Advances in genome sequencing technologies have enabled researchers and breeders to rapidly associate phenotypic variation to genome sequence differences. We recently took advantage of next-generation sequencing technology to develop MutMap, a method that allows rapid identification of causal nucleotide changes of rice mutants by whole genome resequencing of pooled DNA of mutant F2 progeny derived from crosses made between candidate mutants and the parental line. Here we describe MutMap+, a versatile extension of MutMap, that identifies causal mutations by comparing SNP frequencies of bulked DNA of mutant and wild-type progeny of M3 generation derived from selfing of an M2 heterozygous individual. Notably, MutMap+ does not necessitate artificial crossing between mutants and the wild-type parental line. This method is therefore suitable for identifying mutations that cause early development lethality, sterility, or generally hamper crossing. Furthermore, MutMap+ is potentially useful for gene isolation in crops that are recalcitrant to artificial crosses. Recent developments in next-generation sequencing (NGS) technologies are revolutionizing various aspects of genomics. In particular, NGS-based whole genome analysis of organisms is becoming routine. Draft genome sequences of a large number of organisms including non-model species are rapidly accumulating thanks to the tremendous advances made in de novo sequencing techniques and the development of highly efficient assembly software Oryza sativa ssp. japonica) cultivar “Hitomebore” Whole genome sequencing technology promises to dramatically impact crop improvement in this era of looming food crisis and an ever-increasing world population. To exploit genome sequencing in crop breeding, we recently developed a method called MutMap and applied it to the identification of rice genes responsible for agronomically important traits MutMap is based on the crossing of a mutant of interest to the parental line that was used for the mutagenesis, followed by selfing of F1 individuals to generate F2 progeny. If the phenotype is caused by a single recessive mutation, the F2 population is expected to segregate 3∶1 for the wild-type and mutant progeny. DNA from about 20 F2 individuals showing the mutant phenotype is pooled in an equal ratio and subjected to Illumina whole genome sequencing with depth of more than 10x coverage. The short reads are then aligned to the reference genome sequence constructed for the cultivar used for the mutagenesis, to Hitomebore reference sequence in our case. Since the causal SNP is shared by F2 mutant progeny, all the re-sequenced short reads covering such SNP should have a nucleotide different from the reference sequence. In contrast, SNPs that are not relevant to the phenotype under consideration should segregate in 1∶1 ratio among the F2 progeny. Consequently, about half of the short reads covering such positions contain a nucleotide that is different from the reference genome. To quantify the proportion of short reads having nucleotides different from the reference sequence (SNPs), we developed the concept of SNP-index. If the entire short reads covering a particular genomic position share a SNP that differs from the reference, the SNP-index is defined as 1, whereas if only half of the short reads share such an SNP, then the SNP-index is 0.5. The SNP-index is calculated for all the SNPs incorporated by mutagenesis, and the relationship between SNP-index and genomic position is graphically plotted. The genomic region showing a unique SNP-index peak (SNP-index = 1) corresponds to the position of the causal mutation responsible for phenotype of our candidate mutant.e.g. mutants with early development lethality or sterility, are not suitable for MutMap application. To address this problem, we developed MutMap+, a versatile extension of MutMap that is based on selfing of heterozygous plants showing wild-type phenotype and identified in M2 progeny segregating for wild-type and a mutant phenotype of interest that is recessive homozygous. The resulting M3 population segregates for the target mutation and can be directly used for whole-genome sequencing. Since MutMap+ circumvents the necessity of backcrossing to the wild-type parent, it is suitable for the isolation of mutations in genes that hamper artificial crossing. Thus MutMap+ expands the applications of MutMap for the genetic improvement of rice and other crop plants.The MutMap method is based on selecting mutants of interest at M3–M5 generations and crossing them to the parental line, followed by evaluation of phenotypes in segregating F2 progeny. Accordingly, mutants that are not amenable for crossing, The principle of MutMap+ is explained in vs SNP genomic position graphs . Thus, the mutant could not be crossed to the parental line Hitomebore to apply the classical MutMap analysis. As a result, wild-type M2 siblings of the mutant were grown to maturity and allowed to self-fertilize. We obtained over 100 seeds/plant, which were sown separately to generate M3 progeny that were further assessed for phenotypic segregation. One M3 progeny segregated 167 (wild-type) to 56 (mutant-type), conforming to the 3∶1 ratio . This indicated that the mutant phenotype is caused by a single recessive mutation. We then made two bulks of DNA; a mutant bulk of 40 mutant-type progeny and a wild-type bulk of 40 wild-type progeny. The mutant bulk and the wild-type bulk were separately sequenced to generate Illumina GAIIx 75bp paired end reads. We obtained 95,552,424 and 57,925,150 short reads corresponding to 7.17 Gbp and 4.34 Gbp sequence reads for the mutant and wild-type bulks, respectively (Table S1).As a proof of concept, we applied MutMap+ to a rice mutant Hit9188 derived from EMS mutagenesis of cultivar Hitomebore. This mutant, identified among ten M2 plants that originated from a self-fertilized M1 individual planted in the paddy field, is characterized by dwarfism and pale green leaves followed by premature death 3 weeks after germination . However, we identified a single unique region on chromosome 1 (between 0 and 5.23 Mb) with a peak of SNP-index close to 1 in the mutant bulk that is missing from the wild-type bulk , and this could be readily visualized using a Δ(SNP-index) graph . As expected, Δ(SNP-index) was close to 0 across the genome, but within the unique genomic region identified on chromosome 1,0–5.23 Mb, its value was greater than zero. This was the only region that exhibited a SNP-index difference of >0 that is statistically significant between the mutant and wild-type bulks (Fisher’s exact test: P<0.05).The Illumina short reads obtained for the two DNA bulks (DDBJ Accession Read Archive: DRA001007) were separately aligned to the reference sequence of Hitomebore (DDBJ Accession Read Archive: DRA000927) and the SNP-index was calculated for each SNP Table S2). Of these, only two SNPs (nucleotide 1234738 and 1503571) were nonsynonymous causing amino acid changes (Table S2). The first mutation (nucleotide 1234738), a C to T transition, causes amino acid change from proline to leucine in Os01g0121800, a gene encoding a glycosyl transferase family 14 protein (Table S2). The second SNP causes amino acid changes in a 482 amino acid protein encoded by Os01g0127300 . Accordingly, we found a total of 8 SNPs with SNP index = 1 in the region for the mutant bulk  gene encoding a SufD protein OsNAP6 and OsNAP6, respectively. To determine whether the mutation detected in OsNAP6 is responsible for the Hit9188 mutant phenotypes, we used RNA interference to knockdown the expression of OsNAP6. Accordingly, we cloned a 300-bp sequence spanning the last exon and 3′-UTR of OsNAP6 into pANDA vector Agrobacterium tumefaciens. The rice transgenic lines (RNAi-OsNAP6) with reduced expression of OsNAP6 as determined by quantitative RT-PCR  A. thaliana nap6 T-DNA insertion mutant shows various developmental phenotypes such as reduced chlorophyll content, shorter roots, a considerable proportion of abnormal embryos, and altered thylakoids OsNAP6 shares considerable similarity with proteins from multiple plant species and the mutated alanine residue, which is located within the predicted putative β-helix domain, is conserved . For MutMap+ application, we prepared two DNA bulks; the first bulk composed of 20 M3 mutant albino progeny and the second bulk composed of 20 wild-type M3 progeny mixed in equal proportion and sequenced to a depth of 12 and 11.4 fold coverage, respectively (Table S1). SNP-indices were calculated for both bulks and plotted for all the 12 rice chromosomes. Comparison of SNP-index plots of the two bulks as well as generation and further evaluation of Δ(SNP-index) plots identified a 4.34 Mb region (0.32 Mb - 4.66 Mb) on chromosome 8 that is unique to the mutant bulk .We further applied MutMap+ to a second mutant Hit11440 that is characterized by albino seedlings that eventually die from about two weeks after germination ((Table S3). Of these, only a single SNP causing a G (Hitomebore) to A (Hit11440) transition, which corresponded to a C to T substitution due to opposite orientation of the gene, at the 2,178,176th nucleotide position was non-synonymous. The SNP introduced a premature stop codon at the 137th residue (CAG-Gln to TAG-Stop) of Os08g0139100, a gene predicted to encode a protein similar to the chloroplast precursor Differentiation And Greening (DAG) protein  and shares considerable homology with DAG proteins from multiple plant species . The mutated glutamine residue is also highly conserved among the species considered. The DAG locus was previously isolated in Antrrhinum majus by transposon tagging, and the corresponding dag mutant is characterized by a slow growth and variegated leaves DAG is reported to be essential for chloroplast development, and is required for the expression of the nuclear encoded light harvesting chlorophyll a/b-binding (CAB) and RBCS genes, as well as for the expression of the plastid gene RPBOWe assessed the SNPs within the candidate region on chromosome 8 in detail and identified 10 SNPs with SNP index of 1 We recently reported a whole genome sequencing (WGS)-based method called MutMap that accelerates the identification of genomic regions harboring a causal mutation for a given phenotype in rice. The method can generally be extended to other crop plants provided that the prerequisite of one backcross to the parental line is met. Here we describe a variant of MutMap called MutMap+, which we applied to rice mutants to rapidly identify the causal mutations by whole genome resequencing and comparison of SNP-index plots of two bulked DNA obtained from a segregating M3 progeny. Notably, these experiments did not involve crossing between the candidate mutants and wild-type plants, therefore providing new applications that were not possible with the original MutMap protocol.The fact that no crossing is required is a significant advantage of MutMap+. Genetic analysis of early stage lethality or sterility genes has remained time consuming. For instance, heterozygous sibling of a mutant of interest is usually crossed to a wild-type plant of a distantly related cultivar to obtain F1, which is selfed to generate F2. If the F2 segregates for the phenotype under investigation, one could map the mutation with DNA markers. In this traditional scheme, at least a cross and selfing followed by linkage analysis with DNA markers is needed. The recently reported method of SNP-Ratio Mapping (SRM) MutMap+ is conceptually similar to QTL-seq Setaria italica), fonio (Digitaria exilis) and tef (Eragrostis tef), it is not easy to carry out crosses due to small flower sizes and difficulty in emasculation. MutMap+ can be easily applied to these species that are important to agriculture in developing countries. In view of the projected progress in de novo sequencing technologies and the ever-decreasing sequencing costs, we believe that MutMap+ is poised to be applicable for the improvement orphan crops. The improvement of neglected crops species and varieties should contribute to world food security by enhancing locally adapted agricultural systems.The application of MutMap+ is not restricted to genes associated with lethality and sterility as described above. This technique could also be widely used to identify genes involved in all types of agronomically important traits. The prerequisite is the availability of draft genome sequences to generate accurate SNP-index plots. The fact that MutMap+ allows gene isolation without crossing has an additional important practical implication. There are many crop species for which efficient techniques of artificial crossing have not been established. For instance, in many millet species including foxtail millet  used in genetic mapping. MutMap+ offers the same advantage as the classical MutMap protocol. Because it is based on selfing, it enables precise and robust phenotyping of minor effect traits.Oryza sativa L.) mutants used in this study, Hit9188 and Hit11440, were identified in M2 mutant population generated by ethyl-methanesulfonate (EMS) treatment of immature embryo of an elite japonica cultivar Hitomebore. Details of the mutagenesis protocol are provided elsewhere The rice  was mixed in an equal ratio to make the bulk-DNA used for sequencing. The library for Illumina sequencing was constructed from five micrograms of DNA sample and sequenced for 76 cycles on an Illumina Genome Analyzer IIx as described in Abe et al. We aligned the short reads from bulked DNA to the reference genome of the cultivar Hitomebore (DDBJ Sequence Read Archive: DRA000927) using BWA (Burrows-Wheeler Aligner) software P-value in Fisher’s exact test for the SNPs located in the window. Finally we mapped the causal mutation in the window exhibiting the average P-value <0.05.Sliding widow analysis was applied with 4 Mb window size and 10 Kb increment using an R script that we developed for this purpose. In this sliding window analysis, we calculated average SNP-index and average OsNAP6 gene, a 300 bp OsNAP6 partial fragment was amplified from wild-type Hitomebore cDNA by PCR and subcloned into the binary vector pANDA For RNAi analysis targeting the Figure S1The early senescence and premature death phenotype of Hit9188. Phenotype of Hiotmebore wild-type (left) and Hit9188 (right) plants (A) 14 DAS (days after sowing), (B) 18 DAS and (C) 22 DAS. Bar = 10 cm.