedlings under fully fertilized conditions without AM fungi, thus a direct link between increased mycorrhizal responsiveness and the differential gene expression results requires further experimentation. Nevertheless it is conspicuous that differential gene regulation between set A and B plants points to an overall elevated stress response level of set A plants. In particular the strongly increased expression of genes involved in the flavonoid and especially anthocyanin biosynthesis pathway  and in 2007 . Both sets were propagated exclusively after self-pollination either in the field (set A) or in the greenhouse (set B). Seed material of set B plants is available by request to the corresponding author. Plants were grown in the greenhouse with supplemental light under long-day conditions (16 h light/8 h dark) at 27°C during the day and 18.5°C during the night.Leaf material from 12 day old plants at stage V3 were sampled and immediately frozen in liquid nitrogen. One gram of leaf tissue each was ground in a mortar under liquid nitrogen and genomic DNA was prepared using the DNeasy Plant Maxi Kit (Qiagen) according to the instructions of the manufacturer.Material of the third leaf from sixteen day old plants at stage V4 were sampled and immediately frozen in liquid nitrogen. Hundred mg frozen leaf tissue each was ground in a swing mill and RNA was prepared using the RNeasy Mini Kit (Qiagen) according to the instructions of the manufacturer.Genomic DNA was fragmented to a target size of 200 bp using the Covaris S2 and the DNA microTUBE protocol (KBiosciences). Indexed libraries were prepared with the TruSeq DNA Sample Preparation Kit (Illumina) and were subsequently quantified with the KAPA Library Quantification Kit for Illumina (KAPA Biosystems) and the DNA 1000 assay on the 2100 Bioanalyzer (Agilent).Single end reads: Cluster generation on the cBot was performed using the TruSeq SR Cluster Kit v2, followed by a 101 cycles standard single-read sequencing run on the HiScanSQ using the TruSeq SBS Kit v2. Paired end reads: Cluster generation on the cBot was performed using the TruSeq PE Cluster Kit v3, followed by a 2×101+7 cycles multiplexed paired-end run on the HiSeq 2000 instrument using the TruSeq SBS Kit v3.RNAseq and preparation of the respective libraries were carried out as described in the Illumina TruSeq RNA Sample Preparation Guide, the Illumina HiScan 1000 System User Guide (Illumina), and the KAPA Library Quantification Kit - Illumina/ABI Prism User Guide (Kapa Biosystems). In brief, 1 µg of total RNA was used for purifying the poly-A containing mRNA molecules using poly-T oligo-attached magnetic beads. Following purification, mRNA was fragmented to an average insert size of 250–450 bases using divalent cations under elevated temperature (94°C for 4 minutes). Cleaved RNA fragments were copied into first strand cDNA using reverse transcriptase and random primers followed by second strand cDNA synthesis using DNA Polymerase I and RNase H. The resulting cDNA fragments subsequently went through an end repair process, the addition of a single ‘A’ base, the ligation of the adapters, and a purification step. Finally cDNA libraries were created by PCR enrichment. Libraries were quantified using the KAPA SYBR FAST ABI Prism Library Quantification Kit. Equimolar amounts of each library were used for cluster generation using the cBot (TruSeq PE Cluster Kit v3). Sequencing runs were performed on a HiSeq 1000 instrument using the indexed, 2×100 cycles paired end (PE) protocol and the TruSeq SBS v3 Kit. Image analysis and base calling resulted in.bcl files, which were converted into.fastq files by the CASAVA1.8.2 software.Genomic DNA and RNA fragmentation, library generation and sequencing were performed at the local genomics core facility “KFB - Center of Excellence for Fluorescent Bioanalytics” at the University of Regensburg. Sequence data have been deposited under the SRA accession numbers PRJEB4837 (WGS data) and PRJEB4838 (RNA-seq data).2PO4. By this experimental approach, Pi could be taken up by the mycorrhizal phosphate uptake pathway. Individual maize plants were grown in 1.1 kg of a sand:soil /soil  mixture (9∶1) in a rectangular container . The substrate was supplemented with roots of Plantago lanceolata colonized by Glomus intraradices Schenck and Smith (BEG75) (syn: Rhizophagus irregulare) (72 g/kg substrate) for the +AM treatment. As a