The tertiary gene pool of wheat is a very important source of genetic variability for wheat improvement against biotic and abiotic stresses. By exploring the 5Mg of Ae. geniculata Roth  was flow-sorted from a wheat line in which it is maintained as a telocentric chromosome. DNA of the sorted arm was amplified and sequenced using an Illumina Hiseq 2000 with ~45x coverage. The sequence data was used for SNP discovery against wheat homoeologous group-5 assemblies. A total of 2,178 unique, 5MgS-specific SNPs were discovered. Randomly selected samples of 59 5MgS-specific SNPs were tested (44 by KASPar assay and 15 by Sanger sequencing) and 84% were validated. Of the selected SNPs, 97% mapped to a chromosome 5Mg addition to wheat (the source of t5MgS), and 94% to 5Mg introgressed from a different accession of Ae. geniculata substituting for chromosome 5D of wheat. The validated SNPs also identified chromosome segments of 5MgS origin in a set of T5D-5Mg translocation lines; eight SNPs (25%) mapped to TA5601 [T5DL · 5DS-5MgS(0.75)] and three (8%) to TA5602 [T5DL · 5DS-5MgS (0.95)]. SNPs (gsnp_5ms83 and gsnp_5ms94), tagging chromosome T5DL · 5DS-5MgS(0.95) with the smallest introgression carrying resistance to leaf rust (Lr57) and stripe rust (Yr40), were validated in two released germplasm lines with Lr57 and Yr40 genes.The short arm of chromosome 5MThis approach should be widely applicable for the identification of species/genome-specific SNPs. The development of a large number of SNP markers will facilitate the precise introgression and monitoring of alien segments in crop breeding programs and further enable mapping and cloning novel genes from the wild relatives of crop plants. Triticum aestivum L., 2n = 6x = 42, AABBDD genome), which accounts for 95% of the harvested wheat crop, traces its origin to a rare hybridization event ~6,000 years ago involving T. turgidum L.  and Aegilops tauschii Coss.  , and three wheat-Ae. geniculata translocation lines TA5599, TA5601, and TA5602 with 75%, 25%, and 5% of Ae. geniculata (accession TA10437) 5Mg chromosome, respectively [gS) line (TA7670) in wheat [gL · MgS (0.25)] and TA5601 [T5DL · 5DS-5MgS (0.75)] are BC2F5 hybrids derived from a disomic substitution line TA6675 [DS5Mg(5D)] and Chinese Spring (CS)PhI stock [3F6 line derived in a similar way [Lr57 and Yr40 genes. A set of Ae. geniculata accessions  and wheat lines  were also used in this study. Two germplasm lines (KS11WGGRC53-J and KS11WGGRC53-O) released by the WGRC (http://www.k-state.edu/wgrc/Germplasm/grmplsm.html) were used to validate the 5MgS SNPs mapped in TA5602. The germplasm KS11WGGRC53-J was developed by crossing the translocation line TA5602 and winter wheat cultivar Jagger  whereas KS11WGGRC53-O was developed by crossing TA5602 with winter wheat cultivar Overley .All plant materials used in this study were procured, developed and maintained by the Wheat Genetics Resource Center at Kansas State University  [gS was run in a single lane at DNA core facility services at University of Missouri, USA. For this project we opted for single read sequencing where only one end is sequenced. Sequence data generated from the short arm of Ae. geniculata chromosome arm 5Mg was used for SNP discovery.Aqueous suspensions of mitotic metaphase chromosomes were prepared from wheat-(TA7670)  followin(TA7670) . The sam(TA7670) . The DNA(TA7670) . The thrhttp://www.wheatgenome.org/). The 5MgS reads were mapped on these assemblies. In the second step, variants were called against each individual assembly . We used the Bowtie software (v1.3) (http://bowtie-bio.sourceforge.net/index.shtml) to map quality filtered reads of 5Mg to the Chinese Spring 5A, 5B and 5D contigs separately  were used to generate pileup files, which were then fed to BCFtools (http://samtools.sourceforge.net/) to call raw variations using default parameters. All variations with coverage depth > =4, SNP quality > =50 were kept for subsequent analysis  to assemble contigs for the 5Mg genome, with parameter -k 27.Before aligning reads to references, all reads were first trimmed to remove the low-quality bases (phred score < =15) on the end of the reads; reads with more than 30 bp after quality trimming were then filtered with an overall quality . NGS-based SNP discovery involved two basic steps: (1) alignment of NGS on a reference genome sequence, also called read mapping, and (2) variant calling from the aligned sequences. Variant calling is much easier if a reference sequence is available, because short reads with deep sequencing coverage increases the confidence level for SNP discovery. In this study, we used Chromosome Shotgun Sequences (CSS) as reference sequences and assemblies from wheat chromosome arms 5AS, 5BS and 5DS developed under the International Wheat Genome Sequencing Consortium Survey Sequencing Initiative . Of 15 primers, only 12 primer pairs generated PCR products in both Chinese Spring and Ae. geniculata TA2899. Failure to amplify the target DNA by two primer pairs was due to suboptimal PCR conditions, later confirmed by optimization. The PCR products were eluted from the electrophoresis gels and purified. Purified products were then sequenced with an Applied Biosystems 3730xl DNA Analyzer  and sequences were aligned and compared for SNPs.To assess the veracity of the discovered SNPs and estimate the false-positive SNP discovery rate, 15 sequences, with one SNP each, were randomly chosen from the SNP reference sequences. SNP flanking primers were designed with Primer3 (g short arm sequence data has been submitted to SRA under accession SRX474187. An additional table and two additional figures were also included in the manuscript.The 5MThe authors declare that they do not have any competing interests.All the listed authors contributed significantly to the manuscript. VKT, JS, SKS and BSG planned the research. BF, BK and PC contributed to the germplasm development used in the experiments. JV, MK and JD carried out chromosome flow-sorting and DNA amplification experiments. VKT, SW, SKS and EA contributed to DNA sequencing, sequence analysis, SNP discovery, validation and introgression mapping experiments. VKT and BSG prepared the manuscript. All authors read and approved the final manuscript.gS assembled sequences.The frequency and coverage depth of 5MClick here for filegS sequences against reference contigs 5AS, 5BS and 5DS.Coverage depth of 5MClick here for filegS-based, genome-specific SNP markers developed by mapping the 5Mg Illumina reads on 5AS, 5BS and 5DS assemblies of T. aestivum cv. Chinese Spring.List of 104 5MClick here for file


Camellia sinensis. Many wild relatives of the genus Camellia that are closely related to C. sinensis are native to Southwest China. In this study, we first identified the distinct genetic divergence between C. sinensis and its wild relatives and provided a glimpse into the artificial selection of tea plants at a genome-wide level by analyzing 15,444 genomic SNPs that were identified from 18 cultivated and wild tea accessions using a high-throughput genome-wide restriction site-associated DNA sequencing (RAD-Seq) approach. Six distinct clusters were detected by phylogeny inferrence and principal component and genetic structural analyses, and these clusters corresponded to six Camellia species/varieties. Genetic divergence apparently indicated that C. taliensis var. bangwei is a semi-wild or transient landrace occupying a phylogenetic position between those wild and cultivated tea plants. Cultivated accessions exhibited greater heterozygosity than wild accessions, with the exception of C. taliensis var. bangwei. Thirteen genes with non-synonymous SNPs exhibited strong selective signals that were suggestive of putative artificial selective footprints for tea plants during domestication. The genome-wide SNPs provide a fundamental data resource for assessing genetic relationships, characterizing complex traits, comparing heterozygosity and analyzing putatitve artificial selection in tea plants.Tea is one of the most popular beverages across the world and is made exclusively from cultivars of  Camellia sinensis (L.) O. Kuntze, which belongs to the section Thea of the genus Camellia in the family Theaceae. C. sinensis includes two main varieties, i.e., C. sinensis var. sinensis and C. sinensis var. assamica.Tea is one of the most popular non-alcoholic beverages and is consumed by more than one third of the world’s population due to its stimulant effects, attractive aroma, refreshing taste and health benefits . The ancC. sinensis, such as C. tachangensis, C. taliensis and C. crassicolumna and C. gymnogyna etc., all of which belong to section Thea  usi usiC. si of 1e-5 . The six of 1e-5 . Three s of 1e-5 . A total of 1e-5  and “ter of 1e-5 , which aArabidopsis (dN/dS = 0.8) [Additionally, we identified 453 genic SNPs that were located in the coding sequences of unigenes. Of these genic variations, 238 were non-synonymous substitutions, and 215 were synonymous . The ratS = 0.8) . The nonTo assess the accuracy of genic SNP identification and RAD-Seq-based genotyping analysis, we randomly selected 50 genic SNP loci from 900 genotypes across all of the 18 tested accessions to conduct PCR-based sequencing using SNP loci-specific primers . We founwild = 0.13 to 0.57) but had a fixed genotype at each locus in the cultivated accessions (πcultivar = 0). Transitions and transversions accounted for 60.1% and 39.9%, respectively. We suggested that the loss of heterozygosity in the 644 SNP loci was probably due to the selection pressures of tea domestication.To identify the putative selective footprints of tea domestication, we calculated the divergence statistic π and the loss of diversity (LOD)  between C. sinensis cv. Longjing43 unigenes [Arabidopsis At5g66180, encoding an S-adenosyl-L-methionine (SAM)-dependent methyltransferase that catalyzes universal methylation. The SAM-dependent methyltransferase superfamily plays important roles in plant development [Pik (NBS-LRR gene), one of the five classical alleles located at the Pik locus on chromosome 11, has been characterized to be a younger allele emerging noly after rice domestication rather than evolving