(PPTX)Click here for additional data file.Figure S2MutMap+ identifies the genomic region harboring the causative mutation of Hit9188 (A) SNP-index plot of mutant bulk, (B) SNP-index plot of wild-type bulk, and (C) Δ(SNP-index) plot obtained by subtraction of wild-type bulk SNP-index from mutant bulk SNP-index for all the 12 rice chromosomes. Red lines represent the sliding window average of 4 Mb interval with 10 Kb increment. Shaded areas correspond to the candidate genomic region where mutant and wild-type SNP-indices exhibit statistically significant (P < 0.05) differences (i.e. Δ(SNP-index) > 0).(PPTX)Click here for additional data file.Figure S3Alignment of NAP6 homologs from multiple plant species. Identical and similar amino acids are indicated in black and gray backgrounds, respectively. Position of the mutated alanine residue in Hit9188 is given by a red box.(PPTX)Click here for additional data file.Figure S4MutMap+ identifies the genomic region harboring the causative mutation of Hit11440. (A) SNP-index plot of mutant bulk, (B) SNP-index plot of wild-type bulk, and (C) Δ(SNP-index) plot obtained by subtraction of wild-type bulk SNP-index from mutant bulk SNP-index for all the 12 rice chromosomes. Red lines represent the sliding window average of 4 Mb interval with 10 Kb increment. Shaded areas correspond to the candidate genomic region where mutant and wild-type SNP-indices exhibit statistically significant (P < 0.05) differences (i.e. Δ(SNP-index) > 0).(PPTX)Click here for additional data file.Figure S5Alignment of DAG proteins from multiple plant species. The predicted 27-aa chloroplast residue in rice is underlined, and the highly conserved mutated glutamine (Q) residue in Hit11440 is indicated by a red box. NCBI reference numbers of the sequences used: Oryza sativa (NP_001060965.1), Brachypodium distachyon (XP_003573360.1), Hordeum vulgare (BAJ99034.1), Sorghum bicolor (XP_002445021.1), Zea mays (ACN27869.1), Antirrhinum majus (Q38732.1), Arabidopsis thaliana (AAM65001.1), Glycine max (ACU13265.1), Lotus japonicus (AFK45843.1), Medicago truncatula (XP_003593397.1), Populus trichocarpa (XP_002316698.1), Ricinus communis (XP_002518590.1), and Vitis vinifera (XP_002283211.1).(PPTX)Click here for additional data file.Table S1Summary of Illumina GAIIx whole genome sequencing results of Hit9188 and Hit11440 M3 mutant and wild-type bulks.(DOCX)Click here for additional data file.Table S2SNPs with SNP-index 1 within the candidate genomic region detected on chromosome 1 that exhibited statistically significant (Fisher’s exact test: P<0.05) differences between Hit9188 mutant and wild-type bulk sequences.(DOCX)Click here for additional data file.Table S3SNPs with SNP-index 1 within the candidate genomic region detected on chromosome 8 that exhibited statistically significant (Fisher’s exact test: P<0.05) differences between Hit11440 mutant- and wild-type bulk sequences.(DOCX)Click here for additional data file.


Ancestry informative markers (AIMs) are a type of genetic marker that is informative for tracing the ancestral ethnicity of individuals. Application of AIMs has gained substantial attention in population genetics, forensic sciences, and medical genetics. Single nucleotide polymorphisms (SNPs), the materials of AIMs, are useful for classifying individuals from distinct continental origins but cannot discriminate individuals with subtle genetic differences from closely related ancestral lineages. Proof-of-principle studies have shown that gene expression (GE) also is a heritable human variation that exhibits differential intensity distributions among ethnic groups. GE supplies ethnic information supplemental to SNPs; this motivated us to integrate SNP and GE markers to construct AIM panels with a reduced number of required markers and provide high accuracy in ancestry inference. Few studies in the literature have considered GE in this aspect, and none have integrated SNP and GE markers to aid classification of samples from closely related ethnic populations.We integrated a forward variable selection procedure into flexible discriminant analysis to identify key SNP and/or GE markers with the highest cross-validation prediction accuracy. By analyzing genome-wide SNP and/or GE markers in 210 independent samples from four ethnic groups in the HapMap II Project, we found that average testing accuracies for a majority of classification analyses were quite high, except for SNP-only analyses that were performed to discern study samples containing individuals from two close Asian populations. The average testing accuracies ranged from 0.53 to 0.79 for SNP-only analyses and increased to around 0.90 when GE markers were integrated together with SNP markers for the classification of samples from closely related Asian populations. Compared to GE-only analyses, integrative analyses of SNP and GE markers showed comparable testing accuracies and a reduced number of selected markers in AIM panels.Biomarkers Identification and Samples Subdivision) software was developed as an efficient tool for selecting key SNP and/or GE markers and then building models for sample subdivision. BIASLESS was programmed in R and R-GUI and is available online at http://www.stat.sinica.edu.tw/hsinchou/genetics/prediction/BIASLESS.htm.Integrative analysis of SNP and GE markers provides high-accuracy and/or cost-effective classification results for assigning samples from closely related or distantly related ancestral lineages to their original ancestral populations. User-friendly BIASLESS ( Ancestry informative markers (AIMs) are the genetic markers carrying ancestral information for classifying samples from a specific population or various ethnic populations -12. AIMsShort tandem repeat polymorphisms (STRPs) and single nucleotide polymorphisms (SNPs) are the most frequently used genetic markers for AIMs, and each has its own strengths ,9,18. GeAlthough a small to moderate number of SNPs or STRPs could provide promising discriminative power to distinguish a large ethnic discrepancy , it becomes very challenging to classify samples from closely related ancestral lineage  using only a small number of SNPs. In an example of a previous classification study , the HapGene expression (GE) microarray technology has advanced in the past 20 years. Previous studies have shown that GE also is polymorphic and heritable variation in humans ,23. ImpoA flowchart is provided to summarize the materials and analysis flow in this study Figure .http://hapmap.ncbi.nlm.nih.gov/). In addition, GE levels of the 210 HapMap samples were measured using Illumina’s Sentrix Human-6 Expression BeadChip . Each bead chip provided 47,289 transcript probes for the human genome [http://www.ncbi.nlm.nih.gov/geo/) (Series accession number GSE6536). Annotation of SNP data from the Affymetrix 500 K and Array 6.0 was derived from the NetAffx annotation update 30 (version: dbSNP Build 128), which is available on the Affymetrix website (http://www.affymetrix.com/). Annotation of GE probes was derived from the GEO annotation , which is available in the GEO database.In this study, we analyzed SNP and GE data in 210 independent samples from the International HapMap II Project -32. The n genome ,36. Procn genome . The norThis study classified samples in each of eight combinations of the four ethnic populations: 1) “four populations” – CHB, JPT, CEU, and YRI; (2) “three populations” – Asian (CHB + JPT), Caucasian (CEU), and African (YRI); (3) “CHB and JPT”; (4) “CHB and YRI”; (5) “CHB and CEU”; (6) “JPT and YRI”; (7) “JPT and CEU”; and (8) “YRI and CEU”. Quality control of SNP and GE markers was performed together with analysis for each of the eight combinations of ethnic populations. A poor-quality SNP was removed if its genotype call rate was lower than 0.9, its minor allele frequency was 0, or if Hardy-Weinberg equilibrium (HWE) was violated, where departure from HWE was defined as a p-value that was adjusted by a false discovery rate procedure  and that “four pohttp://www.stat.sinica.edu.tw/hsinchou/genetics/aloha/ALOHA.htm). Afterward, intensive supervised classification analyses were performed to identify key SNP and/or GE markers to study subdivisions of samples from the HapMap II populations. A five-step discriminant analysis was developed to identify key SNP and/or GE markers with the highest prediction accuracy for the separation of samples from different populations as follows. First, samples in each study population were randomly partitioned into 10 subsets for cross-validation. Second, a flexible discriminant analysis (FDA) using optimal scoring [Biomarkers Identification and Samples Subdivision), which can be downloaded for free at http://www.stat.sinica.edu.tw/hsinchou/genetics/prediction/BIASLESS.htm.To explore the genetic discrepancy and sample subdivisions among the four HapMap II populations, an exploratory unsupervised analysis was performed, followed by an intensive supervised classification analysis. Both analyses used genome-wide SNP and GE markers. First, to understand whether genome-wide SNP and GE markers provide sufficient information for subdividing samples in HapMap II populations, a preliminary unsupervised classification analysis was performed by drawing allele frequency biplots and gene expression biplots based on genome-wide SNP and GE markers, respectively. The analysis was performed using ALOHA software , which i scoring  was applThe genome-wide SNP-based classification analysis clearly separated samples from ethnic populations using allele frequency profiling of genome-wide SNPs interrogated on Affymetrix 500 K , GI_37540521-S (on OR13C5), and rs11986045, significantly improved the testing accuracy of the Affy500K analysis when classifying samples from CHB and JPT; the testing accuracy of the best model increased from 0.89 to 1 , GI_37540521-S (on OR13C5), and rs10485803, significantly improved the testing accuracy of the Array6.0 analysis; the testing accuracy of the best model increased from 0.89 to 1  for “CHB and YRI”, rs489095 (T/C) for “CHB and CEU”, rs6546753 (G/T) for “JPT and YRI”, rs6437783 (C/T) for “JPT and CEU”, and rs735480 (C/T) for “YRI and CEU” , rs12063564, rs2725379 (on PURG), and rs2250072 as the key predictive markers; the Array6.0 + GE analysis identified rs6546753, GI_4506928-S (on SH3GL1), rs1986420, rs6560625 and rs12632185 as the key predictive markers in the best classification model.All samples from the four study populations could also be classified correctly with the best models of the integrative analyses of SNP and GE markers. In comparison with the SNP-only and GE-only analyses, the best integrative model of SNP and GE markers used only four SNP markers and one GE marker to perfectly classify samples from the four populations that we studied  . Before using BIASLESS software, users are encouraged to read the user guide for software installation, initialization, working directories, functions, operation, and format of input/output data. BIASLESS is structured by the following five main components:The developed classification algorithm is packaged intoBIASLESS software with a user friendly interface programmed in language R and R-GUI ((1) Input/Output settings: Users can choose between two types of data input formats (for markers). Users can click the browse buttons to specify the data input directory (for markers), data input directory (for a trait), and result output directory. All results will be saved automatically in the user-specified output directories. Users should fill in a notation or code to indicate any missing values in their marker data and trait data.