hyphal compartment a small plastic vial was added upside down to the container including 40 g of a sand/soil mixture without inoculum and 80 mg of KH2PO4 as a Pi source. The plastic vial separates maize plant and Pi source via a semipermeable membrane, which is only permeable by fungal hyphae but not by roots of the plant. Three times a week plants were fertilized with a modified single strength Hoagland’s nutrient solution without Pi (NH4PO4 replaced by NH4Cl). Water-holding capacity was maintained at 75%. Plants were grown in the greenhouse at 24°C/20°C day/night temperature for 6–8 weeks. The first completely developed leaf (first source leaf) was harvested for ionomic analysis. For each condition/genotype three to four replicates were analyzed. For determination of the colonization degree by trypan blue staining a representative root sample was harvested per plant Maize plants were grown in a bicompartmented system consisting of a root hyphal compartment (RHC) and a hyphal compartment (HC) containing 80 mg KH3 (66%) and 2 ml of H2O2 (33%). The microwave run started with a 10 min power ramp followed by 30 min at 1400 W and finished with 15 min of cool down. A certified reference material “hay powder”  was used for control of the digestion quality. A 7700 ICP-MS instrument (Agilent) was employed for determination of approx. 20 elements following the manufacturer’s instructions.A microwave system  was employed for complete digestion of plant material for ICP-MS analysis. Approximately 0.1 g of dried and homogenized plant material  was digested using 4 ml of concentrated HNOhttp://www.r-project.org). R source code is provided as http://www.maizesequence.org).WGS reads were mapped against the maize reference sequence (AGPv2) with BWA RNA-seq reads from six samples were mapped against the maize B73 reference sequence with the Tophat spliced-alignment program A multi-sample calling was performed with the SAMtools pipeline from RNA-seq reads using the same parameters as described for the WGS data. For the visualization of all homozygous and heterozygous SNPs called in the genomic and RNA-seq samples, SNPs were filtered for minimal coverage (≥10x), SNP quality score (≥40) and genotype score (≥10). Filtered variant call files are available as Cufflinks Figure S1Determination of P-concentration in source leaves of both plant sets by ICP-MS analysis.(TIF)Click here for additional data file.Figure S2Number of homozygous SNPs in 100 kb bins for all sequenced samples .(TIF)Click here for additional data file.Figure S3Number of heterozygous SNPs in 100 kb bins for all sequenced samples .(TIF)Click here for additional data file.Table S1Functional annotation of 5,615 SNP variant sites.(XLS)Click here for additional data file.Table S2List of 121 differentially expressed genes with functional annotation.(XLSX)Click here for additional data file.Text S1R source code used to generate SNP frequency plots.(TXT)Click here for additional data file.Dataset S1Filtered SNPs in the WGS data.(TSV)Click here for additional data file.Dataset S2Filtered SNPs in the RNA-seq data.(TSV)Click here for additional data file.


Litchi is an important fruit tree in tropical and subtropical areas of the world. However, there is widespread confusion regarding litchi cultivar nomenclature and detailed information of genetic relationships among litchi germplasm is unclear. In the present study, the potential of single nucleotide polymorphism (SNP) for the identification of 96 representative litchi accessions and their genetic relationships in China was evaluated using 155 SNPs that were evenly spaced across litchi genome. Ninety SNPs with minor allele frequencies above 0.05 and a good genotyping success rate were used for further analysis. A relatively high level of genetic variation was observed among litchi accessions, as quantified by the expected heterozygosity (He = 0.305). The SNP based multilocus matching identified two synonymous groups, ‘Heiye’ and ‘Wuye’, and ‘Chengtuo’ and ‘Baitangli 1’. A subset of 14 SNPs was sufficient to distinguish all the non-redundant litchi genotypes, and these SNPs were proven to be highly stable by repeated analyses of a selected group of cultivars. Unweighted pair-group method of arithmetic averages (UPGMA) cluster analysis divided the litchi accessions analyzed into four main groups, which corresponded to the traits of extremely