as a result of a duplication event [Eighty-one of the 644 SNPs were located in genic regions. Correspondingly, the SNP-associated RAD tags exhibited the best alignments with unigenes . We idenelopment , biosyntelopment . Moreoveelopment . In riceon event . These fC. sinensis and its wild relatives. This approach could easily be extended to include other restriction enzymes and identify additional SNPs to further enrich tea plant molecular genetic resources and improve our understanding of the effects of single nucleotide mutations on phenotypic traits.The high heterozygosity of the tea genome was a barrier to the acquisition of detailed genomic information. In contrast to whole-genome sequencing approaches, the RAD-Seq approach focuses on single allelic differences or variations in smaller, more manageable portions of the genome that contain restriction sites and flanking sequences. Our results demonstrated the efficiency and cost-effectiveness of RAD-Seq technology in the generation of high-throughput genomic SNPs in C. taliensis var. bangwei is the only semi-wild tea plant that has been reported [Pedicularis [Camellia spp. to more fully understand the phylogenetic relationships of the genus Camellia with SNPs at the genome-wide level, we will address the controversial taxonomy of the genus Camellia, decipher the origin and evolution of tea and benefit genetic breeding and improvements in tea.The identified genomic SNPs first provided genome-wide information for the investigation of the genetic relationship and comparisons of the heterozygosities of the test cultivated and wild tea accessions in comparison with previous studies , 22–26. reported  until noicularis , temperaicularis  and Chinicularis  that useC. sinensis cv. Longjing43 [Camellia plants.In addition, the completement and high-quality of the reference database is another key factor for the bioinformatic analysis of SNPs. Although we used our previous tea transcriptome dataset from all tissues of ngjing43  as the rThis study confirms that cultivated and wild tea plants are highly heterozygous presumably because of high self-incompatibility. Because the heterozygous rates of each accession were estimated based on shared SNP-associated genomic regions, the results can be used to compare of the relative heterozygosities of cultivated and wild tea genomes. It is important to note that RAD DNA fragments offer a reduced representation of the genome that contains only the restriction sites and their flanking sequences. The absolute nucleotide heterozygous rates across the entire genome cannot be extracted using this approach and can only be determined with whole genome sequencing. Accessions with lower heterozygosities are better suited to genome sequencing using NGS approaches.Thea of the genus Camellia were used in this study  and six accessions of C. sinensis var. sinensis . Csa-1 and Csa-2 were sampled with the permission of the Menghai Agriculture Committee of the Yunnan province. Csa-3 was developed from an ancient cultivated population in the Yunnan province using individual selective breeding methods and was sampled by the Tea Research Institute of the Yunnan Academy of Agricultural Science. Among the six Css accessions, Css-1, Css-2, Css-3, Css-4 and Css-5 are currently the main cultivars used in tea production, and these were sampled from three tea-producing regions in China; in contrast, Css-6 is an F1 individual that resulted from a cross between Csa-3 and Css-5. Permission for the tissue sampling of Css-1 and Css-2 from agricultural plantations was obtained from Anhui Agricultural University. Sampling permission for Css-3, Css-4, Css-5 and Css-6 was obtained from the Tea Research Institutes of the Academies of Agricultural Science in Anhui, Fujian and Yunnan, respectively. The other nine tea accessions are closely related to cultivated tea varieties and were sampled from trees in Yunnan province that are hundreds of years old. Among them, three accessions  belong to C. taliensis, four  belong to C. crassicolumna, the Ctg accession belongs to C. tachangensis, and the Ctb accession belongs to C. taliensis var. bangwei, that is the only known semi-wild tea plant in the world based on evidence from morphological trait and karyotype analyses [Ctl-1 and Ctl-2, Ctl-3 and Ctb, and Ctg were obtained from the Menghai, Shuangjiang and Fuyuan Agriculture Committees in the Yunnan province, respectively. Ccc-1, Ccc-2, Ccc-3 and Ccc-4 were sampled with permission from the Tai Wai Mountain National Nature Reserve in the Yunnan province. All tissue sampling was performed under the supervision of local foresters, and the samples were used only for scientific research. The non-invasive sampling performed in this work did not affect the natural growth of the Camellia plants.A total of 18 cultivated and wild tea accessions belonging to the section is study . The ninanalyses . PermissBuds and young leaves were randomly sampled from healthy young shoots of each accession and immediately frozen in liquid nitrogen. All samples were stored at −80°C until needed for DNA isolation. DNA samples were extracted from the buds and young leaves using a plant genomic DNA kit  following the manufacturer’s protocol. Residual RNA was removed from the genomic DNA by the treatment with RNase.et al [EcoRI (New England Biolabs) was used. Specific 4–8-bp nucleotide barcodes contained in the modified Illumina P1 adapters were used for sample tracking. To distinguish accession-specific barcodes from random single nucleotide differences caused by sequencing errors, the barcodes differed by at least two nucleotides between the different accessions. Subsequently, adapter-ligated DNA fragments were pooled and sheared to a mean size of 500 bp and separated with 2% agarose gel electrophoresis. Fragments of 350–500 bp were isolated using a MinElute Gel Extraction kit (Qiagen), treated with end-blunting enzymes, 3'-adenine overhangs were added, and the fragments were ligated with modified Illumina P2 adapters. Finally, the RAD-Seq libraries were enriched by PCR amplification and sequenced on an Illumina Hiseq 2000  using single-ended reads (50 bp) for each accession.RAD sequencing was performed as reported by Chutimanitsakun et al  with theEcoRI recognition site AATTC (reads lacking unique barcodes and the specific sequence were discarded). The final clean reads were further trimmed to a uniform length of 41 nucleotides that included 5 nt of the EcoRI recognition site and 36 nt of potentially variable sequence.The Illumina sequence reads were quality-filtered by removing the adapter sequences and reads containing greater than 50% low-quality bases . All reads were assigned to the tested accessions with unambiguous barcodes and the de novo using Stacks software [et al [A2 / DepthA1 <0.05) or as heterozygotes when DepthA2 / DepthA1 >0.1. Nucleotide sites with DepthA2 / DepthA1 value between 0.05 and 0.1 were discarded to minimize genotyping inaccuracies. After genotyping, a consensus sequence was assigned to each RAD tag locus.Because a reference tea genome sequence is not currently available, the identification of SNPs was implemented software . Brieflye [et al , if the Consensus sequences from each accession were compared across all accessions with a maximum of one mismatch allowed to generate putative SNP loci. After filtering, the RAD tag loci were genotyped for at least 14 of the 18 accessions , and those containing only one bi-allelic SNP within the 36 nt of potentially variable sequence in each locus were retained to generate high-confidence SNPs.p-distance, defined as Dij between two accessions (i and j), was calculated using the following equation:L is the length of the regions from which high-quality SNPs could be identified, and given that the allele at certain position was C/T, i and j were CC and CC, to 0.5 if the genotypes of i and j were CC and CT, and to 1 if the genotypes of i and j were CC and TT. The To construct the phylogenetic tree, the genetic distances between the different accessions were calculated based on the high-confidence SNPs extracted from the RAD data. The th MEGA5 , with boPrincipal component analysis was performed as previously reported . The decK) based on a maximum likelihood algorithm [K values were set from two to six.The analyses of the genetic structures of the tea accessions were performed using the program FRAPPE . The indlgorithm . The K vH is the heterozygosity of a given tea accession, NhSNP is the number of heterozygous SNPs identified in the 15,444 SNPs shared by 18 tea accessions, and LRAD-genome is the total length of the RAD tags containing the 15,444 SNPs (41 nt of each RAD tag).The heterozygosity rates of the 18 tested tea accessions were evaluated by calculating the ratios of the numbers of heterozygous SNPs to the lengths of the shared SNP-associated genome fragments obtained from RAD sequencing in each accession using the following equation:C. sinensis cv. Longjing43 (sample ID: Css-2) [ftp://ftp.ncbi.nlm.nih.gov/blast/executables/blast+/2.2.29/). Strict thresholds were set with an E-value cut-off of 1e-5. A maximum of one mismatch was allowed, and alignment lengths above 80% and identities greater than 90% were required. For the gene annotations of the identified genic SNP-associated unigenes, the SNPs were compared with the Arabidopsis protein dataset using BLASTX with a strict E-value threshold of 1e-5. Functional classification according to GO terms [Arabidopsis protein datasets using Blast2GO software (version 2.3.5) [C. sinensis cv. Longjing43 unigenes, we also identified the non-synonymous and synonymous substitutions from the coding sequences of the tea unigenes [Among the 15,444 bi-allelic SNPs, the genic SNPs were identified based on the sequence alignments of the 15,444 SNP-associated RAD tag sequences against the tea transcriptome dataset  from : Css-2)  using thGO terms  was perfn 2.3.5)  with an unigenes .C. sinensis cv. Longjing43 (sample ID: Css-2) transcriptome, we designed the SNP loci-specific primers according to the flanking sequences from the unigenes adjacent to the aligned regions using Primer Premier software . The PCR amplifications were