(2) Cross-validation: (a) Seed for cross-validations: users can use a random seed or provide a fixed seed  for partitioning samples into the training dataset and testing dataset. (b) Fold size: users can select the number of cross-validations .(3) Marker selection (Stop if any of the following three procedures stop): (a) Continue until the number of markers in the model increases to a specified value. (b) Continue until the training/testing accuracy increases to a specified value. (c) Continue until the increment of training/testing accuracy is reduced to a specified value.AA call – blue color, AB call – green color, and BB call – brown color) of SNPs and expression distributions (pink color) of genes selected in the classification model are displayed. (e) Sample misclassification plot: states of correct classification or misclassification in training and testing samples in each of 10-fold cross-validations are shown. Misclassification proportion across all cross-validations is shown for each individual in training and testing samples. Misclassification proportions across all training samples and all testing samples also are shown respectively for each step of marker selection in each of 10-fold cross-validations. (f) Marker impact plot: states of markers selected (red color) or unselected (blue color) in training samples in each of 10-fold cross-validations are shown. The figure also provides the selection times of the identified markers across all cross-validations and which step a marker is selected in a forward variable selection procedure. Graphical outputs in the test example of BIASLESS are provided  Graphical output: (a) Overlay line graph: the curves of training accuracy and testing accuracy are overlaid in a plot  by cross-validation datasets. (b) Parallel coordinates plot: the number of markers selected, training accuracy, and testing accuracy over cross-validation datasets are presented in a parallel coordinates plot. (c) Multidimensional scaling plot: the study samples and selected markers in the final classification model with the highest testing accuracy are presented in a multidimensional scaling plot. (d) Stacked-bar/box-whisker plot: genotypic distributions , fixed time/circumstances of taking the biological samples, and single microarray platform (Illumina’s Sentrix Human-6 Expression BeadChip). More investigations should be carried out to understand the proportions of the identified AIMs specific to the currently used conditions or transferable to more general conditions. For practical applications, we also plan to integrate SNP and GE variation from global genomic studies and construct larger reference database for normalizing GE data. SNP and GE markers will be integrated to identify AIMs and establish robust discriminant models using BIASLESS software. Biological specimen from a tested individual are collected and used to genotype/measure the identified and confirmed AIMs. Finally, SNP genotypes and GE levels of the tested individual are plugged into the discriminant models to determine the correct ethnic group.Regarding the supervised classification method, two points are important to discuss. First, we modified the efficient and broadly used FDA algorithm and integrated forward variable selection and cross-validation procedures with FDA to select key predictive markers from enormous numbers of SNP and GE markers, and we then built accurate classification models for sample subdivision. Our supervised classification procedure provides multiple candidate models . Choosing a model with the highest testing accuracy is recommended but should not be the only criterion for model selection. Other optimal criteria and domain knowledge may need to be considered to determine the best model that satisfying both statistical properties and biological relevance. For example, the cross-validation consistency of a model among all candidate models may be used simultaneously, or genetic knowledge, biological relevance, and quality evaluation of genetic markers may also be integrated to assist in selection of the final classification model. Second, there is a very rich body of literature in the field of supervised classification, including support vector mechanisms  and clasThis study analyzed the data in the HapMap II Project, which contains only four populations, rather than the HapMap III Project, which contains 11 populations because GE data for the majority of samples in the HapMap III Project are not available. However, the proposed method and software can be applied in general to construct AIM panels for additional populations. The SNP data in this study came from two genotyping platforms: Affymetrix 500 K and Array6.0 SNP chips. The results of the sample classification were similar, although the number of SNPs interrogated on Affymetrix 500 K  was only about half the number in Array 6.0 , suggesting that the ancestral information in SNPs identified with Affymetrix Array6.0 is not more informative than that in SNPs identified with Affymetrix 500 K, with regard to the classification of samples in the HapMap II Project. Recently, whole-genome sequencing technology, in comparison with SNP microarrays, has become more common and has promoted the identification of new common SNPs and rare variants. Novel population-specific or ancestry-informative variants may be identified, and more eQTLs that contribute to genetic variation of ancestry informative GE may become available. It will be interesting to investigate if the bottleneck in a SNP-only analysis for discerning samples from closely related populations can be overcome using highly dense common SNPs and rare variants from massive parallel sequencing in the 1000 Genomes Project .In conclusion, we recommend SNP-only analysis for sample subdivision when the study samples come from ethnically distant populations such as Asian (CHB + JPT), African (YRI), and Caucasian (CEU); ancestry informative or population-specific SNPs provide sufficient information for sample classification in this situation, but population-specific SNPs may not be available or may be very hard to identify in ethnically close populations such as Chinese (CHB) and Japanese (JPT). Quantitative GE data, which are more variable than qualitative SNP data, are useful for sample classification after properly removing noisy GE markers. Note, however, that the GE-only analysis is still limited by slightly fluctuating testing accuracies and a larger number of predictive markers even when the samples are from ethnically distant populations. However, GE data do reveal important classification information supplemental to SNP data. Using an integration of SNP and GE markers, we established classification models with a reduced number of markers to accurately assign samples to the correct ethnic populations. Importantly, the genotyping cost is reduced because the number of required markers in an AIM panel is significantly diminished after inclusion of ancestry informative GE markers.http://www.stat.sinica.edu.tw/hsinchou/genetics/prediction/BIASLESS.htm.The BIASLESS software, test examples, and user guide can be downloaded from the BIASLESS website: Project name: Biomarker identification and sample subdivision.Project home page:http://www.stat.sinica.edu.tw/hsinchou/genetics/prediction/BIASLESS.htm.Operating system: MS Windows®.Programming language: Language R and R-GUI.Other requirements: No.Any restrictions to use by non-academics: On request and citation.AF : Allele frequency; AIM : Ancestry informative marker; CEU : CEPH Utah residents; CHB : Han Chinese in Beijing; CVC : Cross-validation consistency; eQTL : Expression quantitative trait loci; FDA : Flexible discriminant analysis; GE : Gene expression; JPT : Japanese in Tokyo; BIASLESS : Biomarkers Identification and Samples Subdivision; SNP : Single nucleotide polymorphism; SSB : Sum of squares between populations; SSW : Sum of squares within population; STRP : Short tandem repeat polymorphism; YRI : Yoruba in Ibadan.The authors declare that they have no competing interests.HCY conceived the study, developed the statistical methods, and prepared the manuscript and software user guide. PLW and CWL developed the BIASLESS software and analyzed the data with HCY. CHC and CHC contributed to the discussion and reviewed the manuscript. All authors have read and approved the final manuscript.Table S1. The total numbers of SNP and GE markers remaining in the analysis after quality control. This table summarizes the number of SNP and GE markers in each analysis of the eight combinations of ethnic populations. After quality control, 18,807 GE markers, 403,067 – 486,092 SNPs in the Affymetrix Human Mapping 500 K set, and 700,682 – 868,434 SNPs in the Affymetrix Array 6.0 set remained. The number of SNPs in the intersection of the Affymetrix Human Mapping 500 K and Array 6.0 sets was 385,493 – 469,057.Click here for fileFigure S1. Classification of HapMap samples using whole-genome SNPs of Affymetrix Human Mapping 500 K set. All samples were superimposed onto a two-dimensional plane in an allele frequency (AF) biplot. (A) CHB, JPT, CEU, and YRI, (B) CHB and JPT, (C) CHB and YRI, (D) CHB and CEU, (E) JPT and YRI, (F) JPT and CEU, and (G) YRI and CEU. Red line with a B symbol indicates CHB samples; blue line with a J symbol indicates JPT samples; gray line with a Y symbol indicates YRI samples; green line with an E symbol indicates CEU samples.Click here for fileTable S2. The best classification models. This table summarizes the key predictive markers, testing accuracy, cross-validation consistency (CVC), and number of genetic markers in the best classification models in the analysis of each of the eight combinations of ethnic populations.Click here for fileTable S3. Genotype frequencies of key predictive SNPs in the best model for the classification of samples from ethnically distant populations. This table summarizes genotype frequencies of the key predictive SNPs in the 500 K-only (first field), Array6.0-only (second field), 500 K + GE (third field) and Array6.0 + GE (fourth field) analyses for the classification of samples from “CHB and YRI” (first panel), “CHB and CEU” (second panel), “JPT and YRI” (third panel), “JPT and CEU” (fourth panel), and “YRI and CEU” (fifth panel). The genotype frequencies also are shown in Figure Click here for fileFigure S2. Interface of BIASLESS software. BIASLESS software programmed in R and R-GUI is a user-friendly tool for the identification of key predictive markers to classify samples from different populations/groups.Click here for fileFigure S3. Graphical outputs in BIASLESS software. BIASLESS software outputs six graphs including (A) overlay line graph, (B) parallel coordinates plot, (C) multidimensional scaling plot, (D) stacked-bar/box-whisker plot, (E) sample misclassification plot, and (F) marker impact plot from the analysis of a test example .Click here for file