early-maturing, early-maturing, middle-maturing, and late-maturing, indicating that the fruit maturation period should be considered as the primary criterion for litchi taxonomy. Two subpopulations were detected among litchi accessions by STRUCTURE analysis, and accessions with extremely early- and late-maturing traits showed membership coefficients above 0.99 for Cluster 1 and Cluster 2, respectively. Accessions with early- and middle-maturing traits were identified as admixture forms with varying levels of membership shared between the two clusters, indicating their hybrid origin during litchi domestication. The results of this study will benefit litchi germplasm conservation programs and facilitate maximum genetic gains in litchi breeding programs. Litchi chinensis Sonn.), a member of the Sapindaceae family, is an important fruit tree in tropical and subtropical areas of the world. The desirable characteristics of litchi fruit, such as bright color, exotic aroma, excellent taste, and rich nutritional value, make it very attractive and popular in international markets [Litchi , and different cultivars may have the same name (homonyms) . TraditiFurthermore, detailed genetic relationships among litchi germplasm in China are presently unclear. Precise evaluation of genetic diversity and population structures of germplasm collections contributes to information regarding (a) germplasm genetic variability, (b) parental combination identifications with maximum genetic variability for breeding programs, and (c) stratification and admixture avoidance when performing genome-wide association studies. Therefore, in order to facilitate optimum germplasm management and to establish appropriate breeding programs, a more comprehensive genetic characterization of litchi germplasm collections and their genetic relationships is urgently required.Molecular genetic marker technology provides the most direct means for cultivar identification and genetic relationship analysis. A number of systems have been used in previous studies for litchi germplasm in China, including random amplified polymorphic DNA (RAPD) –11, amplRecently, single nucleotide polymorphisms (SNPs) have become the most popular molecular genetic marker in plants. Compared to previous marker systems, SNPs have several advantages. Firstly, SNPs are the most abundant sequence variation class in plant genomes . Secondlhttp://litchidb.genomics.cn/page/species/index.jsp). Deployment of available genomic resources and high-throughput tools will expedite cultivar identification standardization and the genetic relationship assessment of the litchi collection.In 2010, the litchi genome sequencing and resequencing project was initiated by researchers from the China Litchi and Longan Industry Technology Research System (Project No. CARS-33) and many genomic resources have been developed for litchi including validated SNP sequences and high-density linkage maps  assess the genetic diversity of 96 litchi accessions collected from China; (ii) evaluate SNP use to differentiate litchi genotypes and to select a core set of SNP markers for litchi cultivar identification; (iii) survey the genetic relationships and population structure among those litchi accessions. The results will facilitate the establishment of a baseline for conservation and utilization of litchi germplasm in China.All the plant materials collected from National Litchi Germplasm Gene Bank and experimental stations/orchards were permitted by Institute of Fruit Tree Research, Guangdong Academy of Agricultural Science and China Litchi and Longan Industry Technology Research System, respectively. The plant materials used in this study did not involve protected species.Ninety-six litchi accessions originating from five main litchi-producing provinces in China  were used to represent the available genetic variation of litchi germplasm . Young lThe stability analysis of a selected 14 SNP set for genetic identification was conducted using 10 cultivars, representing a large variation in the cultivated litchi. Because litchi cultivars are clones, if the SNP markers used are stable, every plant of the same cultivar should show the same genotype for each SNP. Leaf material from 98 plants belonging to those cultivars was collected from National Litchi Germplasm Gene Bank in Guangzhou and 23 experimental orchards of the China Litchi and Longan Industry Technology Research System in