Cicer accessions through integrated reference genome- and de novo-based GBS assays. High intra- and inter-specific polymorphic potential (66–85%) and broader natural allelic diversity (6–64%) detected by genome-wide SNPs among accessions signify their efficacy for monitoring introgression and transferring target trait-regulating genomic (gene) regions/allelic variants from wild to cultivated Cicer gene pools for genetic improvement. The population-specific assignment of wild Cicer accessions pertaining to the primary gene pool are more influenced by geographical origin/phenotypic characteristics than species/gene-pools of origination. The functional significance of allelic variants (non-synonymous and regulatory SNPs) scanned from transcription factors and stress-responsive genes in differentiating wild accessions  from cultivated desi and kabuli accessions, fine-mapping/map-based cloning of QTLs and determination of LD patterns across wild and cultivated gene-pools are suitably elucidated. The correlation between phenotypic  and molecular diversity-based admixed domestication patterns within six structured populations of wild and cultivated accessions via genome-wide SNPs was apparent. This suggests utility of whole genome SNPs as a potential resource for identifying naturally selected trait-regulating genomic targets/functional allelic variants adaptive to diverse agroclimatic regions for genetic enhancement of cultivated gene-pools.We identified 82489 high-quality genome-wide SNPs from 93 wild and cultivated  Furthermore, this process can be complemented with marker-based introgression of trait-associated novel genes, QTLs (quantitative trait loci) and natural allelic variants scanned from diverse wild gene pools into cultivated accessions.Chickpea  governing drought tolerance traits, particularly the root traits (58.2% PVE) mapped on an intra-specific genetic map (ICC 4958 × ICC 1882) of linkage group (LG) 4, was selected to evaluate the potential of our identified genome-wide GBS-based SNPs for fine mapping. A major genomic region (9102618–16180940 bp spanning 7.08 Mb on chromosome 4) harboring this robust QTL was defined by integrating that region with inter-specific genetic linkage (ICC 4958 × PI 489777) and physical map of the kabuli genome. The comprehensive GBS-based SNP analysis at this 7.08 Mb target QTL interval among 93 wild and cultivated Cicer accessions detected 375 SNPs. The structural annotation of these SNPs on the kabuli genome revealed the presence of 161 SNPs in the intergenic regions with 214, including 36 (29 genes) non-synonymous and 58 (31) regulatory, SNPs in the 60 genes. The functional annotation of 60 non-synonymous and regulatory SNP-carrying genes exhibited maximum correspondence to transcription factors (25%) and stress-responsive genes (33%). The differential expression analysis of 60 non-synonymous and regulatory SNP-containing genes based on in silico digital expression profiling showed pronounced differential up-/down-regulation (2 to 18.5-fold) of 51 genes in at least one of the tissues of ICC 4958 compared to others. The preferential and tissue-specific expression (≥4-fold) in the roots compared to the leaves of ICC 4958 was evident. The LD estimates and LD decay using 439 SNPs (with significant LD at MAF ≥0.05) identified and annotated on a 7.08 Mb drought responsive QTL region of kabuli chromosome 4 were determined. The LD estimates in the QTL region varied from 0.09 to 0.52 with a mean of 0.21, which is much lower than the LD estimates measured on all of kabuli chromosome 4 (mean LD: 0.39) using 2418 SNPs. The LD decay  determined at a 200 kb physical distance from the target QTL is much faster compared to all eight kabuli chromosomes, including kabuli chromosome 4 (500–550 kb).The structural and functional annotation as well as Cicer species depicted that a larger fraction (>90%) of such genes had Ka/Ks < 1.0, indicating negative/purifying selection pressure (62289C. arietinum [desi (0.37)/kabuli (0.40)] and C. judaicum, and lowest between C. arietinum [desi (0.14)/kabuli (0.18)] and C. microphyllum. The time of divergence estimation based on synonymous substitution rates (Ks) among conserved SNP-carrying genes between each of the wild and cultivated Cicer species revealed the highest Ks between C. arietinum (desi/kabuli) and C. judaicum, giving their approximate divergence time at 0.61 and 0.65 Mya, respectively (C. arietinum [desi (0.58 Mya)/kabuli (0.62 Mya)] and C. bijugum and by the minimum between C. arietinum [desi (0.35 Mya)/kabuli (0.38 Mya)] and C. microphyllum  is consistent with a number of previous studies62289The estimation of the proportionate distribution of Ka to Ks substitution rates across SNP-carrying genes conserved between each of the wild and cultivated pressure  and 5I. raction >% of suchectively  and 5I. ophyllum  and 5I. raction >% of suchCicer species from diverse geographical origins selected for genome-wide discovery and high-throughput SNP genotyping using GBS assay had considerable phenotypic diversity with regard to multiple agromorphological and disease