performed in 25 μL of reaction volumes, containing 0.5 U Taq polymerase (TaKaRa), 5 nmol of each primer, and 10–30 ng DNA templates. The reactions were performed in a Bio-Rad Sequence Detection System with the following cycling parameters: 94°C for 3 min; 35 cycles of 94°C for 30 s; annealing at an optimum temperature for 30 s; 72°C for 30 s; and a final extension at 72°C for 10 min. The PCR products were sparated using agarose gel electrophoresis, purified and recovered using PCR purification kits and subjected to bi-directional sequencing on an ABI3730xl sequencer . At each SNP locus, sequences of all 18 accessions obtained by Sanger sequencing were aligned with SNP loci-associated RAD tag sequences using DNAman software.To experimentally validate the reliability of the SNP loci and genotyping of all of the 18 tested tea accessions, we randomly chose 50 identified genic SNP loci to perform 900 PCR amplifications and Sanger sequencing with SNP loci-specific primers. According to the best BLAST hits for the SNP loci-associated RAD tags with unigenes from the cultivated) and wild groups (π wild) were calculated for each SNP locus with an in-house PERL script. According to the results from genetic relationship analysis, 6 Css accessions and 3 Csa accessions were included in the cultivated group, and the wild group was composed of all of the other 8 wild accessions except Ctb. We estimated the value of the loss of diversity (LOD) to detect the regions that were putatively under selection pressure [The average pairwise divergences between the cultivated , one kind of important secondary metabolites in tea, include non-galloylated catechins (epicatechin (EC), catechin (C), epigallocatechin (EGC), gallocatechin (GC)) and their gallate esters  and epigallocatechin gallate (EGCG)) . Catechiet al . Briefly−1 with the mobile phase containing 0.17% (v/v) acetic acid (A) in water, 100% acetonitrile (B), and the gradient elution was as follows: B 6% from 0 to 4 min, to 14% at 16 min, to 15% at 22 min, to 18% at 32 min, to 29% at 37 min, to 45% at 45 min, to 45% at 50 min, to 6% at 51 min and to 6% at 60 min. Samples from all tested accessions as mentioned above in RAD-Seq were analyzed in triplicate. The standards (purities > 98%) of gallic acid (GA), (+)-C, (−)-EC, (+)-GC, (−)-EGC, (+)-GCG, (−)-EGCG, and (−)-ECG were purchased from Shanghai Winherb Medical Science Co.,Ltd.,Shanghai, P.R. China.The filtered sample (10 μL) was injected into a Waters 2695 HPLC system equipped with a 2489 ultraviolet (UV)-visible detector for detection of the catechins contents in the extracts. The detection wavelength was set to 278 nm. A reverse-phase C18 column  was used at 25°C. The samples were eluted at a flow-rate of 1 mL minCamellia sinensis and its wild relatives from the genus Camellia in the absence of prior genome sequences. The identified genomic SNPs have not only considerably increased the available molecular markers of Camellia but also provided comprehensive information about the genetic divergence and variant heterozygosities between cultivated and wild teas at the genome-wide level. These SNPs also provide the oppprtunity to glimpse putative selective footprints in tea plants. Genic SNPs related to functional genes, especially those involved in secondary metabolic processes, were identified and experimentally validated, which will aid future research on the molecular mechanism of distinct phenotypic traits of cultivated and wild teas. The genomic SNP data extend our knowledge of Camellia genomes, and the methods developed here can be applied to future genomics and phylogenomic studies and breeding programs for Camellia and other plants.In this study, we applied RAD-Seq technology for the rapid and cost-effective discovery of 15,444 genomic SNPs from 18 tea accessions of S1 Fig(TIF)Click here for additional data file.S2 Fig(TIF)Click here for additional data file.S3 Fig(TIF)Click here for additional data file.S1 Table(DOC)Click here for additional data file.S2 Table(XLSX)Click here for additional data file.S3 Table(DOC)Click here for additional data file.S4 Table(XLSX)Click here for additional data file.S5 Table(XLSX)Click here for additional data file.S6 Table(XLSX)Click here for additional data file.S7 Table(XLSX)Click here for additional data file.S8 Table(XLSX)Click here for additional data file.S9 Table(DOC)Click here for additional data file.S10 Table(XLSX)Click here for additional data file.S11 Table(XLSX)Click here for additional data file.S12 Table(XLSX)Click here for additional data file.


In regions where malaria is endemic, individuals are often infected with multiple distinct parasite genotypes, a situation that may impact on evolution of parasite virulence and drug resistance. Most approaches to studying genotypic diversity have involved analysis of a modest number of polymorphic loci, although whole genome sequencing enables a broader characterisation of samples.Plasmodium falciparum in 95 clinical isolates from a highly endemic area in the Republic of Guinea, to characterize within-isolate genetic diversity. Separately, single nucleotide polymorphism (SNP) data from genome-wide short-read sequences of the same samples were used to derive within-isolate fixation indices (Fws), an inverse measure of diversity within each isolate compared to overall local genetic diversity. The latter indices were compared with the microsatellite results, and also with indices derived by randomly sampling modest numbers of SNPs.PCR-based microsatellite typing of a panel of ten loci was performed on Fws fixation index . However, the microsatellite analysis revealed that most isolates contained mixed genotypes, even those that had no detectable genome sequence heterogeneity. Random sampling of different numbers of SNPs showed that an Fws index derived from ten or more SNPs with minor allele frequencies of >10 % had high correlation (r > 0.90) with the index derived using all SNPs.As expected, the number of microsatellite loci with more than one allele in each isolate was highly significantly inversely correlated with the genome-wide Fws).Different types of data give highly correlated indices of within-infection diversity, although PCR-based analysis detects low-level minority genotypes not apparent in bulk sequence analysis. When whole-genome data are not obtainable, quantitative assay of ten or more SNPs can yield a reasonably accurate estimate of the within-infection fixation index (The online version of this article (doi:10.1186/s12936-016-1324-4) contains supplementary material, which is available to authorized users. Plasmodium chabaudi in mice, multiple genotype infections have been associated with apparent short-term evolution of virulence [Malaria parasite blood-stage infections commonly contain a mixture of different haploid parasite genotypes, particularly in areas of high endemicity where superinfection frequently occurs . Cross-mirulence , alteratirulence , 9, and irulence . If suchirulence , 11.P. falciparum, the causative agent of most human malaria cases globally [P. falciparum, which inversely correlates with local levels of multilocus linkage disequilbrium [P. falciparum infections has been performed using genome-wide single nucleotide polymorphism (SNP) data, showing that a high degree of relatedness is seen among some distinct parasite clones within infections, in comparison to those sampled from separate infections [P. falciparum infection may be comprised of a mixture of closely related, non-identical parasites, or multiple genetically unrelated parasites, or it may be a complex mixture of both.Previous analyses of within-host diversity of globally , have tyglobally , or multglobally . These huilbrium , 14–17. fections . This ilFws [The relative proportions of SNP alleles in whole-genome sequence data from an infection can be estimated from the proportions of reads mapping to a reference sequence, and this allows computation of a within-isolate fixation index, Fws , 20. ThiP. falciparum within a highly endemic population in West Africa has been characterized using two distinct methods. Firstly, microsatellite PCR-based genotyping was performed with a panel of ten loci widely distributed in the parasite genome, and then whole-genome sequence data from the same samples were used to analyse SNPs in order to compute the Fws indices. The relationship between these different types of estimates is examined, and the use of small numbers of SNPs to derive Fws indices is also illustrated, so that the potential value of SNP genotyping may be considered when whole-genome data are not obtainable.Here, the within-host diversity of P. falciparum malaria, as confirmed by rapid diagnostic test , were recruited from local health facilities within a 25-km radius of the town of N’Zerekore in the Republic of Guinea, between March and May 2011. Written informed consent was obtained from a parent or guardian of each child sampled, and all patients diagnosed with malaria were treated with artesunate-amodiaquine regardless of study participation. Ethical approval for the sample collection for parasite sequencing and genotypic analysis was obtained from the Comité d’Ethique National pour la Recherche en Santé, République de Guinée . Following detection of P. falciparum infection, up to 5 mL of venous blood was collected in EDTA (ethylenediaminetetraacetic acid) vacutainers from consenting subjects and leukocyte depletion of each sample was performed using CF11 cellulose column filtration [Patients between the ages of one and 9 years presenting with uncomplicated ltration , and theltration .Microsatellite genotyping was performed on 95 isolates, out of the 100 for which genome sequence data were separately published . Ten polP. falciparum clinical isolates genotyped here are published and available at the European Nucleotide Archive as previously described [Fws metric [p and q representing the proportions of read counts for the minor and major alleles. All SNPs were then assigned to ten minor allele frequency (MAF) intervals representing the proportional frequency of the minor allele at each SNP across the Guinean population, with the ten equally sized intervals ranging