Guangdong, Guangxi and Fujian Provinces .Total DNA was extracted from fresh leaves of each accession using the modified CTAB method .Among SNPs developed from litchi germplasm resequencing (unpublished work), three criteria were used for SNP selection: (1) evenly spaced across the seventeen litchi linkage groups; (2) minor allele frequency (MAF) above 5%; and (3) absence of other known SNPs in their vicinity. One hundred and fifty-five SNPs were selected based on the above criteria. The name, linkage group location, and alleles for the 155 SNPs are listed in Genotyping assay for the selected SNPs was designed using the MassARRAY platform . Loci that did not satisfy the Sequenom assay technical qualifications were discarded and the 148 loci that met all requirements were genotyped on the MassARRAY platform  according to the manufacturer’s instructions, at BGI .Key descriptive statistics for measuring the informativeness of SNP markers were calculated, including minor allele frequency (MAF), observed heterozygosity (Ho), expected heterozygosity (He), and polymorphism information content (PIC). All of the estimates were calculated using PowerMarker v.3.25 software .For duplicate identification, pair-wise multi-locus matching was applied among individual accessions using GenAlEx 6.5 . AccessiThe phylogenetic tree was constructed using unweighted pair-group method with arithmetic mean (UPGMA) cluster analysis based on the p-distance model by MEGA 6.05 .K varying from 1 to 10. Ten independent runs for each K were conducted using a burn-in length of 10,000 and a run length of 100,000 iterations. We determined the optimal K using ad hoc ΔK statistics described by Evanno et al. [Bayesian clustering method implemented in STRUCTURE 2.3.3  was usedo et al. .Individuals were assigned probabilistically to populations according to their membership coefficient (q). A graphical bar plot of membership coefficients was generated using DISTRUCT software .The genetic structure of the germplasm collection was analyzed by a principal coordinate analysis (PCoA) implemented by the GenAlEx 6.5 program. PCoA was based on standardized covariance of genetic distances calculated for markers.ST statistics by Arlequin 3.5 [Analysis of molecular variance (AMOVA) was performed by decomposing the litchi accessions into four different fruit maturation-time groups using GenAlEx 6.5. The genetic differentiation between fruit maturation-time groups was analyzed using Fquin 3.5 .Of the 148 genotyped SNP markers, 17 were monomorphic across the 96 litchi accessions, 19 generated SNP profiles with more than 10% missing data, and another 22 had MAF less than 5%. The remaining 90 SNP markers were used in the subsequent data analysis.The expected heterozygosity (He) and observed heterozygosity (Ho) ranged from 0.100 to 0.500 and 0.011 to 0.813, with an average of 0.305 and 0.232, respectively . In addiNinety-four different genotypes were identified among 96 litchi accessions based on the 90 SNPs. Two synonymous groups were identified, including ‘Heiye’ from Guangdong Province and ‘Wuye’ from Fujian Province, ‘Chengtuo’ from Guangdong Province and ‘Baitangli 1’ from Guangxi Province.Using 62 SNPs that had no missing data, 85 non-redundant genotypes were identified. The number of genotypes identified increased with the addition of SNPs, and 14 SNPs were enough to distinguish all 85 genotypes .Stability of the 14 SNPs for cultivar identification was evaluated by the genotype analysis of 9 plants (on the average) for each of 10 cultivars. The 10 cultivars exhibited phenotypic diversity for several important litchi traits, including fruit color (green and red), presence of seeds (seeded and seedless), ripping period  . The 10 Ninety-eight plants were analyzed with the selected 14 SNP set. In order to analyze genetic relationships among accessions, a phylogenetic tree applying the UPGMA method was constructed based on the SNP genotype data of the 96 litchi accessions. The dendrogram revealed the classification of analyzed accessions into four clusters, and there was a perfect association between SNP genotypes and fruit maturation time since each cluster contained accessions of one maturation-time group . ClusterIn addition, principal coordinate analysis, which was performed as a complementary approach to display genotype clusters, also separated 96 litchi genotypes into four major clusters, which