The catalog of genetic variants in the horse genome originates from a few select animals, the majority originating from the Thoroughbred mare used for the equine genome sequencing project. The purpose of this study was to identify genetic variants, including single nucleotide polymorphisms (SNPs), insertion/deletion polymorphisms (INDELs), and copy number variants (CNVs) in the genome of an individual Quarter Horse mare sequenced by next-generation sequencing.de novo assembled resulting in 19.1 Mb of new genomic sequence in the horse. Using a stringent filtering method, we identified 3.1 million SNPs, 193 thousand INDELs, and 282 CNVs. Genetic variants were annotated to determine their impact on gene structure and function. Additionally, we genotyped this Quarter Horse for mutations of known diseases and for variants associated with particular traits. Functional clustering analysis of genetic variants revealed that most of the genetic variation in the horse's genome was enriched in sensory perception, signal transduction, and immunity and defense pathways.Using massively parallel paired-end sequencing, we generated 59.6 Gb of DNA sequence from a Quarter Horse mare resulting in an average of 24.7X sequence coverage. Reads were mapped to approximately 97% of the reference Thoroughbred genome. Unmapped reads were This is the first sequencing of a horse genome by next-generation sequencing and the first genomic sequence of an individual Quarter Horse mare. We have increased the catalog of genetic variants for use in equine genomics by the addition of novel SNPs, INDELs, and CNVs. The genetic variants described here will be a useful resource for future studies of genetic variation regulating performance traits and diseases in equids. Of the known SNPs in horses, most (~64%) originate from the Thoroughbred mare used for the genome assembly [The sequencing and assembly of a horse genome was a great achievement in equine genomics and veterinary medicine because of the broad range of potential applications of this information for improving health and performance and for understanding differences among species . To dateP/ build 35), withSequencing the genome of a Quarter Horse was considered important for several reasons. Almost 3 million Quarter Horses were registered in the United States in 2010 according to the American Quarter Horse Association, making it the single largest breed registry in the country . Thus, tHere we describe the whole-genome sequencing of an individual Quarter Horse mare using massively parallel paired-end sequencing. Sequence reads were mapped to the reference Thoroughbred nuclear and mitochondrial genomes. We developed a comprehensive list of genetic variants, including SNPs, INDELs, and CNVs. We annotated genetic variants and examined their impact on gene structure and function. The genomic sequence was also examined for mutations and polymorphisms associated with diseases and traits in horses. Furthermore, we examined biological processes enriched for genetic variants and compared these biological processes between this Quarter Horse mare and the reference Thoroughbred mare.Figure S1), and 14 lanes of 75-bp paired-end sequencing were performed using the Illumina Genome Analyzer II  . A schematic of the sequence mapping and variant identification, annotation, and analysis is presented in Figure Table S2). Sequence reads were trimmed and mapped to the assembled autosomes, × chromosome, and mitochondrial genome of the reference Thoroughbred horse genome (EquCab2.0). A total of 817,470,439 reads  were aligned to approximately 97% of the reference genome , resulting in an average of 24.7X sequence coverage of the horse's genome . Sequence coverage of assembled chromosomes ranged from 13X to 55X . Additionally, 81,993 reads mapped to 100% of the reference mitochondrial genome, resulting in an average of 355.6X sequence coverage. Reads not mapping to assembled chromosomes were subsequently mapped to the unassembled chromosomes (ChrUn), where 12,865,623 reads  were mapped. Next, we de novo assembled 12,657,236 reads  not mapping to the assembled or unassembled chromosomes. The de novo assembly yielded 35,540 contigs totaling 19.1 million bases (Mb) of sequence  was generated  and were further filtered to remove those near other variants  or within the pseudo-autosomal region (PAR) of the × chromosome. The resulting SNPs were then filtered using different minimum read-depths . As expected, increasing the minimum read-depth resulted in fewer SNPs being detected . Additionally, we found that a greater proportion of the total SNPs was lost than that of the total non-synonymous SNPs, with homozygous SNPs being lost at the highest rate . At 10X coverage, which was the minimum read-depth used for the subsequent annotation and downstream analysis, we identified 3,157,093 SNPs. Additionally, we characterized and annotated SNPs detected at lower minimum read-depths . Intergenic SNPs, located within 1,000 bases (1 kb) upstream or downstream of a gene, were determined as well as genic SNPs located within introns, non-coding exons, 5' and 3' untranslated regions (UTR), intron and exon splice sites, and coding exons. Non-synonymous SNPs were further annotated as either radical or conservative . Additionally, we identified SNPs located within the coding exons of 305 candidate imprinted genes for future studies in the horse ([Table S21). Analysis of the mitochondrial genome identified 71 SNPs, including 5 heteroplasmic SNPs .We analyzed sequences mapped to the assembled chromosomes for putative SNPs, INDELs, and CNVs. SNPs were required to meet several stringent criteria (see http://www.ncbi.nlm.nih.gov/projects/SNP/) revealed that 342,726 were known and 2,814,367 were novel, including 18,140 non-synonymous SNPs  and 2,629 complex  SNPs. Comparison of the putative SNPs to those identified in the sequencing and assembly of the reference Thoroughbred genome revealed that 244,669 SNPs were overlapping between the 2 horses, including 174,682 intergenic and 69,987 genic SNPs. By removing overlapping known SNPs, we found that the Quarter Horse and Thoroughbred genomes had 2,912,424 and 522,610 unique SNPs, respectively.Comparison of putative SNPs in the mapped sequences to the horse SNP database . Additionally, we identified and annotated INDELs using lower read-depth coverage . Although our analysis was limited to INDELs no greater than 8 bp, the overwhelming majority were single bp gains and losses . Of the INDELs identified, 2,574 caused a frameshift mutation (frameshift deletion + insertions) in a coding exon, whereas 35 did not . The sizes ranged from 3.74 kb to 4.84 Mb, with an average length of 296.1 kb. Annotation of the CNVs indicated that 192 and 90 were genic and intergenic, respectively.Copy number variants (CNVs) were identified using a read-depth algorithm that corrects for GC bias. CNVs were then filtered to remove those within telomeric regions, as investigation of these regions is associated with high false discovery rates . The fil-15), cellular processes , and signal transduction . Separate functional enrichment analysis of genes with either radical or conservative non-synonymous SNPs revealed that the biological processes of the enriched pathways remained unchanged, although there was an increase in enrichment of conservative amino acid changes in the immunity and defense pathway . Clustering analysis of INDELs indicated that genes containing variants were enriched in pathways for signal transduction , developmental processes , and cellular processes , immunity and defense , and signal transduction  enriched for these classes of genetic variants. Using the horse Ensembl gene identification (ID) for the functional annotation clustering, we found that only a small percentage of BP terms were returned (0.6%). Therefore, we decided to perform the analysis using the human orthologs of horse genes found to possess genetic variants. Of the 6,899 genes containing non-synonymous SNPs, 5,712 had human orthologs and 3,880 returned BP terms (67.9%). Clustering analysis indicated that non-synonymous SNPs were enriched (P < 0.05) in pathways for sensory perception  revealed that the Quarter Horse had SNPs enriched in pathways for sensory perception , cellular processes , and signal transduction , whereas the Thoroughbred had SNPs enriched in pathways for immunity and defense , sensory perception , and cellular processes . As the enrichment analysis reflected percentages of genes within pathways, and because the Quarter Horse contained nearly three times as many heterozygous SNPs as the Thoroughbred, we determined the total number of genes for each BP term that contained a non-synonymous heterozygous SNP. As expected, the Quarter Horse had more genes containing SNPs for each BP class, except for the immune and defense (P = 2.4 × 10-27) and nucleic acid metabolism (P = 1.1 × 10-11) pathways .Comparison of biological pathways containing heterozygous non-synonymous SNPs between the Quarter Horse and reference Thoroughbred  gene, which causes hereditary equine regional dermal asthenia (HERDA)[Next, we investigated the DNA sequence of the Quarter Horse mare for mutations of known diseases and for SNPs associated with various traits in horses Table . We founa (HERDA), heteroza (HERDA), and homa (HERDA),19. No oTable S3). However, caution should be used with these variants as the FDR is expected to increase with a reduced minimum coverage, although not directly proportional to the decrease of the FNR.The current catalog of equine genetic variants is limited and primarily consists of those detected from the sequencing and assembly of a single Thoroughbred horse . In the De novo assembly of reads not mapping to the assembled or un-assembled chromosomes led to the generation of 19.1 Mb of new horse genomic sequence. Our analysis of CNVs in horses and cattle using array comparative genomic hybridization , as well as studies in the human and mouse show that large (Mb) deletions are common variants in the genome [de novo assembled sequence represents sequence missing from the reference Thoroughbred genome due to homozygous deletions.e genome -24. Thise genome  is commoFunctional annotation clustering analysis of genetic variants revealed that pathways for sensory perception, signal transduction, protein processing, cellular process, and immunity and defense were differentially affected by each type of genetic variant , suggesting varying degrees of tolerance and selection for genetic variants underlying these biological processes. Genes involved in sensory perception pathways contained most of the genetic variation, primarily SNPs 27%) and CNVs (60%). The observed enrichment in sensory perception genes may be related to selection of the Quarter Horse for a relatively calm disposition ,27, alth% and CNVThis is the first sequencing of a horse genome by next-generation sequencing and the first genomic sequence of an individual Quarter Horse. The genetic variants identified in this study will be a useful resource for future studies to understand the genetic basis of phenotypic variation and disease in equids.DNA was isolated from whole blood of a Quarter Horse mare using a standard phenol-chloroform extraction including 2 phenol-chloroform-isoamyl (PCI) steps, followed by rinses with chloroform, isopropanol, and 70% ethanol. The sample was suspended in Qiagen EB buffer . The Texas A&M University Institutional Animal Care and Use Committee approved this study.For the construction of sequencing libraries, we first sonicated high-quality genomic DNA by pulsing 3X for 15 sec/pulse at 14% using a Sonic Dismembrator 500  and purified with an Invitrogen PureLink PCR Kit . The DNA was blunt end-repaired, adenylated, and ligated with paired-end adaptors, according to the manufacturer's protocol . The prepared library was resolved on a 2% low range agarose gel and a 2-mm section of DNA was isolated at 271 bp . The library was then enriched according to the manufacturer's protocol . The size and concentration of the sequencing library was determined by PCR, visualization of polyacrylamide gel electrophoresis (PAGE) gels, and through the use of the Agilent 2100 Bioanalyzer DNA kit . Cluster generation and paired-end sequencing was performed according to the manufacturer's protocols (Illumina) at the AgriLife Genomics and Bioinformatics Center We used the trim function in the CLC Genomics Workbench  using the following parameters: ambiguous limit, 2; ambiguous trim, yes; quality limit, 0.1; quality trim, yes; and, remove 3' nucleotide, no; remove 5' nucleotide, no. The CLC Genomics Workbench Reference Mapping function was used to assemble the trimmed reads to the equCab2 reference assembly using the following parameters: similarity score = 0.8; and, length fraction = 0.5. Paired-end reads were mapped using an insert range of 180-bp to 500-bp and reads mapping to multiple places in the reference were mapped using the random setting. In order to determine the percent of the genome that was mapped by uniquely mapped read, we used the previously stated settings with nonspecific reads being ignored.We used the SNP detection function in CLC Genomics Workbench, based on the neighborhood quality standard (NQS) algorithm, using the following parameters: minimum coverage = 5; minimum central base quality = 30; average base quality over a window length of 11 nucleotides = 15; and, minimum allele frequency = 35%. SNPs were filtered by removing those within 10 bp of another variant (both INDELs and SNPs). Also, SNPs located within the pseudo-autosomal region (PAR) were removed . The remWe used the deletion and insertion polymorphism (DIP) function in CLC Genomics Workbench using the following parameters: minimum coverage = 5; minimum allele frequency = 35%; and, maximum expected variations = 2. INDELs were filtered in a similar manner as SNPs, with those near other variants and within the PAR being removed. Analyses were conducted at both 5X and 10X read-depths.We used the Control-FREE copy number algorithm (FREEC) program to identify CNVs in the mapped sequence data . We optihttp://galaxy.psu.edu/ and ANNOVAR software programs [We re-annotated the variant calls from CLC Genomics using both