from 0–5 % up to 45–50 %. Levels of within-host (Hw) and local parasite sub-population (Hs) heterozygosity for each SNP were calculated as Hw = 2*pw*qw and Hs = 2*ps*qs. The mean Hw and Hs of each MAF interval were then computed from the corresponding heterozygosity scores of all SNPs within that particular interval. The resulting—plot of Hw against Hs for each isolate was produced and a linear regression model was used to determine a value for the gradient Hw/Hs−, with Fws = 1−(Hw/Hs). All Fws analyses were performed using custom scripts in R.The whole genome illumina paired-end, short-read sequence data from the escribed . Quantifs metric , 20. ForFws indices were then derived from sampling a small number (between one and 20) of randomly selected SNPs and compared with the genome-wide indices previously determined. A Pearson’s correlation coefficient with the genome-wide SNP estimate of Fws was determined across all isolates. For each limited SNP selection of n SNPs, 100 random samples of n SNPs were analysed and the mean Pearson’s coefficient observed for these was calculated. This analysis was repeated and comparisons performed between sets of SNPs with different ranges of overall minor allele frequencies.P. falciparum clinical isolates  was derived, and the gradient of Hw/Hs  was calculated to derive the Fws index for each isolate . Over all 95 clinical isolates, the mean genome-wide Fws index was 0.79, with a range of 0.16 to 1.00  indicating that they each contain a single predominant haploid genome sequence, with any additional genotypes being rare or absent, or closely related to the predominant genotype. Conversely, 45 of the clinical isolates (47.4 %) had an Fws index of <0.95 and were thus clearly genotypically mixed infections. No significant correlation was seen between average genome sequence read mapping depth and Fws index (Pearson’s r = 0.02), indicating that there was no bias in estimating within-host diversity due to sequence coverage.Genome-wide SNP data for each of the isolates were used to generate within-host Fws fixation index derived from the SNP analysis and the number of microsatellite loci with multiple alleles detected within each infection  . However, many infections that appeared to be unmixed on the basis of the genome sequence data (having an Fws index of >0.95) were shown to have multiple genotypes as assessed by PCR-based microsatellite genotyping, with some of these infections having multiple alleles at several of the microsatellite loci . As expected, there was a highly significant negative correlation between the 01) Fig. . There wFws indices as above was performed with genome-wide SNP data for each infection, although such data will be rarely available for new clinical samples. In order to investigate the potential of using small numbers of SNPs, data from up to 20 randomly chosen SNPs were used to estimate Fws indices, and correlations with values derived from genome-wide SNP data were determined  which may be derived from genome-wide short read sequence data if this is available, or alternatively can be estimated from a modest number of randomly sampled SNPs which could be genotyped by other methods. Multilocus microsatellite PCR-based genotyping gives estimates of infection complexity that correlate strongly with those from the SNP analyses, while also being more sensitive to detect additional genotypes in some infections that appear to have unmixed sequences. With a wide range of methods now available, studies can choose genotyping and analytical approaches to suit investigational goals, recognizing relative advantages of each in relation to the costs and available resources.This study shows that estimates of genotypic complexity of malaria parasite infections using very different methods give correlated and complementary information. The within-infection fixation index


TaGW2 is an orthologue of rice gene OsGW2, which encodes E3 RING ubiquitin ligase and controls the grain size in rice. In wheat, three copies of TaGW2 have been identified and mapped on wheat homoeologous group 6 viz. TaGW2-6A, TaGW2-6B and TaGW2-6D. In the present study, using as many as 207 Indian wheat genotypes, we identified four SNPs including two novel SNPs (SNP-988 and SNP-494) in the promoter sequence of TaGW2-6A. All the four SNPs were G/A or A/G substitutions (transitions). Out of the four SNPs, SNP-494 was causal, since it was found associated with grain weight. The mean TGW (41.1 g) of genotypes with the allele SNP-494_A was significantly higher than mean TGW (38.6 g) of genotypes with the allele SNP-494_G. SNP-494 also regulates the expression of TaGW2-6A so that the wheat genotypes with SNP-494_G have higher expression and lower TGW and the genotypes with SNP-494_A have lower expression but higher TGW. Besides, SNP-494 was also found associated with grain length-width ratio, awn length, spike length, grain protein content, peduncle length and plant height. This suggested that gene TaGW2-6A not only controls grain size, but also controls other agronomic traits. In the promoter region, SNP-494 was present in ‘CGCG’ motif that plays an important role in Ca2+/calmodulin mediated regulation of genes. A user-friendly CAPS marker was also developed to identify the desirable allele of causal SNP (SNP-494) for use in marker-assisted selection for improvement of grain weight in wheat. Using four SNPs, five haplotypes were identified; of these, Hap_5 (G_A_G_A) was found to be a desirable haplotype having significantly higher grain weight (41.13g) relative to other four haplotypes (36.33-39.16 g). One of the most important traits contributing to grain yield is grain size (thousand grain weight = TGW), which is also related to higher flour recovery [Improvement in average grain yield in wheat has attracted the attention of wheat breeders all over the world, and several initiatives in this direction have recently been taken, both at the national and international levels. These include Wheat Inititaive  in the promoter region of TaGW2-6A, and an insertion of a single T-base in the eighth exon of this gene  were shown to be associated with grain size [TaGW2 and grain size was observed in two earlier studies [TaGW2 transcript levels, led to reduction in endosperm cell number associated with reduction in grain size [TaGW2-6A encodes a functional E3 RING ubiquitin ligase with nucleocytoplasmic subcellular partitioning.Grain weight is a polygenic trait and is controlled by a large number of genes/QTL that are distributed on all wheat chromosomes , 5–21. ATaGW2-6D . Two SNPain size –23. Alth studies –23, a poain size . FurtherTaGW2-6A in a collection of 207 Indian wheat genotypes. Interestingly, we found two novel SNPs (one SNP present in CGCG motif) in the promoter region along with two other SNPs that were also reported by Su et al. [TaGW2 in developing grains so that the negative regulation of the gene expression was associated with higher grain weight. A functional marker (cleaved amplified polymorphic sequence—CAPS) was also developed for identification of individual alleles of causal SNP for use in wheat breeding programs aimed at grain weight improvement.In the present study, we analysed sequence polymorphism in the promoter region of u et al. . A studyThe plant material used in this study comprised as many as 207 Indian wheat genotypes, released during 1910–2006 for commercial cultivation in different agro-climatic regions of India. The seed of the above genotypes was procured from the Indian Institute of Wheat and Barley Research (IIWBR), Karnal (India). The data on TGW, grain width, grain length, length-width ratio, and five other agronomic traits recorded on the above 207 Indian wheat genotypes were used in the present study.Each metric observation was based on an average of 10 randomly selected plants. The observations and data on different traits were recorded in the following manner: (i) 1000-grain weight (TGW); weight of 1000 grains expressed in grams; (ii) grain-length; recorded in milimeter using software SmartGrain, (iii) grain-width; recorded in milimeter using software SmartGrain (iv) grain length-width ratio; recorded using software SmartGrain; (v) awn length; measured in cm from middle one-third region of the ear; (vi) spike length; measured in cm from the base of the ear to the tip of the apical spikelet (excluding awns); (vii) grain protein content: estimated using Food and Feed Analyzer NIR 1255; (viii) peduncle length: measured in cm from base (collar) of the spike to the first node; and (ix) plant height: measured in centimetre (cm) from base of the plant to the tip of the spike (excluding awns) of the longest tiller. Data for grain length, width and length-width ratio were recorded during present study, and those for the remaining traits were procured from IIWBR, Karnal [TaGW2-6A [For each genotype, genomic DNA was extracted from the leaves of one month-old plants using a modified CTAB method . IsolateTaGW2-6A . PCR reaFor sequencing of PCR products, approximately 500 ng of each PCR products obtained above were used and cleaned using the following reaction. 