resistance traits based on their multi-location replicated phenotyping at two diverse agro-climatic zones of North Western India by Singh et al.C. reticulatum accession ILWC 36 originating from Turkey (52 days), C. judaicum accessions (ILWC 4 and ILWC 273) from Lebanon (41 days) and C. pinnatifidum accessions (ILWC 226 and ILWC 251) also from Turkey (41 days) displayed multiple resistance against Ascochyta blight and root knot nematodes. One accession of C. judaicum (ILWC 256) originally from Jordan containing a higher number of pods/plant (284) vis-a-vis cultivated desi and kabuli Cicer species showed resistance to both Ascochyta blight and Botrytis gray mold. Therefore, large-scale non-synonymous and regulatory SNPs in the stress-responsive known/candidate genes and transcription factors differentiating the wild Cicer accessions  from sensitive cultivated desi and kabuli accessions scanned by us could potentially be utilized as functional markers for various large-scale genetic analyses in chickpea. The practical significance of these functional alleles (SNPs) mined at a genome-wide scale was apparent for fine-mapping/map-based cloning of genes underlying QTLs as well as for understanding natural allelic diversity and domestication pattern among diverse wild and cultivated Cicer species/accessions across gene pools based on their population genetic structure and LD patterning. These findings could help us to identify potential natural targets for adaptive trait genetic enhancement in cultivated gene pools that are domesticated in diverse agro-climatic regions. This would eventually accelerate genomics-assisted breeding, including hybridization breeding programs by introgressing diverse traits of agronomic importance from the wild Cicer gene pools into cultivated Cicer species for genetic improvement.The wild accessions constituting six annual and perennial Cicer accessions representing one annual cultivated C. arietinum (12 accessions), five annual wild species, namely C. reticulatum (16), C. echinospermum (8), C. judaicum (22), C. bijugum (19) and C. pinnatifidum (15) and one perennial species C. microphyllum were selected (C. reticulatum and C. judaicum) were used as biological replicates to evaluate the reproducibility of GBS-based SNPs. To constitute 96-plex GBS libraries, the genomic DNA of 95 accessions was digested with ApeKI and ligated to adapters carrying unique barcodes, while one sample (without genomic DNA) was considered as control. These libraries were pooled together and sequenced (100-bp single end) using Illumina HiSeq2000 adopting the methods of Elshire et al.et al.desi  to identify the high-quality and non-erroneous SNPs from 93 accessions.For genome-wide discovery and genotyping of SNPs, 93 diverse selected . The genospermum , C. judajudaicum , C. bijuculatum 1, C. echidesi and kabuli genomes and visualised using Circos visualisation tool. For structural and functional annotation of SNPs in diverse coding and non-coding sequence components of genes and genomes (chromosomes/pseudomolecules and scaffolds), the physical positions (bp) of reference-based SNPs were correlated with the GFF of desikabulihttp://snpeff.sourceforge.net), PFAM database v27.0 (http://pfam.sanger.ac.uk) and KOGnitor NCBI database (ftp://ftp.ncbi.nih.gov/pub/COG/KOG).To deduce the genomic distribution of GBS-based SNPs across chickpea chromosomes, the total SNPs, including non-synonymous SNPs were plotted individually based on their unique physical positions (bp) on eight chromosomes (pseudomolecules) of Cicer accessions was correlated with an available in silico SNP database of four chickpea accessions  based on their nature/types and congruent physical positions on the desi (ICC 4958) genomeCicer accessions. The PCR amplicons were sequenced and aligned to detect SNPs among accessions as per Kujur et al.80The SNP genotyping data obtained from 93 wild and cultivated Cicer accessions was analysed with PowerMarker v3.51, MEGA v5.0 and TASSEL v3.0 (http://www.maizegenetics.net). Based on these outcomes, the PIC (polymorphism information content), average pair-wise nucleotide diversity (θπ) and Watterson’s estimator of segregating sites (θω) were measured and an unrooted neighbour-joining (NJ)-based phylogenetic tree (with 1000 bootstrap replicates) among accessions was constructed. To determine optimal value of population number (K) and population structure among 93 accessions, the SNP genotyping information was analyzed in STRUCTURE v2.3.4 following Kujur et al.et al.The SNP genotyping information  generated from 93 wild and cultivated Cicer accessions for diverse yield contributing qualitative and quantitative traits  were obtained from Singh et al.et al.Cicer accessions were executed using NTSYS-pc V2.1 and POPGENE V1.32, and the methods of Zhang et al.The replicated multilocation  field phenotyping information of 93 wild and cultivated 2; average correlation coefficient among pairs of alleles across a pair of SNP loci) individually in desi and kabuli chickpea, the genotyping information of SNPs physically mapped on eight chromosomes