was consistent with assignments generated by the UPGMA clustering analysis . PCO 1 aΔK corresponded to K = 2, indicating that there were two major subpopulations. The membership coefficient (q) is presented in As a complement, the STRUCTURE analysis was used to reveal the number of subpopulations composing the genetic structure of the litchi germplasm collection. The maximum ST statistics estimated from pairwise comparisons. Pairwise comparisons showed considerable variation in the FST values, ranging from 0.153 to 0.625 (ST = 0.153), and the highest genetic differentiation was between the late-ripening group and the extremely early-ripening group (FST = 0.625)  . Geneticto 0.625 . The low= 0.625) .This is the first study in which the genetic diversity of 96 representative litchi genotypes in China was investigated using SNP markers. The genetic variation revealed among litchi accessions (He = 0.305) was higher than value for citrus based on SNPs , and comIn general, diversity values (He) for SNPs are lower than those estimated for SSR markers because of the SNP marker’s bi-allelic nature –41. HoweAs the largest litchi-producing country, the use of homonyms and synonyms for cultivar designation is a big problem in Chinese litchi genotypes. Several previous studies have reported many synonyms and homonyms in litchi collections in China –11, 17, Due to the problem of homonyms and synonyms in litchi cultivar nomenclature, accurate characterization of litchi germplasm is a primary concern. Molecular markers are the chosen methods for cultivar identification in many crops –46. As fIn this study, we investigated the possibility of using SNP markers for litchi cultivar identification. Our results showed 14 SNPs were sufficient to distinguish the 85 non-redundant litchi genotypes. These SNPs were found to be highly stable by repeated analyses in a selected group of cultivars. Therefore, the SNP genotypes in these 14 SNPs could be used as reference genetic barcodes for each analyzed litchi accession. As SNPs are bi-allelic, allele identification and genotype recording are always comparable among different genotyping platforms and laboratories, the litchi SNP barcode database constructed in this study allowed for the standardization of cultivar nomenclature.In the course of litchi’s dissemination to other countries, there has been considerable confusion regarding the identity of the original Chinese litchi cultivars, owing to misidentification or mislabeling, the corruption in the English transliteration of Chinese dialects, the replacement of the original Chinese names by local names, and the poor documentation of passport information during germplasm introduction –53. OnlyUnderstanding the genetic relationships and population structures of litchi germplasm is crucial for litchi breeding projects by facilitating the selection of optimal parental combinations, and for germplasm management to avoid genetic redundancy. Traditionally, the most commonly used classification criterion for Chinese litchi accessions is the shape of skin segments and protuberances, based on which the germplasm are divided into three types: smooth protuberances , protruding and hard protuberances , and hair-like protuberances  . HoweverIn contrast, the UPGMA dendrogram revealed four groups of litchi genotypes that were congruent with fruit-maturation time, and similar results were reported in Liu and Mei , and Fu Litchi chinensis Sonn. [In our UPGMA dendrogram, six accessions with extremely early-ripening time grouped together remotely from the other litchi accessions, indicating a divergent origin. Similar results were reported in Liu and Mei  who founis Sonn. .The model-based STRUCTURE analysis revealed the presence of two clusters among the collected litchi genotypes. Accessions with extremely early-ripening and late-ripening time were assigned to Cluster 1 and 2 with nearly total ancestry, respectively. However, accessions with early-ripening and middle-ripening times were identified as admixed individuals with varying levels of membership shared between these two clusters, indicating a hybrid origin of these accessions during litchi domestication. The evolution of cultivated litchi regarding its geographic origin and number of domestication has been historically unclear . TherefoS1 Table(XLSX)Click here for additional data file.S2 Table(XLSX)Click here for additional data file.