1 U Shrimp Alkaline Phospatase (Fermentas) and 10 U of Exonuclease I (Fermentas) in a final volume of 10 μl at 37°C for 15 min followed by enzyme inactivation at 85°C for 15 min.One μl (~50 ng) of each of the above cleaned samples was directly used as template for sequencing. The reaction was set-up using 10 pmole primer and 0.5 μl Big-dye chemistry v3.1 (ABI) in a final volume of 10 μl. The sequence of cycles was set-up with the following profile at a ramp rate of 3.35°C/second: denaturation at 96°C for 10s, primer annealing at 50°C for 5 s and extension at 60°C for 4 min for a total of 30 cycles. Gene Amp PCR system 9700 (Applied Biosystem) was used for PCR amplification. The fluorescently labelled PCR products were analysed using an ABI 3730xl sequencer.http://www.clcbio.com). In order to identify quality SNPs, specific criteria based on the read depth, minor allele frequency and the quality of flanking regions were used. Each high quality SNP was identified in a segment of appropriate size, where all bases matched except the SNP identified, so that a 15-bp flanking region on each side of an identified SNP had no extra SNPs or indels [Sequence alignment and SNP detection were performed using software CLC genomics/DNA workbench . Significance of the association was determined by p-value (<0.05). Mann-Witney (non-parametric test) was applied to test the significance of difference for TGW between the two allele classes of each SNP locus using SPSS. Analysis of variance (ANOVA) was conducted by PROC GLM in the Statistical Analysis System  to test the significant differences of TGW among different haplotypes.Descriptive statistics for all nine traits including TGW were obtained using SPSS. Association analysis was conducted using General Linear Model (GLM) with 1000 permutations with the help of software TASSEL  was used as internal control and HI 1500 genotype (with minimum expression level) was used as a reference. For expression analysis, two biological replications for each genotype were performed and three technical replications were analyzed for each biological replication.Total RNA was extracted from immature seed (15 DAP = days after pollination) from 10 genotypes (5 genotypes with SNP-494_A and 5 genotypes with SNP-494_G) using Sigma Aldrich’s Spectrum Plant Total RNA kit. Quantitative Real-time PCR (qRT-PCR) was used to analyze the transcript level of CAACGGTA ). qRT-PCT method . TaActinTaGW2-6A gene was examined using PLACE database (http://www.dna.affrc.go.jp/htdocs/PLACE/) [For motif search, promoter region involving ~1-kb segment upstream of the /PLACE/) .TaGW2-6A was first amplified using Hap-6A-P1_For and Hap-6A-P1_Rev, followed by a second PCR (primer pair: Hap-6A-P2_For and Hap-6A-P2_Rev [FauI (New England Biolabs) using 1 unit enzyme at 55°C for one h. The fragments resulting due to digestion were separated on 2% agarose gel.Phenotyping and genotypic data were used to identify the causal SNP (at -494bp). The causal SNP was then converted into a CAPS (cleaved amplified polymorphism sequence) marker. Restriction site was identified using dCAPS Finder 2.0 program. Promoter region of A-P2_Rev ) to get TGW in 207 Indian wheat genotypes ranged from 31.1 to 48.5 g with a mean of 38.7 g. The data gave a good fit to normal distribution with a standard deviation of 3.26 and coefficient of variation (CV) of 8.41%. Descriptive statistics for the remaining eight agronomic traits are presented in TaGW2-6A in 207 Indian wheat genotypes allowed identification of four SNPs, at positions -988bp, -739bp, -593bp and -494bp . The sequence alignment revealed that out of the four SNPs, two SNPs (at -988bp and at -494bp) were novel and were reported for the first time, while the remaining two SNPs at -739bp and -593bp positions were also reported earlier by Su et al. [PCR amplification  and sequd -494bp  with minu et al. .Association mapping using general linear model (GLM) and Mann-Whitney U test revealed that out of the four SNPs identified during the present study, only one SNP (G/A at -494bp) was associated with TGW ; 13 genoUsing four SNPs, following five haplotypes could be constituted [Analysis of variance (ANOVA) showed significant difference for TGW among 5 haplotypes , and the genotypes with SNP-494_G had expression level, which was 1.5 to 7.7 fold (average = 4.45 fold) relative to expression in HI 1500, used as a reference  was observed in the cleavage products, which could be easily distinguished on agarose gels  was converted into a CAPS (cleaved amplified polymorphism sequence) marker to distinguish the alleles of ose gels .Two hundred seven (207) Indian wheat genotypes used in the present study were released over a period of ~100 (1910–2006) years and captured almost the entire genetic variation in TGW among Indian wheat genotypes. TGW in these 207 genotypes was normally distributed suggesting suitability of the mapping panel for conducting candidate gene-based association analysis. Similar candidate gene-based association mapping studies involving TGW and grain length were earlier conducted in China mainly using Chinese wheat germpalsm –35.TaGW2-6A, and detected two novel SNPs in this region. However, an insertion of a single base (T) was also earlier reported in the coding region, generating a pre-mature stop codon [TaGW2-6A, are now known . Thi studies . In futuTaGW2-6A, was detected. This SNP was available at -494bp position within the promoter; the other SNP that occurred at -593bp position was also reported earlier by Su et al. [TaGW2-6A, as also indicated by the results of expression analysis conducted during the present study. Association of an insertion in the coding region of TaGW2-6A with grain weight was also reported in an earlier study [During the present study, association of TGW with only one novel SNP that occurred in the promoter region of u et al. , but waser study , but couTaGW2-6A with TGW was reported in a Chinese wheat collection [TaGW2-6A was also involved in controlling agronomic traits other than grain size. A user-friendly CAPS marker for the causal SNP was also developed for exploitation of the variation in TaGW2-6A gene for improvement in TGW and other associated agronomic traits through marker-assisted selection (MAS) in wheat. During the present study, only five of the 16 possible haplotypes (involving four SNPs) were available. A failure to detect all the possible haplotypes may be attributed to small population size as well as strong LD. Using the above five haplotypes, we conducted haplotype-based marker-trait association analysis to study intragenic interaction. Of all the five haplotypes, Hap5 (G_A_G_A) had significantly higher TGW than other haplotypes except Hap2, which did not show any significant difference from Hap5  regulation [CGCGG” motifs in the promoter region of TaGW2-6A, one at -810 bp and other at -495bp. The presence of more than one Ca2+/calmodulin-responsive cis regulatory elements in the promoter region of TaGW2-6A favours the possibility of a Ca2+-mediated regulation of TaGW2-6A gene expression in a manner similar to that of calcium-dependent protein kinases (CDPKs). In rice, a CDPK with calmodulin like domain (SPK) has been shown to be involved in accumulation of storage products during seed development [TaGW2-6A in wheat also. Interestingly, the SNP-494 was located in one of the two “CGCG” motifs (at -495bp). Expression analysis also revealed that SNP-494 was involved in the regulation of the expression of TaGW2-6A. In view of the above, a hypothetical pathway of CGCG mediated regulation of TaGW2-6A gene is proposed  is the predominant wild type and the other motif CACG carrying the allele A evolved during the course of evolution. A perusal of TGW values of these two sets of haplotypes revealed that haplotype with CACG motif had significantly higher mean TGW than the haplotype with CGCG; this suggested that CACG motif might have evolved later due to selection for higher grain weight.Of the 207 wheat genotypes examined during the present study, as many as 194 genotypes carried CGCG motif with G allele and the remaining set of 13 genotypes had CACG motif with ‘A’ allele at the SNP-494 locus. This suggested that the motif CTaGW2-6A in wheat and its homologues in rice and maize are constitutively expressed [OsGW2 for grain size encodes a RING-type protein with E3 ubiquitin ligase that negatively regulates grain width through control of cell division in the spikelet hull. Loss-of-function mutations in the coding sequence, or interference with the expression level of OsGW2, resulted in enhanced grain width, grain weight and grain yield [TaGW2-6A concluded that like rice gene OsGW2, its wheat otrthologue TaGW2-6A is also a negative regulator of grain-width and grain-weight [TaGW2-6A. However, Bednarek et al. [TaGW2 expression resulted in a significant reduction in final grain weight and size. Following may be the possible reasons for these apparently opposite results: (1) the gene TaGW2-6A may have different genetic backgrounds in the genotypes used in different studies; (2) there may be other genes, which may be silenced during the study conducted by Bednarek et al. [TaGW2 may have different effects on grain weight, so that silencing of all the three genes might result in reduction in grain weight: this contention received support from a recent study [TaGW2-6A is negatively associated with the grain width, but the transcript levels of TaGW-2B and TaGW-2D were positively associated with the grain width in the same bread wheat accessions. This suggested that triplicate homoeologues of TaGW2 might have different functions in grain development, and that there is a balance among three genes finally determining the grain size in bread wheat.xpressed , 41–42. n-weight , 23. Thek et al.  reportedk et al. , who usent study , where iTaGW2, three other genes, namely TaGASR7-A1, TaGS-D1 and 6-SFT-A2 which control grain weight and/or length have been recently reported [GS3, GW5, GW8, TGW6, Ghd7 and GIF1) have actually been isolated and cloned in rice [TaGW2 used for the present study may prove useful for development of high yielding wheat cultivars through marker-assisted selection.Beside reported –35. Howe in rice –48 and tTaGW2-6A, which significantly affects TGW, grain length-width ratio and five other agronomic traits in wheat. This SNP was also a part of a haplotype and was located in an important motif (CGCG), which may possibly be a site for one or more calmodulin-binding transcription factors and eventually may be involved in regulation of the expression of the TaGW2-6A gene. This SNP was found to regulate the expression of the gene TaGW2-6A. The findings of the present study provide an initial step toward dissecting the molecular mechanism underlying seed development and TGW in wheat. The functional CAPS marker developed for causal SNP during the present study is recommended for use in marker-assisted selection for improvement of TGW along with other agronomic traits in wheat.A novel SNP (SNP-494) was identified in the promoter region of the gene S1 FigSNPs are highlighted with yellow  and red . SNP involving CGCG motif is represented with box.(DOCX)Click here for additional data file.S2 Fig(DOCX)Click here for additional data file.