were analysed using PLINK and the full-matrix approach of TASSEL, following Zhao et al.et al.2 against 50 kb uniform physical intervals across eight desi and kabuli chromosomes using aforementioned methods.To access the genome-wide and population-specific LD patterns (rCicer species from accessions of cultivated (desi and kabuli) species were functionally annotated. To determine the differential expression pattern of these SNPs-carrying genes, in silico digital expression profiling was employed. For this, the global transcript profiling data (whole genome transcriptome sequencing) available for selected informative non-synonymous and regulatory SNPs-carrying genes in different vegetative  and reproductive (flower bud and young pod) tissues of chickpea  were obtained. The differential expression profiling data assayed by SNPs-carrying genes in diverse vegetative and reproductive tissues of ICC 4958 was compared and a heat map was constructed using the TIGR MultiExperiment Viewer .To determine the functional significance of SNPs, the gene-derived SNPs (including non-synonymous coding and regulatory SNPs) differentiating accessions belonging to each of the wild kabuli genomein silico digital expression profiling and heat map construction were performed following aforesaid methods.The potential of GBS-based SNPs identified in our study for fine mapping/map-based cloning of QTLs was further assessed. For this, the genetic positions of markers linked/flanking the known QTLs regulating various yield component and stress tolerant traits mapped on eight LGs/chromosomes of diverse chickpea intra- and inter-specific genetic linkage maps were correlated and integrated with that of available high-resolution reference genetic linkage map (ICC 4958 × PI 489777)Cicer species were analyzed using the CODEML program in PAML interface tool of PAL2NAL . The time  of divergence among Cicer species was measured using a synonymous mutation rate of λ substitutions per synonymous site per year as T = Ks/2λ (λ = 6.5 × 10−9)For estimation of divergence time, the synonymous (Ks) and non-synonymous (Ka) substitution rates of the aligned amino acid sequences and their corresponding cDNA sequences of SNPs-carrying genes conserved across each of seven wild and cultivated How to cite this article: Bajaj, D. et al. Genome-wide high-throughput SNP discovery and genotyping for understanding natural  allelic diversity and domestication patterns in wild chickpea. Sci. Rep.5, 12468; doi: 10.1038/srep12468 (2015).


These potential susceptibility loci were then investigated for associations with fractional anisotropy (FA) images in a cohort consisting of 73 SZ patients and 87 healthy controls (HC). A significant correlation  was identified between one genetic factor and one FA component after controlling for scanning site, ethnicity, age, and sex. The identified FA-SNP association remained stable in a 10-fold validation. A 5000-run permutation test yielded a p-value of 2.00 × 10−4. The FA component reflected decreased white matter integrity in the forceps major for SZ patients. The SNP component was overrepresented in genes whose products are involved in corpus callosum morphology  as well as canonical pathways of synaptic long term depression and protein kinase A signaling. Taken together, our finding delineates a part of genetic architecture underlying SZ-related FA reduction, emphasizing the important role of genetic variants involved in neural development.It is becoming a consensus that white matter integrity is compromised in schizophrenia (SZ), however the underlying genetics remains elusive. Evidence suggests a polygenic basis of the disorder, which involves various genetic variants with modest individual effect sizes. In this work, we used a multivariate approach, parallel independent component analysis (P-ICA), to explore the genetic underpinnings of white matter abnormalities in SZ. A pre-filtering step was first applied to locate 6527 single nucleotide polymorphisms (SNPs) discriminating patients from controls with a nominal uncorrected  Schizophrenia (SZ) is a severe, debilitating mental disorder with heritability estimated up to ~80%  , a collaborative effort of four research teams from New Mexico (NM), Boston (MGH), Iowa (IOWA), and Minnesota (MINN) was involved in subject recruitment and data collection  Motion and eddy current correction: We registered all the images to a b =0 sec/mm2 image. Twelve degrees of freedom, affine transformation with mutual information cost function was used for image registration. (3) Adjusting the diffusion gradient direction: Two corrections were applied to the diffusion gradients. The nominal diffusion gradient directions were prescribed in the magnet axis frame. We rotated them to correspond to the image slice orientation. No correction was required if the imaging slice was pure axial. A second correction accounted for any image rotation during the previous motion and eddy current correction step. The rotation part of the transformation found previously was extracted, and each gradient direction vector corrected accordingly. All