Galaxy programs ,30. The Table S29). Statistical significance  for each enriched group was determined using Fisher's combined probability test with the p-value created from the DAVID Functional Annotation Tool. The enriched genes for each biological process group were compared between the Quarter Horse and reference Thoroughbred using the Fisher's exact test for a 2 × 2 contingency table.The gene lists for each group were converted to human Ensembl gene IDs using Ensembl Biomart. Biological function analysis was performed through the DAVID Functional Annotation Tool, using the default settings . The resde novo assembly of all reads that did not align to the reference genome (including ChrMt and ChrUn) using de Bruijn Graphs (CLC Genomics). We were able to assemble 8,186,040 of the 12,657,236 unmapped reads into contigs with minimum and average contig lengths of 200 bp and 537 bp, respectively. We performed de novo assembly of reads not mapping to either assembled or unassembled chromosomes using the following parameters: similarity = 0.8; length fraction = 0.5; insertion cost = 3; deletion cost = 3; mismatch cost = 3; minimum paired distance = 180 bp; and, maximum paired distance = 500 bp. The resulting 35,540 contigs were further analyzed by BLAST, mapping all complete genomes and chromosomes from RefSeq.We created a http://www.ncbi.nlm.nih.gov/sra under accession number SRX110702. The mapped sequences and genetic variants have been submitted to Intrepid Bioinformatics and are available for public viewing http://server1.intrepidbio.com/FeatureBrowser/ngsdatasetrecord/record?ngsrecord=6197673305. A complete list of SNPs, INDELs, and CNVs are listed in Additional files The Illumina FASTQ data generated from this study has been submitted to the NCBI Sequence Read Archive The authors declare that they have no competing interests.RD generated the sequencing libraries and analyzed the data. NG and CJ sequenced the libraries and analyzed the data. RD, NDC, JS and SVD conceived the study and wrote the manuscript. All authors read and approved the final manuscript.Distribution of paired-end sequencing library insert length. Figure indicating distribution of library insert length and number of sequence reads generated per length.Click here for fileSequencing reaction statistics. Table of raw summary statistics of each lane of Illumina sequencing.Click here for fileDetailed overview of mapping results. Table of total sequence reads (paired and single end), the number of mapped reads and bp to assembled and unassembled chromosomes.Click here for fileAverage depth of coverage of assembled chromosomes. Figure of average mapping read coverage of each chromosome.Click here for file5X filtered SNPs and INDELS identified by whole genome sequencing. Table listing chromosomal coordinates of SNPs and INDELs identified using a minimum sequence read depth coverage of 5X.Click here for file10X filtered SNPs and INDELS identified by whole genome sequencing. Table listing chromosomal coordinates of SNPs and INDELs identified using a minimum sequence read depth coverage of 10X.Click here for fileLoss of SNPs by post-filtering. Table describing the number of SNPs and INDELs removed by post-filtering.Click here for fileComparison of minimum depth of coverage SNP filters. Total number of (A) SNPs and (B) non-synonymous SNP remaining after increasing the minimum sequence read depth of coverage. Proportion of homozygous and heterozygous (C) SNPs and (D) non-synonymous SNPs lost by increasing minimum sequence read depth of coverage.Click here for fileAnnotation of SNPs with minimum read depth coverage of 5X. Table describing the genic annotation of SNPs identified at 5X coverage.Click here for fileAnnotation of SNPs with minimum read depth coverage of 5X relative to the Ensembl gene annotation. Table describing the intergenic and genic location of SNPs identified at 5X coverage.Click here for fileGenes affected by SNPs with minimum read depth coverage of 5X. Table describing the number of genes identified with SNPs, including the annotation of the SNPs within the genetic elements.Click here for fileOverview of SNP annotation with minimum read depth coverage of 5X. Table summarizing the number and annotation of SNPs with minimum read depth coverage of 5X.Click here for fileAnnotation of SNPs with minimum read depth coverage of 6X. Table describing the genic annotation of SNPs identified with minimum read depth coverage of 6X coverage.Click here for fileAnnotation of SNPs with minimum read depth coverage of 6X relative to the Ensembl gene annotation. Table describing the intergenic and genic location of SNPs identified with minimum read depth coverage of 6X coverage.Click here for fileAnnotation of SNPs with minimum read depth coverage of 7X. Table describing the genic annotation of SNPs identified with minimum read depth coverage of 7X coverage.Click here for fileAnnotation of SNPs with minimum read depth coverage of 7X relative to the Ensembl gene annotation. Table describing the intergenic and genic location of SNPs identified with minimum read depth coverage of 7X coverage.Click here for fileAnnotation of SNPs with minimum read depth coverage of 8X. Table describing the genic annotation of SNPs identified with minimum read depth coverage of 8X coverage.Click here for fileAnnotation of SNPs with minimum read depth coverage of 8X relative to the Ensembl gene annotation. Table describing the intergenic and genic location of SNPs identified with minimum read depth coverage of 8X coverage.Click here for fileAnnotation of SNPs with minimum read depth coverage of 9X. Table describing the genic annotation of SNPs identified with minimum read depth coverage of 9X coverage.Click here for fileAnnotation of SNPs with minimum read depth coverage of 9X relative to the Ensembl gene annotation. Table describing the intergenic and genic location of SNPs identified with minimum read depth coverage of 9X coverage.Click here for fileAnnotation of SNPs with minimum read depth coverage of 10X. Table describing the genic annotation of SNPs identified with minimum read depth coverage of 10X coverage.Click here for fileAnnotation of SNPs with minimum read depth coverage of 10X relative to the Ensembl gene annotation. Table describing the intergenic and genic location of SNPs identified with minimum read depth coverage of 10X coverage.Click here for fileGenes affected by SNPs with minimum read depth coverage of 10X. Table describing the number of genes identified with SNPs, including the annotation of the SNPs within the genetic elements.Click here for fileImprinted genes containing SNPs in coding regions. List of known imprinted genes in humans containing coding SNPs in Quarter Horse.Click here for fileAnnotation of INDELs with minimum read depth coverage of 5X.Click here for fileAnnotation of INDELs with minimum read depth coverage of 10X. Table describing genic annotation and location of INDELs with minimum read depth coverage of 10X.Click here for fileDistribution of total number of INDELs by length. Plot showing the number of INDELS by length identified with minimum read depth coverage of 10X.Click here for fileCoordinates of CNVs. Table describing the location of CNVs identified by analysis of read depth.Click here for fileIdentification of a loss within an olfactory gene cluster by sequencing read-depth. Plot of read depth indicating a copy number loss within olfactory gene cluster.Click here for fileIdentification of a homozygous deletion by read-depth coverage. Plot of read depth indicating a homozygous deletion.Click here for fileBiological process enrichments and associated p-values for SNPs, INDELS, and CNVs with minimum read depth coverage of 10X. Statistical analyses of biological process enrichments for SNP, INDELS, and CNVs with minimum read depth coverage of 10X.Click here for fileBiological process enrichment analysis of conserved and radical non-synonymous SNPs. Statistical analyses of biological process enrichments for conserved and radical SNPs with minimum read depth coverage of 10X.Click here for fileAnnotation of SNPs in the reference Thoroughbred genome. Table describing genic annotation and location of known SNPs in the reference Thoroughbred genome.Click here for fileComparison of biological processes affected by SNPs in the Quarter Horse and Thoroughbred genomes. (A) BP enrichment of non-synonymous homozygous SNPs and (B) heterozygous SNPs in the Quarter Horse genome. (C) BP enrichment of non-synonymous heterozygous SNPs in the Thoroughbred genome. (D) Number of genes containing non-synonymous SNPs within each BP pathway.Click here for fileBiological process enrichments and associated p-values for SNP comparison between Quarter Horse and reference Thoroughbred with minimum coverage of 10 reads. Comparison and statistical analysis of biological process enrichments between Quarter Horse and reference Thoroughbred.Click here for fileStatistical analysis of enriched genes in the Quarter Horse and reference Thoroughbred. Statistical analysis of comparison between enriched genes in the Quarter Horse and reference Thoroughbred.Click here for fileBiological process terms and classification. All terms used for biological processes enrichment and their associated category.Click here for file


In this study we carried out a genome-wide association analysis for plant and grain morphology and root architecture in a unique panel of temperate rice accessions adapted to European pedo-climatic conditions. This is the first study to assess the association of selected phenotypic traits to specific genomic regions in the narrow genetic pool of temperate japonica. A set of 391 rice accessions were GBS-genotyped yielding—after data editing—57000 polymorphic and informative SNPS, among which 54% were in genic regions.−7 to 0.92 (median: 0.25). In most cases, the significant detected associations co-localised with QTLs and candidate genes controlling the phenotypic variation of single or multiple traits. The most significant associations were those for flag leaf width on chromosome 4 (FDR = 3 · 10−7) and for plant height on chromosome 6 (FDR = 0.011).In total, 42 significant genotype-phenotype associations were detected: 21 for plant morphology traits, 11 for grain quality traits, 10 for root architecture traits. The FDR of detected associations ranged from 3 · 10We demonstrate the effectiveness and resolution of the developed platform for high-throughput phenotyping, genotyping and GWAS in detecting major QTLs for relevant traits in rice. We identified strong associations that may be used for selection in temperate irrigated rice breeding: e.g. associations for flag leaf width, plant height, root volume and length, grain length, grain width and their ratio. Our findings pave the way to successfully exploit the narrow genetic pool of European temperate rice and to pinpoint the most relevant genetic components contributing to the adaptability and high yield of this germplasm. The generated data could be of direct use in genomic-assisted breeding strategies. Oryza sativa) can be subdivided into two subspecies -O. sativa indica and O. sativa japonica- and five major groups: indica, tropical japonica, temperate japonica, aus and aromatic );Oryza sativa varieties from the Rice Germplasm Collection maintained at the CREA-Rice Research Unit . Varieties were selected based on the results of a genetic diversity analysis with 24 independent SSR markers [The accession panel used in this study included 391  markers ; the aimThe sampled collection included 9 aromatic, 11 aus, 22 indica, 96 tropical japonica and 253 temperate japonica accessions. Most of these were temperate rice developed and selected in Italy (126 accessions), or developed elsewhere and adapted to Italian agro-climatic conditions (127 accessions). This collection was representative of the worldwide rice diversity , and wasThe distribution of the 391 accessions per taxonomic group can be seen in in-silico digestion of the Nipponbare reference genome. Digested DNAs were ligated to 12 of 0.6 / adapter pairs , and the 96-plex library constructed according to the GBS protocol. The libraries were loaded into Genome Analyzer II  for sequencing. Raw sequence data filtering, sequence alignment to the rice reference genome  with a TECAN Freedom EVO150 liquid handling robot . For each accession, a single individual plant was used. Whole-genome genotyping was carried out using Genotyping-By-Sequencing (GBS) technology ; DNA digGSP-1.0, ) and SNPiversity , 34. Defiversity ) using dA total of 166418 SNPs were called from GBS genotyping. Missing SNP genotypes were then imputed using the FILLIN  algorithm in the Tassel GBS pipeline, accounting for complete homozygosity of domestic rice by considering rice accessions as