In large scale population-based whole-exome sequencing (WES) studies, there are some samples occasionally sequenced two or more times due to a variety of reasons. To investigate how to efficiently utilize these duplicated sequencing data, we conducted comprehensive evaluation of variant calling strategies. 92 samples subjected to WES twice were selected from a large population study. These 92 duplicated samples were divided into two groups: group H consisting of the higher sequencing depth for each subject and group L consisting of the lower depth for each subject. The merged samples for each subject were put in a third group M. Using the GATK multisample toolkit, we compared variant calling accuracy among three strategies. Hierarchical clustering analysis indicated that the two replicates for each subject showed high homogeneity. The comparative analyses on the basis of heterozygous-homozygous ratio (Hete/Homo), transition-transversion ratio (Ti/Tv), and overlapping rate with the 1000 Genomes Project consistently showed that the data quality of the SNPs detected from the M group was more accurate than that of SNPs detected from the H and L groups. These results suggested that merging homogeneous duplicated exomes instead of using one of them could improve variant calling accuracy. Next generation sequencing technologies generate huge amount of data in a single experimental run and provide a revolutionary tool for various genomics studies . PrimariAmong currently available exome enrichment platforms, some platforms such as Illumina TruSeq were also designed to capture a portion of noncoding regions, including untranslated regions (UTRs) and intronic regions . In addiIn large WES studies, some samples are occasionally sequenced twice or even more times due to a variety of reasons, for example, insufficient coverage in the first experiment, sample duplication, and the rest. It is challenging how to best utilize these duplicated exomes for SNP discovery and genotype calling, especially with batch effects taken into consideration. In the present study, we systematically evaluated SNP detection performance of three strategies to utilize the duplicated exome data of 92 subjects, only using the data with higher depth, only using the data with lower depth, and using the merged data from technical replicates.The subjects in this study were a subset of participants in the Shanghai Breast Cancer Study (SBCS), which was a population-based breast cancer case control study. Details of the study have been described elsewhere , 20. In For each of 92 subjects, one sequencing library was constructed for genomic DNA captured on the Illumina TruSeq platform according to the manufacturer's instructions and subjected to 75 or 100 bp paired end sequencing twice on the Illumina HiSeq instrument. For each exome, raw reads in FASTQ format were aligned to the human reference genome (hg19) using Burrows-Wheeler Aligner  in default parameters . Mapped The metric of RPKM (reads per kilobase and million mapped reads) is a commonly used method for RNA-sequencing data normalization. Recently, the RPKM was also used to evaluate the performance of exome sequencing . In thisWe compared SNP detection performance for each of three SNP calling strategies from the following aspects: (1) the total number of SNPs observed; (2) the ratio of heterozygous genotypes to nonreference homozygous genotypes (Hete/Homo), which is expected to be close to 1.5 on genomic scale , 27; (3)http://dx.doi.org/10.1155/2014/319534). On average, 98.23% and 98.65% of the reads were aligned to the human reference genome, and 49.70% and 49.11% were mapped to the target regions, in the H and L groups, respectively. Approximately 86.16% and 86.14% of the reads in the H and L groups had mapping quality ≥ 20, respectively. Overall, the comparative results showed the high homogeneity between the duplicates for each subject (Supplementary Table  1). Furthermore, the hierarchical clustering result based on PRKM values also showed that the two replicates for each subject were closely clustered with each other , 1.39 ± 0.21, and 1.22 ± 0.19 for the M, H, and L groups, respectively . For SNPThe Hete/Homo ratio was another parameter to evaluate the genotype quality with the expected value of 1.5 on a genomic scale , 27. AmoP < 0.001) in the M group  is markedly superior to the other two approaches, especially for identifying variants located in the off-target regions.The Ti/Tv ratio is a critical metric for assessing the specificity of SNP calling . The empAlthough the aim of WES is to identify SNPs located in the coding regions, not all coding variants could be equally discovered. There are diverse reasons, including high or low GC contents in the target regions, uneven DNA capture, and sequencing depth –32. BesiMerging two or more replicated sequencing data could improve SNP calling accuracy, as this strategy is to a large extent similar to the addition of sequencing depth. However, there is a prerequisite. It needs to assess the heterogeneity between replicates within each subject before merging them. Multiple measurements, including on-target region mapping rate, low quality read rate, the fraction of four bases, and genome coverage, could be used for evaluation. We also propose that the hierarchical clustering based on the RPKM statistic is a robust evaluation for quality control in the WES performance  as the RIn summary, our investigation indicates that merging the low heterogeneous duplicated WES data within each subject into a single sample and then conducting SNP calling are a reasonable strategy to discover variants from the next generation sequencing technology.Table S1: Data production by 92 duplicated WES subjects.Table S2: Number of variants observed across the on-target and off-target regions.Figure S1: Alternative allele frequency distribution of SNPs uniquely called in H and M.


QTL mapping through genome-wide association studies (GWAS) is challenging, especially in the case of low heritability complex traits and when few animals possess genotypic and phenotypic information. When most of the phenotypic information is from non-genotyped animals, GWAS can be performed using the weighted single-step GBLUP (WssGBLUP) method, which permits to combine all available information, even that of non-genotyped animals. However, it is not clear to what extent phenotypic information from non-genotyped animals increases the power of QTL detection, and whether factors such as the extent of linkage disequilibrium (LD) in the population and weighting SNPs in WssGBLUP affect the importance of using information from non-genotyped animals in GWAS. These questions were investigated in this study using real and simulated data.Analysis of real data showed that the use of phenotypes of non-genotyped animals affected SNP effect estimates and, consequently, QTL mapping. Despite some coincidence, the most important genomic regions identified by the analyses, either using or ignoring phenotypes of non-genotyped animals, were not the same. The simulation results indicated that the inclusion of all available phenotypic information, even that of non-genotyped animals, tends to improve QTL detection for low heritability complex traits. For populations with low levels of LD, this trend of improvement was less pronounced. Stronger shrinkage on SNPs explaining lower variance was not necessarily associated with better QTL mapping.The use of phenotypic information from non-genotyped animals in GWAS may improve the ability to detect QTL for low heritability complex traits, especially in populations in which the level of LD is high. Despite advances in genome-wide association study (GWAS) approaches, QTL detection using real data is still challenging, especially in unfavorable scenarios such as those posed by low heritability complex traits and by the availability of relatively few genotyped animals.Bayesian multiple-regression models are the preferred methods for GWAS . In addiThe single-step GBLUP method (ssGBLUP) , 5, origThe aims of the present study were: 1) to determine whether additional phenotypic information from non-genotyped animals affects QTL mapping of real data, and 2) to evaluate, by simulation, whether this additional phenotypic information enhances QTL mapping for a low heritability complex trait and whether factors such as the extent of linkage disequilibrium (LD) in the population and weighting SNPs in WssGBLUP affect the importance of using information from non-genotyped animals in GWAS.The set of real data contained the phenotypic information of age at first calving (AFC) of Nelore heifers controlled by the DeltaGen® breeding program. This dataset was chosen to represent the scenario of a low heritability complex trait, with few available genotyped animals and a relatively large number of phenotypes.The dataset contained records from animals raised on tropical pasture in different commercial herds located in the southeastern, western and central regions of Brazil. During the breeding season, usually the rainy period, the heifers are artificially inseminated or naturally mated. In general, the first mating of heifers occurs at about 26 months of age, although some herds expose heifers earlier at an age of about 14–18 months. Two scenarios were established to perform GWAS of AFC. The first scenario included all heifers that had the opportunity to breed at an early age and with observed AFC (SI), totaling 43,482 females, and the second considered only heifers with observed AFC and available genotype (SII), totaling 1,813 females. The pedigree file used in the two scenarios contained 237,602 animals; of these, 100,697 had known parents.A total of 1,829 Nelore females were genotyped using the Illumina Bovine HD panel , which contains 777,962 single nucleotide polymorphism (SNP) markers. These females were born between 2007 and 2009 and were chosen among contemporary heifers exposed at an early age (14–18 months), which belonged to two main farms of the commercial breeding program. Heifers that did not conceive in the anticipated breeding season were also exposed in the regular breeding season, at about 26 months of age. Heifers that did not conceive in either season were excluded from the study. The minimum, maximum and average values of observed AFC of the genotyped females were 698, 1,200 and 1,050 days, respectively, and the proportion of heifers considered precocious (AFC < 900 days) was 28.2 %.−5 . Fifty-four SNPs presenting duplicated map coordinates with other SNPs were also excluded from the dataset. To avoid redundancy of genotypic information, SNPs that were highly correlated (r2 > 0.995) with other SNPs within a sliding window containing 100 consecutive markers were removed from the dataset. Only one marker of each pair of highly correlated SNPs was discarded . Samples with a call rate < 0.9 for SNPs passing QC were excluded (16). The remaining numbers of SNPs and samples after QC were 333,878 and 1,813, respectively.Quality control (QC) of the genotypes was performed discarding information of SNPs mapped in non-autosomal regions  and SNPs with a GC score < 0.7, a call rate < 0.95 , a minor allele frequency (MAF) < 0.02 , and a p-value of the Hardy-Weinberg equilibrium test < 10The number of phenotypes and genotypes available in the simulation study mimics the information available in the real dataset. The phenotypes and genotypes were simulated using the QMSim v.1.10 software . Ten repTwo different populations, which differed in the number of historical generations, were simulated to produce different levels of LD. In both cases, a historical population was generated from generation zero to 1,000, with a constant size of 1,000 animals. From generation 1,001, a gradual reduction in the number of animals  was simulated, producing a bottleneck effect and, consequently, genetic drift and LD. This gradual reduction occurred over 1,020 or 2,020 generations, resulting in a population with a lower (LLD) or higher (HLD) level of LD to mimic levels of LD in taurine  and indiIt was assumed that QTLs explain 100 % of genetic variance. The simulated genome had a total length of 2,333 cM, 735,293 markers, and 7,000 QTLs. The number of markers and QTLs per chromosome ranged from 46,495 to 12,931 and from 121 to 438, respectively, and were randomly distributed over 29 autosomes. All markers were bi-allelic, mimicking SNPs found in commercial bovine panels. For the QTLs, the number of alleles per loci ranged (randomly) from 2 to 4.−4 for markers and QTLs in the historical population was considered. The number of mutations was sampled from a Poisson distribution with mean u (u = 2*number of loci*mutation rate) and each mutation was assigned to a random locus in the genome. A total of 335,000 markers (MAF ≥ 0.02) and 1,000 segregating QTLs were randomly selected from the last generation of the historical population to generate genotypic data for the selection population. The average distance between adjacent markers was 0.007 cM. Although the genetic architecture of reproductive traits is unknown, the simulation parameters used in this study aimed to mimic a polygenic complex trait affected by many genes of small effects and by few genes with more pronounced effects. The phenotypes of the animals comprised the sum of QTL effects plus an error term sampled from a normal distribution with zero mean and variance of 0.86, resulting in a trait with a heritability of 0.14.QTL allele effects were sampled from a gamma distribution with a shape parameter of 0.4  and a sc2 measure − 1âg, where D is a diagonal matrix with weights for SNPs; P is a matrix relating the genotypes of each locus, and âg is the vector of predicted breeding values of genotyped animals. Matrix D, direct additive genetic effects and SNP effects were iteratively recomputed according to the method referred to as “ssGBLUP/S2” by Wang et al. [D (di) were assumed to be 1 . For the subsequent iterations, di was calculated as: di = ûi2pi(1- pi), where ûi is the allele substitution effect of the ith marker estimated from the previous iteration, and pi is the allele frequency of the second allele of the ith marker. Before the beginning of a new iteration, matrix D was normalized to ensure that the total genetic variance was constant across iterations. Three iterations  were performed for each scenario, resulting in an increasing shrinkage from w1 to w3 for the SNPs explaining lower variance and, consequently, in an increasing proportion of variance being explained by the remaining markers. According to Wang et al. [For both scenarios (SI and SII) and datasets , the solutions of SNP effects  sampled from a binomial distribution with parameters n and π, where n is the number of SNPs and π is the fraction of SNPs not included in the model. For the real dataset, a prior beta distribution with parameters α = 108 and β = 1010 was assumed for π so that, in practice, π was almost fixed to 0.99 [π assumed two possible values, almost fixed to 0.99 as used for the real data, or almost fixed to 0.999 assuming a prior beta distribution with parameters α = 108 and β = 1011. This strategy, rather than letting π be estimated from the data (Bayes Cπ), was adopted because it was found to provide better results in QTL detection [σ2g) and for the residual variance (σ2e).Bayes C analysis was based on the following model : \docume to 0.99 . For theentclass1pt{minimay in Bayes C contained the pre-adjusted phenotypes of AFC. Bayes C analysis was performed using the Markov chain Monte Carlo algorithm implemented in the GS3 software [The SNP genotypes were coded as the number of copies of one of the SNP alleles, i.e., 0, 1, or 2. For Bayes C, only scenario SII was evaluated because the adopted implementation did not allow the inclusion of phenotypes from non-genotyped animals. As in scenario SII of WssGBLUP, vector software , runningFor the real data, the GWAS results were compared based on SNP effect estimates and on the proportions of variance explained by SNPs within consecutive 1-Mb windows. A total of 2,525 windows spanning all autosomes were considered, with an average density of 132 ± 44 SNPs per window. The top 10 windows that captured the highest proportion of variance explained by the markers were identified for the different scenarios and methods. The QTLdb database  was consstQTL) and a top marker window (Pvar_1stMRKw), and the estimated number of true QTLs (NtrueQTL), i.e., the number of topQTL identified by a topMRKw located no more than 1 Mb from a true QTL position.For the simulated data, the following statistics were calculated: number of QTLs explaining 1 % or more of the genetic variance (topQTL); number of top 1-Mb marker windows accounting for the highest proportion of variance explained by the markers (topMRKw) - this number was set equal to topQTL so that the different methods could be compared on the same basis; sum of the percentages of genetic variances explained by all topQTL (Pvar_topQTL) and top marker windows (Pvar_topMRKw); highest percentage of genetic variance explained by a topQTL  of 16.7 in the HLD population (Table a priori (π = 0.99 to π = 0.999) resulted in higher variance that could be explained by the top marker windows. The method providing the highest proportion of variance explained by the top marker windows was Bayes C (π = 0.999)  and a higher proportion of SNPs assumed unimportant  to w3 anIn general, irrespective of the method or scenario, GWAS exhibited poor ability to map the topQTL  more precisely through relatedness. Since the SNP effects estimated with the WssGBLUP method are computed as a function of âg, better predictions of âg would ultimately result in better estimation of SNP effects.The use of phenotypic information from non-genotyped animals tended to increase the precision of QTL detection, especially in the HLD population. The analysis with the best result considering additional phenotypic information was able to detect on average 17.4 % (2.9 out of 16.7) of the topQTL, whereas WssGBLUP analysis ignoring this information detected a maximum of 12.0 % (2.0 out of 16.7) of the topQTL  in the LLD population was 15.4. Taken together, the topQTL explained 24.32 % of the genetic variance, with the most important QTL explaining on average 3.19 %. Compared to the HLD population, all methods and scenarios involving the LLD population exhibited a poorer ability to detect true QTLs. In addition, the pattern of the Bayes C results obtained for the LLD population differed from that of the HLD population. The average NtrueQTL of the analyses assuming π = 0.999 was slightly higher than the value obtained with the analyses using π = 0.99  on SNPs explaining lower variance redistributed the variance and resulted in larger variances that are explained by the (supposedly) most important regions (Table  on SNPs Although the most important genomic regions identified by the different analyses were not the same, some coincidence was observed. Considering all seven analyses, 31 different windows were indicated as top 10 (Table Although the simulation results indicated a poor ability to detect QTLs, some regions identified as important in the present study using real data coincided with regions reported by different authors –28 for dOther studies have also identified important genes. Waters et al. , studyinAdditional phenotypic information from non-genotyped animals influences the results of GWAS. Despite some coincidence, the most important genomic regions for AFC in Nelore cattle, identified by analysis using or ignoring phenotypes from non-genotyped animals, were not the same. The simulation results indicated that the inclusion of all available phenotypic information, even that of non-genotyped animals, provides small improvement in the detection of QTLs in GWAS of low heritability complex traits. This improvement is expected to be less pronounced in populations in which the level of LD is low.


Argentina has a long tradition of sunflower breeding, and its germplasm is a valuable genetic resource worldwide. However, knowledge of the genetic constitution and variability levels of the Argentinean germplasm is still scarce, rendering the global map of cultivated sunflower diversity incomplete. In this study, 42 microsatellite loci and 384 single nucleotide polymorphisms (SNPs) were used to characterize the first association mapping population used for quantitative trait loci mapping in sunflower, along with a selection of allied open-pollinated and composite populations from the germplasm bank of the National Institute of Agricultural Technology of Argentina. The ability of different kinds of markers to assess genetic diversity and population structure was also evaluated.i.e., SSR, SNP and SSR + SNP), with the maintainer/restorer status being the most prevalent characteristic associated with group delimitation.The analysis of polymorphism in the set of sunflower accessions studied here showed that both the microsatellites and SNP markers were informative for germplasm characterization, although to different extents. In general, the estimates of genetic variability were moderate. The average genetic diversity, as quantified by the expected heterozygosity, was 0.52 for SSR loci and 0.29 for SNPs. Within SSR markers, those derived from non-coding regions were able to capture higher levels of diversity than EST-SSR. A significant correlation was found between SSR and SNP- based genetic distances among accessions. Bayesian and multivariate methods were used to infer population structure. Evidence for the existence of three different genetic groups was found consistently across data sets  contains supplementary material, which is available to authorized users. Helianthus annuus L. var. macrocarpus) is one of the most important oilseed crops, with a cultivated area of 25 million hectares worldwide (www.sunflowernsa.com). Its annual production ascends to 36 million metric tons and it is mainly concentrated in the Russian Federation, Ukraine, European Union, and Argentina, which is the fourth largest producer and the third oil exporter [Cultivated sunflower  is a relatively recent quantitative trait loci (QTL) mapping approach, that has the potential for resolution to the level of individual genes  . In contThe genetic diversity and population structure of North American and European resources has been exhaustively assessed by Coque et al.  and MandIn AM studies, population structure is commonly estimated by using SSR derived information, because of the proven usefulness of this type of markers for population genetics inferences and their higher information content when compared to biallelic markers ,23-28. NHere we present the genetic characterization of the 137 inbred lines that currently compose the INTA association mapping population (AMP-IL), and of a set of allied open-pollinated (OP) and composite populations (CP). The aims of this study were: (a) to assess the levels of molecular diversity and population structure using gSSR, EST-SSR and SNP; and (b) to compare the performance and the estimates produced by the different types of markers.