fully inbred (inbreeding coefficient ≥ 0.99). FILLIN is based on haplotype reconstruction around recombination break points. Haplotypes are clustered per genotype similarity using the Hamming distance function. This information is eventually used to impute the target locus in an iterative approach that attemps, through a Markov Chain MonteCarlo (MCMC) process, to maximise the likelihood of the observed SNP calls given the unobserved imputed genotypes. When the FILLIN algorithm could not find haplotypes to satisfy any of the threshold requirements, the SNP locus was not imputed. After imputation, SNPs with call-rate ≥ 90% and MAF  > 1% (109888 SNPs) were used to investigate population structure and estimate linkage disequilibrium (LD). SNPs with call-rate ≥ 90% and MAF, respectively, ≥ 5% or ≥ 10% were used in the GWAS for plant and grain traits (57179 SNP) and for root (37827 SNP) traits. The distribution of SNP per chromosome is reported in Temperate japonica, Tropical japonica, Indica, Aromatic and Aus) -or mixtures thereof. The underlying population structure was investigated using SNP genotype data to estimate the most likely number of clusters (K) into which the accessions can be grouped, and their degree of admixture. The following likelihood model was used to estimate the value of K:gij is the observed number of copies  of the reference allele at marker j in individual i; qik is the fraction of the genome of i contributed by population k; and fkj is the reference allele frequency at marker j in population k. Q and F are the matrices of estimated qik and fkj, and have dimensions I x K  and J x K (n. of markers x n. of clusters) respectively; Q and F given the observed genotypes  was monitored. The value of K that best fitted the data (the most likely number of clusters in the population) was determined based on the lowest prediction error and the smallest number of iterations for convergence. As described by Alexander et al. . From the matrix of contributions Q the probabilities of belonging to one of the clusters were derived, and accessions assigned accordingly.The model was run for values of . , the nuTemperate and Tropical japonica genetic diversity within the available rice collection, was selected and used for agronomic evaluations in field conditions, to record phenotypes related to plant morphology and grain quality. A further subset of 93 temperate and tropical japonica accessions was also evaluated for root architecture traits in controlled conditions. The list of the accessions selected for phenotyping -both plant and grain morphology and root traits- is reported in An unweighted neighbor-joining (NJ) tree was constructed using a shared allele index based on a dissimilarity matrix estimated from the SNP dataset . Based oTwo experiments were conducted to record phenotypes on the rice accessions analysed: a field experiment for plant and grain morphology traits (153 accessions), and a greenhouse experiment for root traits (93 accessions). In the field experiment, rice plants were grown at the CREA-Rice Research Unit . Seeds were sown directly into dry soil in a randomized complete block design with three replicates. Each replicate (plot) measured 1.7 x 0.4 m and consisted of three rows, with 0.2 m inter-row and 0.3 m inter-plot spacing. Within each row, seeds were planted at 0.15 m from each other. A standard fertilization was applied, and conventional submersion was realised at 3°—4° leaf development stage up to one month before harvesting. At the post-emergence stage, two herbicide treatments were applied. During the growing season 7 agronomic traits related to plant and grain morphology were measured on five individual plants per replicate, following the specifications of the International Union for the Protection of New Varieties of Plants (IUPOV): plant height (PH), panicle length (PL), flag leaf length (FLL), flag leaf width (FLW), seed length (SL), seed width (SW) and seed length/width ratio  placed at the top of PVC pipes (st class), 27°-45° (2nd class), 45°-57° (3rd class), 57°-90° (4th class). Roots were then placed on different glass trays according to the layer, taking care of spreading the smallest secondary roots, and scanned. Images were analysed with the WinRHIZO software (Regent Instruments Inc.). A total of 8 root traits were considered for the GWAS analysis: root dry weight (RDW), root length (RL), root volume (RV), root surface area (RSA), number of root tips (RT), length of thick roots (RL_TK), volume of thick roots (RV_TK) and volume of vertical thick roots (RV_VTK). RL, RV, RSA and RT were measured on roots from class 3 and 4; RL_TK and RV_TK were measured on roots of any angle but with diameter > 0.6 mm; RV_VTK on roots with diameter > 0.6 mm and angle class 4. During the root experiment, also shoot dry weight (SDW) was measured. All phenotypes were measured in 2012. Details are reported in For root phenotyping rice plants were grown under controlled conditions in plastic cylindrical mesh baskets 2 is the squared difference between observed and expected haplotype frequencies [r2 values were averaged over SNPs grouped based on stepwise increasing base-pair distance . The average LD as a function of base-pair distance was used to estimate LD decay in the rice accessions.The pairwise linkage disequilibrium (LD) among SNPs was estimated as quencies . For LD y is the vector of phenotypic observations (averages across replicates); μ is the overall mean; b is the vector of SNP effects, with the corresponding matrix X of SNP genotypes ; a is the vector of polygenic effects with the related incidence matrix Z; e is the vector of residuals. Polygenic and residual random effects were assumed to be normally distributed K is the kinship matrix among all accessions based on SNP genotypes and calculated according to Astle and Balding [I is the identity matrix, and Only SNPs with a call-rate ≥ 90% and minor alle frequency (MAF) ≥ 0.05 (plant and grain traits) or ≥ 0.1 (root traits) were used for the GWAS. Data filtering was applied separately to the two subsets of accessions phenotyped one for plant and grain morphology traits (153), and the other for root traits (93 accessions). This led to different numbers of SNPs used in GWAS: 57179 and 37827 for plant/grain and root traits respectively. The association between SNP genotypes and plant, grain and root phenotypes was tested fitting one SNP at a time in a mixed linear model of the following form (in matrix notation): Balding , I is tht-test contrasting the model including the SNP effect with a reduced model including just the polygenic effect. The issue of testing multiple hypothesis  was addressed by monitoring the false discovery rate  of deteADMIXTURE [--r2 --ld-window-kb 5000 --ld-window 99999 --ld-window-r2 0 command in Plink [R [SNP-calling, imputation of missing genotypes and the GWAS were carried out using the TASSEL pipeline and software package for genetic analysis , 41. PopDMIXTURE . Pairwisin Plink . Plots aPlink [R .Tassel GBS pipeline, the residual missing rate was 10.8%. Only SNPs with a call-rate ≥ 90% were used for downstream biostatistics analyses. For the association study, an additional filter on MAF was applied: MAF ≥ 0.05 for plant and grain traits (153 accessions), resulting in 57179 SNP and MAF ≥ 0.10 for root traits (93 accessions), resulting in 37827 SNP . This is not unexpected, considering that some of the accessions are likely to be crosses between populations, and that the tropical japonica subgroup has been repeatedly reported to be constituted by two main clusters [K = 5, 243 were assigned to temperate japonica, 85 to tropical japonica, 22 to indica, 9 to aromatic, 11 to the aus group, with probability higher than 0.6. Twenty-one accessions had a probability of assignment lower than 0.6, and appeared to be admixed, mainly between the temperate and tropical japonica groups . After  SNP see . Conside SNP see , the mosbe K = 5 , which taponica; ). Howeveclusters . Considea groups . The unwa groups  displayecessions , 47. TheBoth in the admixture and NJ tree analyses, the admixed accessions most likely originate from interspecific breeding programmes, with extensive gene exchange between specific subpopulations. Within the japonica group, the temperate and tropical clusters were clearly distinguished, and represented most of the European accessions included in the study . The analysis of population structure guided the selection of the most suitable set of accessions to be phenotyped in the field and root experiments. Mostly, temperate and tropical japonica accessions were selected, with indica and aromatic accessions as outgroups.On the selected set of informative temperate rice accessions, 5 plant morphology, 4 grain quality and 8 root traits were measured . The anarSL,SW = −0.48), SW vs SL/SW ratio  and PL vs SW . The strongest positive correlations were rRL,RSA = 0.99, rRSA,RV = 0.99, rRL_TK,RV_TK = 0.98, rRSA,RT = 0.98, and rRV,RT = 0.97. PH and PL showed a moderate positive correlation , like PL and FLL . Shoot dry weight was positively correlated with all root traits; in particular, correlations with RDW, RL and RSA higher than 0.75 were observed.Most traits (14 out of 16) showed a larger than 2-fold difference between the minimum and the maximum values . The coe for FLW , which i for FLW . All corr2 between all SNP markers genotypes in the 391 accessions included in the study. The average r2 as a function of inter-marker distance was used to estimate the LD decay in the population. Estimates of mean r2 at each 10 kbps incremental steps between 0 and 5000 kbps (5 Mbps correspond to ∼10 cM) indicated that average r2 starts at about 0.4 for very close markers (< 50 kbps), and decays to approximately 0.1 for SNPs as distant as 5 Mbps , an500 kbps ). Data fAF (e.g. ). On theAF (e.g. ).The wide phenotypic variability recorded in the analysed plant material and the long-range estimated LD, are both preliminary building blocks for the construction of a successful model for a genome-wide association study in temperate rice.The traits included in the GWAS analysis were categorised in three classes: plant morphology, grain quality and root traits. The ample phenotypic variability and long-range LD extent, coupled with an average marker density of one SNP every 6 kbps, provided a good basis for whole-genome association mapping in temperate rice. The near normal distribution of most recorded phenotypes justified the use of a linear regression model for continuous traits in the association study , in whic−7 to 0.92, with median 0.25. In GWAS studies it is however debatable whether multiple tests of a single global null hypothesis or individual tests of multiple hypothesis are concerned, and whether the threshold depends on the number of tests or on the prior probability of true SNP- phenotype associations [P(reject H0|H1 is true)): probability of correctly rejecting the null hypothesis of no association when the SNP is truly associated to the phenotype), divided by estimated p-value:The FDR was estimated to monitor the effect of multiple testing: FDR for the associations in ciations . In the −4 for shoot and grain traits, and 10: 37827 = 2.64 · 10−4 for root traits. Assuming a power of 0.5, the posterior odds of the associations reported in P(x) = 0.46) between SNP S6_23311513 and PH on chromosome 6, to a maximum of 6387094 (P(x) = 0.99) between SNP S4_31080152 and FLW on chromosome 4, with median value of 2.26 (P(x) = 0.69); 13 out of 42 associations (∼31%) had posterior odds > 4 (P(x) > 0.8).If 10 detectable signals are expected over the entire genome for any given trait, a plausible estimate of the prior odds of true association would be 10: 57179 = 1.75 · 10These results indicate the value of looking at significance of GWAS results from multiple perspectives, and point to a high credibility of the associations detected in the analysed panel of temperate rice accessions.log > 4) detected in the GWAS study are listed in −7 to 0.927 (average 0.32), with seven associations having a FDR < 0.1.The Manhattan plots of -log and the Q-Q (quantile-quantile) plots of expected (under a Gaussian distribution) vs observed p-values for SNP-based genotype-phenotype associations for the three classes of plant traits under examination are reported in Figs −11), and SNP S6_22330734 on OSA6 for PH . Other 4 associations for plant morphological traits had a p-value < 1 · 10−5 and included two SNP for PH (S5_17644414 and S6_24644771) and one each for FLL (S2_3104099) and PL (S9_13253243). Fourteen loci were associated with a morphological trait with p-value < 1 · 10−4 and one locus with p-value < 1 · 10−3  > 4) between SNPs and the analyzed plant morphological traits were identified. The association with the lowest p-values were SNP