−27, the probability of identity among siblings (PIsibs ) was 3.3 × 10−12, and the average Polymorphism Information Content (PIC) was 0.50. In the whole collection, the total number of alleles was 208, and ranged from 2 to 14 per locus, with an average of 4.95. The expected heterozygosity (He) across the total 646 sampled plants was 0.51 ± 0.16. Of the 208 alleles present in the sunflower accessions, 10 were private, or unique to the AMP-IL. In contrast, 36 private alleles were detected for the OP + CP group. The AMP-IL and OP + CP collection had 162 alleles in common. Within the AMP-IL, 25 alleles were unique to the maintainer (HA) lines, while 16 were private to the restorer (RHA) lines; and 36 alleles were private to the Argentinean germplasm when compared to the “Other origin” germplasm.A total of 170 sunflower accessions, corresponding to the AMP-IL (137 accessions), and a set of CP and OP (33 accessions) were analyzed using 42 SSR markers. Missing data accounted for 4.57% of the data matrix. For the full panel of accessions, the probability of identity (PI) was 3.5 × 10Variability indices obtained from EST-SSR were always lower than those derived from genomic SSR (gSSR). Genetic diversity statistics for each of the SSR and cultivar categories used in this study are presented in Table −46 and the PIsibs was 3.3 × 10−24. The estimated PIC was 0.232. Inspection of the distribution of SNP allele frequencies showed a pattern different from that observed for SSR, with a larger proportion of alleles at intermediate frequencies , showed more than 10% missing values or had ambiguous SNP calling. The resulting data set was composed of 182 high quality informative SNPs. The average proportion of missing data was 0.91%. The PI was 1.0 × 10Minor allele frequencies (MAF) were larger than 0.1 for 91.76% of the 182 polymorphic SNP loci. Overall, the expected heterozygosis (He = 0.29) was lower than the values observed for SSR markers. As expected for inbred lines, the observed heterozygosis was very low for both the SSR and the SNP data sets . Diversity indices obtained from SNP markers are summarized in Table Analysis of molecular variance (AMOVA) was conducted to test putative differences among the groups defined in the present work (the AMP-IL vs. the OP + CP) and between the subgroups in which the AMP-IL was further subdivided . Significant differences were detected between AMP-IL and OP + CP . Within the AMP-IL, the analyses were done using the three marker-sets available: SSR, SNP and SSR + SNP. In all three cases, the AMOVA revealed significant differentiation among the groups delimited within the AMP-IL; however they only explained 2-3% of the total variance, with the remaining variation resting among individuals within groups and within individuals .Population structure estimation for the whole panel of accessions, including the AMP-IL, the OP and the CP, was done using the Bayesian clustering approach implemented in STRUCTURE. Given that the log likelihood values increased progressively as K increased, the method of Evanno et al.  was applTo test the performance of the different marker sets  for predicting population STRUCTURE, the AMP-IL was subjected to further analysis. The method of Evanno et al.  detectedInspection of the DAPC plot also revealed the presence of genetic structure within the AMP-IL. In agreement with the STRUCTURE analysis, the sequential k-means algorithm identified 3 groups regardless of the data set under study Figure .Figure 3To test the consistency of individual assignments across marker sets, we computed Spearman correlation coefficients between STRUCTURE membership coefficients. Correlations between SSR and SNP outputs were significant for all three groups . Significant correlations were also found when comparing SSR vs SSR + SNP  and SNP vs SSR + SNP .To assess the correspondence among the groupings retrieved under Bayesian and multivariate approaches, we computed the percentage of individuals assigned to STRUCTURE groups that were assigned to the same group using DAPC. As shown in Table One interesting aspect of the DAPC method is that it allows the identification of those alleles that are most relevant to group delimitation. To get some insight into the underlying causes of the differentiation among the groups detected within the AMP-IL, we inspected the associated allele loadings for the SNP dataset, since it was the only set for which functional annotation was available. A total of 13 SNP were identified as the most contributing: SNP 30, 34, 44, 69, 72, 105, 116, 168, 178, 192, 193  and SNP 139 and 147 . The loading plots for each type of marker are presented in Figure Distance matrices based on allele sharing were constructed for all pairs of individuals using either SSR or SNP data. For the SSR data set, distances varied from 0.012 to 0.78, with an average of 0.47. For the SNP dataset distances ranged from 0.003 and 0.45, with an average of 0.28. The Neighbor-joining trees depicting the relationships among inbred lines are provided in Additional file Argentina has a long tradition of sunflower breeding, and its germplasm is a valuable genetic resource worldwide, with several international differential lines being derived from Argentinean varieties ,31.The inbred lines included in the present work were part of the first association mapping study reported for sunflower  and are In most studies, population structure and genetic diversity are commonly estimated using SSR derived information. Genomic SSR (gSSR) are attractive markers for population diversity studies because of their abundance, reproducibility and high levels of polymorphism. Recently, there was an increase in the use of EST-derived SSR, as they can be easily obtained by electronic search of EST databases. These kinds of markers that belong to the coding regions of DNA are expected to be more conserved than gSSR. Nevertheless, only few studies documented the difference in information content and other estimates of genetic variation -35. Compin silico searches from EST databases [n silico approaches .sibs were estimated. Within the AMP-IL, the PI for the 42 SSR loci was 3.5× 10−27, and PIsibs = 3.3 10−12. For the 182 polymorphic SNPs, the PI was 1.0 10−46, and PIsibs = 3.3 10−24, suggesting that both panels of markers have a high discriminant capacity for sunflower germplasm collections, with the SNP data set being the most informative. Yu et al. [The analysis of polymorphism in the set of sunflower accessions tested here showed that both the microsatellites and SNP markers were informative, although to different extents. To test the discriminant capacity of the panel of markers, PI and PIu et al.  suggesteu et al.  proposedu et al. ,40, the According to theoretical expectations, the distribution of allele frequencies differed between SNPs and SSR markers. There was a higher presence of SSR alleles at low frequencies, whereas SNPs showed more alleles at intermediate frequencies. These spectra of allele frequencies are consistent with previous studies ,27,41, sDifferent mutational processes govern allelic variation at SSR and SNP loci, with mutation rates of SNP being several orders of magnitude lower than those of SSR. As a consequence, SNPs are typically biallelic, whereas SSR generally have high allelic richness and heterozygosity levels . The gapContradictory results have been reported by different studies regarding the correlation of genetic distances estimated with SSR and SNP markers. Jones et al.  and HambIn general, the estimates of genetic diversity obtained here for the AMP-IL are moderate and slightly lower than those detected in the 271 NCRPIS and INRA lines that compose the association mapping population used by Mandel et al. ,19. It sAnalysis of diversity levels in the full sunflower panel and the AMP-IL showed that the latter did not comprise all the alleles that are present in the OP and CP. This suggests that new inbred lines could be included in our AM panel to fully capture the allelic diversity preserved at the AGB-IM. This reduction or apparent loss of genetic diversity is a common consequence of the sampling strategy, where the alleles in lower frequency are less likely to be captured. Similarly, the AM panel used by Mandel et al.  did not e.g., HA, RHA, Oil, Non-Oil).Differences in the number of alleles and the number of private alleles were detected among the categories in which the AMP-IL was subdivided, but interestingly, there were no detectable differences in terms of expected heterozygosity. Several studies have evaluated the levels and distribution of genetic diversity in different sunflower accessions ,12,46. Ie.g., Russia, Israel, Europe, USA).The occurrence of population genetic structure was evaluated via analysis of molecular variance (AMOVA). Although moderate, significant structuring of genetic variation was found between the AMP-IL and the group composed of OP + CP and also between the different classes in which the AMP-IL was subdivided, confirming the previously suggested differences between Argentinean germplasm and that from other origins . Under the Bayesian approach, the results were almost identical regardless of the data set. However, we observed that far more individuals were classified as mixed when using the SNP data than when using either the SSR or SSR + SNP data sets. Moreover, as K increased, the percentage of assignment declined for the SNP data set. Differences in assignment percentages between SSR and SNP markers were also reported by several studies [Evidence of genetic substructure within the AMP-IL was found consistently for all data sources , 13 open-pollinated (OP) and 20 composite (CP) populations from the Active Germplasm bank of INTA Manfredi (AGB-IM) were included in this study. ID, Pedigree information, and origin are summarized in  and following manufacturer’s instructions. The quality and the concentration of the genomic DNA were assessed using electrophoretic analysis and Picogreen® technology . Genomic DNA was normalized to 25 ng/μL before genotyping.All DNA samples were genotyped using 22 genomic SSR (gSSR) selected from Paniego et al.  and 20 EFor further genetic characterization, the 137 AMP-IL were also examined using a custom-designed 384 SNP Illumina Oligo Pool Assay (OPA) ,55.SSR genotyping was performed using multiplexed PCR with fluorescent labeled primers . Fragment analysis was carried out with GeneMapper® 4.0 software  using a commercial size standard for allele size assignment . Automatic allele calls were subsequently confirmed manually reviewing all electropherograms. Genotyping of the SNPs was performed on the Illumina GoldenGate, BeadXpress  at the Biotechnology Institute  with the protocol provided by Illumina . Data weMeasures of genetic diversity, including total number of alleles, mean number of alleles per locus (A), unbiased expected heterozigosity (He) , observeThe probability of identity (PI), the PI considering genetic similarity among siblings (PIsibs) and the minor allele frequency (MAF) were calculated using GenAlEx .Population structure was investigated via analysis of molecular variance , using The model-based approach implemented in the software package STRUCTURE  was usedGenetic relationships among the AMP-IL were also examined by applying the discriminant analysis of principal components  on the Measures of genetic distance, estimated from the proportion of shared alleles, were obtained for the SSR and SNP datasets, respectively. Correlations between distance matrices were assessed using the Mantel test as implemented in GenAlEx .In addition, a neighbor-joining tree was constructed based on the genetic distances calculated between pairs of accessions. Cluster analyses and bootstrap resampling (1000 pseudo replicates) were performed using PowerMarker 3.25 . Branch The source of the SNPs used for the analyses presented here is given in Additional file