S4_31080152 on OSA4 for FLW  biosynthetic pathway [Two SNPs were found to be significantly associated to PH and co-localized with two known QTLs implicated in the control of plant height in rice: SNP S2_29790001 associated to qPH2.2, which was mapped on chromosome 2 using fifteen Chromosome Segment Substitution Lines ; and SNP pathway . It is w pathway , thus mu pathway . Moreove pathway .O. sativa variety crossed with the wild relative O. rufipogon [SNP S3_13264119 associated to PL co-localised with two QTLs on chromosome 3: qPLT3-2, identified using a Double Haploid (DH) population from an indica and japonica cross grown at nine different locations across Asia ; and pl3ufipogon .sd-1 gene running a model that did not account for population structure  arising from a defective GA20-oxidase enzyme, a GA biosynthetic enzyme, which originates from a deletion of 280 bp within the coding region of Os20ox2 in indica rice, or from a substitution of a highly conserved Leu amino-acidic residue at position 266 in japonica rice .A genome-wide association study was successfully carried out for a set of specific traits related to plant morphology, grain quality and root architecture in a unique panel of temperate rice accessions adapted to European temperate conditions. This is one of the largest studies on temperate rice for morphological and root traits, especially where the tridimensional architecture of the roots is considered. Although much is already known about root functioning in rice, there is still need for better understanding of root traits in the context of breeding strategies for growth under water-saving management systems.The effectiveness of the presently developed platform for high throughput phenotyping, genotyping and association studies for temperate japonica was validated by confirming the involvement of specific QTLs and known genes in the control of plant, root and seed phenotypes of relevance for high yield and adaptation to the temperate climate . A number of QTLs related to the different agronomically relevant traits have been detected. These results and the developed platform bear the potential of being used in breeding programmes to improve the quality of the grain and the drought tolerance of temperate rice accessions. The European GWAS platform was developed using a panel constituted mostly of adapted temperate japonica breeding lines rather than on a diversity panel providing the opportunity to apply the obtained association results directly to ongoing and future breeding programs in Europe and other temperate rice-growing areas . The identified segregating haplotypes are valuable tools to define the most suitable parents for crossing, to enhance the frequency with which these haplotypes appear in the progenies.Marker Assisted Selection for specific favorable haplotypes will enable us to increase breeding efficiency and decrease cost by reducing the number of plants to be brought to the next generation of breeding or that need to be in-depth phenotyped.S1 FigRoot phenotyping experiment. A) The various phases of the experiment. B) The four layers defined according to the root growth angle.(PDF)Click here for additional data file.S2 FigThe phenotypic distribution of the plant morphology, grain quality and root traits used for the genome-wide association study.(PDF)Click here for additional data file.S1 TableList of accessions used in the study with their geographical origin, commercial class, taxonomical group and collected pheno- types .(PDF)Click here for additional data file.S2 TableCommand lines and parameters used to generate the imputed non-filtered SNP dataset with Tassel GBS pipeline 3.0. Command lines with the “Plugin” suffix are part of the TASSEL pipeline; filenames and directories are not listed. In order to run the “FastImputationBitFixedWindowPlugin”, a tab-separated file with an inbreeding coefficient of 0.99 for each taxon was provided. When not reported, default parameters were used.(PDF)Click here for additional data file.S3 TableList of accessions used in the study with the taxonomical group they have been assigned to and the probability of assignment to each of the 5 major groups based on their SNP genotypes.(PDF)Click here for additional data file.S4 TableAssociations between SNP genotypes and plant height in the region of the gene SD1 on chromosome 1, from a linear GWAS model without accounting for population structure.(PDF)Click here for additional data file.


The genetic architecture of complex traits in farmed animal populations is of interest from a scientific and practical perspective. The use of genetic markers to predict the genetic merit  of individuals is commonplace in modern farm animal breeding schemes. Recently, high density SNP arrays have become available for Atlantic salmon, which facilitates genomic prediction and association studies using genome-wide markers and economically important traits. The aims of this study were (i) to use a high density SNP array to investigate the genetic architecture of weight and length in juvenile Atlantic salmon; (ii) to assess the utility of genomic prediction for these traits, including testing different marker densities; (iii) to identify potential candidate genes underpinning variation in early growth.n = 622) were measured for weight and length traits at one year of age, and genotyped for 111,908 segregating SNP markers using a high density SNP array. The heritability of both traits was estimated using pedigree and genomic relationship matrices, and was comparable at around 0.5 and 0.6 respectively. The results of the GWA analysis pointed to a polygenic genetic architecture, with no SNPs surpassing the genome-wide significance threshold, and one SNP associated with length at the chromosome-wide level. SNPs surpassing an arbitrary threshold of significance  were aligned to an Atlantic salmon reference transcriptome, identifying 109 SNPs in transcribed regions that were annotated by alignment to human, mouse and zebrafish protein databases. Prediction of breeding values was more accurate when applying genomic (GBLUP) than pedigree (PBLUP) relationship matrices (accuracy ~ 0.7 and 0.58 respectively) and 5,000 SNPs were sufficient for obtaining this accuracy increase over PBLUP in this specific population.A pedigreed population of farmed Atlantic salmon  contains supplementary material, which is available to authorized users. Salmo salar), an anadromous species found primarily in the northern Atlantic Ocean, is widely known for its importance in both wild fishing and aquaculture. According to statistics from the Food and Agriculture Organization (FAO), the estimated global economic value of this species in 2010 was approximately $7.8 billion )The objectives of this study were (i) to use the high density (~132 K) SNP array to estimate genetic parameters for weight and length of juvenile farmed salmon and compare to those based on pedigree; (ii) to detect individual SNPs/chromosomes associated with these traits; (iii) to estimate breeding values and prediction accuracy for the two traits by applying the pedigree and the genomic relationship matrix across different marker densities; (iv) to identify putative growth candidate genes by annotating the most significant markers from transcribed regions of the genome.The final dataset used for the GWAS consisted of ~ 112 K QC-filtered SNPs successfully genotyped in 622 fish (from 61 full sibling families) with weight and length measurements taken approximately 1 year post-hatching. Sex of the fish was predicted based on the Y-specific probes on the SNP array  and theP < 0.005 from model (1)] and were used for determining putative candidate genes , whereas one SNP mapping to chromosome 17 surpassed the chromosome-wide significance level for length and was estimated to explain ~ 7 % of the additive genetic variation Table . 684 of The use of the SNP markers for genomic prediction (GBLUP) of the weight and length traits was assessed and compared to the equivalent model using the pedigree to define relationships between the animals (PBLUP) using a five-fold cross validation design. The accuracy of the GBLUP model was approximately 20 % higher than PBLUP for both traits when using all markers in the model, reaching a value of approximately 0.7 within this population. Interestingly, while the prediction accuracy was improved by approximately 20 % with increased marker density from 0.5 K to 5 K SNPs, there was very little or no improvement in accuracy of prediction with increased marker density beyond this level. At the lowest marker density analyzed (0.5 K), the accuracy of GBLUP and PBLUP had the similar performance in both traits Fig. . HoweverA large proportion of the SNPs on the 132 K Axiom array were derived from an RNA-Seq experiment and, therefore, are likely to be located within genes. 109 of the 684 SNPs surpassing a nominal significance threshold were matched with salmon fry transcriptome data using blastn alignment. From these 109 transcripts, twelve, seven, and fifteen corresponding unique peptides were identified from human, mouse, and zebrafish database respectively using strict alignment criteria (E ≃ 0). Five genes were identified in all reference species, while ten, seven, and two genes were detected specifically in the zebrafish, human, and mouse databases respectively. Details including the associated gene name, putative chromosome in Atlantic salmon, gene ontology (GO), transcript id and gene id are given in Additional file RIA2; Table The single marker that surpassed the chromosome-wide significance level for length . Therefore, the GWAS results suggest that early growth in salmon is highly heritable but with a polygenic architecture and no evidence for major QTL. Based on previous linkage mapping and the current salmon reference genome assembly (AKGD00000000.4), the individual SNPs with the lowest P value for the growth traits were located on chr. 5, 16, 17, 20, 21 and 28. QTL associated with growth traits have been reported on the same chromosomes in some  previous studies in Atlantic salmon ., 15.p < mon , while Hickey et al. , where the AB represents the predicted phenotypic mean of the heterozygote. The proportion of genetic variance explained (PVE) by the SNP was estimated using the following equation:The SNP additive effect  was not included in the equation  or the genomic one (GBLUP). These predictions were compared in terms of their accuracy to predict an unknown phenotype. In order to do so, a five-fold cross validation analysis was performed using the individuals with genotype data and phenotypes in both traits.n ~ 425)] was used as the training population. The phenotype recorded in the validation population was then masked and breeding values were estimated using ASReml 3.0 assuming model (1). Accuracy was calculated as the correlation between the predicted EBVs of the validation set and the actual phenotypes divided by the square root of the heritability [~ r/h] using all individuals. The whole procedure was independently replicated five times and average accuracy values were calculated.The individuals were randomly divided into five non-overlapping subsets . One subset of data was then used as a validation set and the reminder of the data [four fifths  for GEBV calculation. Firstly, as part of a pipeline for designing a lower density SNP genotyping platform for routine genomic evaluations, a subset of ~ 33 K SNPs were selected from the ~ 132 K array as follows: (i) only polymorphic high resolution SNPs were retained as defined using Affymetrix software, (ii) only one SNP per genome contig in the salmon genome assembly was retained (NCBI Assembly GCA_000233375.1), (iii) removed one of every pair of SNPs with rP < 0.005 in model (1), ~ top 0.5 % of markers) were chosen to identify those located within or proximal to genes. Firstly, the flanking sequence of all the significant markers were aligned (using blastn) with an Atlantic salmon fry transcriptome database from RNA-seq of salmon fry in a separate study in which a large proportion of the SNPs on the array were discovered , mouse (Mus musculus), and zebrafish (Danio rerio) peptide reference database respectively , from which a stringent criterion of e-value ≃ 0 were used as evidence for homology. Secondly, for each unique peptide in each of the species, the corresponding gene id, associated gene name, chromosome position, and gene ontology (GO) were retrieved from ensembl biomart database  respectively. The corresponding chromosome of SNP markers were identified by aligning the marker and its flanking sequence with salmon reference genome sequence (AKGD00000000.4) and existing LG mapping [Based on the result of the GWA analysis, the SNPs surpassing the relaxed significance threshold . Only m mapping .


Wheat is grown as a staple crop worldwide. It is important to develop an effective genotyping tool for this cereal grain both to identify germplasm diversity and to protect the rights of breeders. Single-nucleotide polymorphism (SNP) genotyping provides a means for developing a practical, rapid, inexpensive and high-throughput assay. Here, we investigated SNPs as robust markers of genetic variation for typing wheat cultivars. We identified SNPs from an array of 9000 across a collection of 429 well-known wheat cultivars grown in China, of which 43 SNP markers with high minor allele frequency and variations discriminated the selected wheat varieties and their wild ancestors. This SNP-based barcode will allow for the rapid and precise identification of wheat germplasm resources and newly released varieties and will further assist in the wheat breeding program. DNA fingerprinting is commonly used in crops to identify and characterize cultivars and to protect the rights of breeders . TherefoTwo panels of materials were used in this study. Panel 1 was composed of 429 common wheat cultivars, including land races and modern varieties widely planted across the major wheat-growing areas of China from the 1930s to the 2010s. These cultivars confer important agronomical traits, such as resistance to diseases , tolerance to stresses , increased quality, larger grains, longer spikes, and dwarf height . In addiTriticum dicoccoides) and goat grass (Aegilops tauschii) (Panel 2 was composed of 193 bread wheat cultivars randomly selected from panel 1 and 96 pairs of wheat ancestor species including wild emmer wheat (auschii) . Panel 2DNA was isolated from the leaves of two-week-old seedlings using a DNA extraction kit . The DNA samples were genotyped using the Illumina wheat 9K Infinium Assay . SNP cluThe SNP allele frequency and polymorphism information content (PIC) were estimated for each locus using PowerMarker v3.25 . The PIChttp://cli.im/) to generate the 2D barcode for each accession. The genotype based on the SNP barcode was entered, and the 2D barcode was automatically generated. Once the barcode was scanned, the genotype of each accession was shown.We used online software Caoliaoerweima , and 12 μl of each tailed primer (100 μM). The total reaction volume was 5 μl in a 384-well plate, composed of 2.43 μl of V3 2× Kaspar mix, 0.07 μl primer mix, and 2.5 μl template (10–20 ng of genomic DNA) as described previously [TM Real-Time PCR software. As suggested by Trick et al. [The SNP-based barcode was converted to Kompetitive Allele Specific PCR (KASP) primers, which are specific for SNP genotyping technology . For each KASP SNP, two allele-specific primers and one common primer were designed . Parameteviously . Ten comk et al. , if the Of the SNP loci with three typical clusters of AA, AB and BB genotypes, only those loci of the classical bi-allelic type were used for further analysis. According to suggestions on data conversion , if raw original SNP list) were retained. To evaluate the resolution, genotypes based on the 50 most variable SNP loci were used to construct UPGMA trees for the 429 accessions. Among them, 364 cultivars could be distinguished from each other, accounting for 84.8% of the panel 1 accessions. However, the remaining 15.2% cultivars were not distinguished due to the close relationships among the accessions. For example, Yangmai5 and Funo could not be clearly separated because Funo was a parent of Yangmai5 (Nanda2419/Triumph//Funo///St1472/506) [Screening for the SNP barcode was conducted on panel 1 cultivars . First, 472/506) . FurtherUsing the UPGMA tree based on the original SNP list, to differentiate the closely related accessions, we identified the SNP loci with different alleles from the other 3439 SNP loci among these accessions. PIC values were calculated for these newly selected SNP markers based on panel 1 accessions. At least one SNP marker with a high PIC value was retained for each pair of closely related accessions. These newly screened SNP loci were sequentially added to replace the SNP markers in the original list. A new UPGMA tree was constructed after each adjustment to the SNP list. In determining the optimal minimum numbers of SNPs used as the SNP barcode, the highest resolution of SNP markers and their distribution across chromosomes were all taken into account. Finally, 43 SNP loci were obtained from the panel 1 accessions to form a SNP-based barcode for hexaploid wheat .2 = 0.1), though pairs of SNP markers on Chr1B and Chr3B were linked  ranged from 8% to 50%, with a mean MAF of approximately 37% across the 429 wheat cultivars. The genetic diversity (PIC) for the 43 SNP loci ranged from 0.14 to 0.38, with a mean PIC value of 0.34. Compared with the original polymorphic data (3489 SNPs), the 43 SNP markers selected showed increased diversity, by approximately 17%, for the panel 1 accessions  . Based oThe overall predictive accuracy of the 43-SNP barcode was 100% for the 429 wheat cultivars, distinguishing all the accessions distinctively , S1 Fig.Triticum aestivum L.), deriving from spontaneous hybridization of tetraploid Triticum dicoccoides  with diploid Aegilops tauschii . The 43-SNP-based barcode was investigated to evaluate its utility for distinguishing accessions with different ploidy. The results indicated that this barcode not only separated common wheat but also distinguished common wheat from its ancestors, as shown by the UPGMA tree  are the third-generation molecular marker. Their advantages, including high frequency across the whole genome, ease of detection and cost efficiency, make SNP markers particularly popular. With the development of next-generation sequencing (NGS) technology, substantial numbers of SNPs are being discovered, and high diagnostic SNP arrays have been developed for several major crops –11. NGS http://www.lgcgroup.com/services/genotyping) specific for detecting single-nucleotide variations, SNP markers can be converted to KASP probes. Thus, SNP detection no longer relies on array technology, and the number of SNP markers depends instead on the requirements of the set of samples for comparison. Using the KASP system, the cost for one SNP per sample is approximately $0.12. In fact, the number of KASP probes or samples per PCR is flexible according to the aims of the experiment. The procedure of PCR amplification is quite simple. Within one to two hours, the genotypes of 384 samples for one SNP marker or one sample for 384 SNP markers will be available. More importantly, KASP probes are labeled by two fluorophores, eliminating the uncertainty of the final data. Any detection systems for Real-time PCR could, in theory, be used for signal capture of KASP probes. Thus, SNP markers detected using the KASP system are less expensive and more efficient than SSR markers [As suggested, diagnostic SNP panels should be composed of the minimal number of SNPs required to differentiate all pairwise comparisons . For SNP markers .A SNP barcode provides a tool to discriminate very closely related accessions or the origins of wheat subgenomes. In the present study, the wheat accessions used for generating the SNP-based barcode are collections grown in China from the 1930s to the 2010s with important agricultural characteristics. These accessions are rich in variants including landraces and modern cultivars. The 43-SNP-based barcode could discriminate closely related accessions, such as the parent Funo and pedigree Yangmai5. In addition, this SNP barcode could differentiate between genome donors of wheat. For example, the origin of the A and B genomes of Gansu96 is the durum wheat Lumillo, which was detected as disparate from those of other hexaploid wheat cultivars.By 2006, more than 41,000 wheat accessions had been preserved in the Chinese GeneBank . From 2001 to 2015, 340 new varieties were registered according to the national assessment standard, in addition to the varieties released by provinces in China. Therefore, major concerns for wheat researchers and breeders include how to classify the wheat collections in this GeneBank and how to ensure the authenticity and purity of seeds. This 43-SNP barcode may be highly useful in the above work. However, there is still some room to improve this SNP barcode. SNPs of important genes controlling functional divergence as well as SNPs in intergenic regions can be used for designing SNP barcodes in the future.S1 Table(XLSX)Click here for additional data file.S2 Table(XLSX)Click here for additional data file.S3 Table(XLSX)Click here for additional data file.S4 Table(XLSX)Click here for additional data file.S1 FigEach line represents one SNP locus, and each column represents one accession. The SNP and cultivar information is listed in (TIF)Click here for additional data file.S2 FigThe black digital numbers are 193 elite cultivars, and the red ones are wild relatives.(TIF)Click here for additional data file.S3 FigThe corresponding accessions for digital numbers are listed in (TIF)Click here for additional data file.