the image registration and transformations were done with the FLIRT (FMRIB's Linear Image Registration Tool) program . Dtifit, a tool in FSL, was adopted to calculate the diffusion tensor and the fractional anisotropy (FA) maps. The FA image was aligned to a MNI FA template with a non-linear registration algorithm FNIRT  and resliced via SPM resulting in a final voxel size of 2 × 2 × 2 mm. After preprocessing, the FA maps were smoothed using 8 mm smoothing kernel , (e) minor allele frequency (MAF > 0.05), and (f) missing calls were replaced using high linkage disequilibrium (LD) loci if available or otherwise removed. A total of 777,365 SNP loci were retained after quality control and discrete numbers were then assigned to the categorical genotypes: 0 , 1 , and 2 . With PCA, two principal components were identified as ethnicity-related and eliminated from the data. No clear population structure was observed in the corrected data.The genotyping and genetic quality control procedures were same as described in our previous work  inverse of A. Equation (3) illustrates the objective function of entropy and correlations computed between columns of loadings matrices A1 and A2. yf(Y) is the probability density function of Y; E is the expected value; H is the entropy function; W0 is the bias vector. W is then iteratively updated based on the natural gradient rule to optimize the objective function. A full description as well as the mathematical/simulation details of the algorithm can be found in our previous publications .P-ICA first extracts components from the two modalities in parallel based on infomax ICA  . The algorithm was configured with a threshold of 0.3 for constrained correlations to avoid false positive associations and to only constrain one pair of components. The endurance parameter was set to −1 × 10−5 to control the decreasing slope of the entropy term and avoid over fitting. We performed two independent P-ICA decompositions, one with the entire dataset  and the second using Caucasian participants only . We also performed association analysis between the FA/SNP loadings and the Positive and Negative Syndrome Scale (PANSS) symptoms as well as medication.P-ICA yields best performances when the ratio of sample size to the number of variables is above 0.02 for the genetic modality  and passing the Bonferroni threshold of 7.93 × 10−4, after controlling for age/sex and correcting for 63 independent tests. The FA-SNP pair replicated in all 10-fold validations having a correlation range of 0.18–0.46 and a median of 0.33. The FA and SNP components identified in subset decompositions in 10-fold cross-validation showed similar patterns to those identified with the entire dataset. The overlapping ratio refers to the percentage replication of the associated SNP component from the entire dataset with the associated SNP component in various subset decompositions. The overlapping ratio was around 50–70% in the various subset decompositions. Though variability existed in SNP component from entire dataset and subset decompositions, the genes/pathways obtained were very similar. Hence the final set of associated SNPs was selected from the results of entire dataset. In a 5000-run permutation test, the absolute values of FA-SNP top correlations ranged from 0.11 to 0.43 with a median of 0.19, yielding a p-value of 2.00 × 10−4 and passing the Bonferroni threshold of 7.93 × 10−4 for the identified association. We further examined the FA-SNP association within the SZ and HC groups, respectively, and observed a partial FA-SNP correlation of −0.1304 (p = 0.27) within the schizophrenia patients and −0.0019 (p = 0.98) within the healthy controls.P-ICA identified one FA-SNP pair components presenting a significant correlation , as shown in Figure Figure p = 1.62 × 10−32) as expected, since the SNPs for P-ICA were selected based on group difference. As in Figure www.snp-nexus.org), while the rest were from inter-genic regions. We also examined the genetic architecture of our finding using Ingenuity Pathway Analysis (IPA: https://www.ingenuity.com) where the 90 genes were compared with the whole genome as background. IPA identified enrichment in abnormal morphology of corpus callosum with gene hits in CNTNAP2, NFIB, and NPAS3 (p = 2.78 × 10−4), as illustrated in Table p = 8.71 × 10−4) and protein kinase A (PKA) signaling (p = 1.02 × 10−3), as shown in Table The linked SNP component depicted in Figure We also analyzed the FA-SNP pair obtained from the independent secondary P-ICA decomposition , observing the FA component covered the same region of forcep majors as obtained with the entire dataset. The overlapping ratio of snps between SNP components  was around 30%. Our previous studies have shown that this overlap with such small sample size is much higher than random , also known as splenium. These fibers interconnect bilateral parietal and occipital , as well as temporal cortices    beta whose activity is shown to modulate myelin gene expression in enriched oligodendrocytes . GRID2 is a glutamate receptor known for its role in synapse formation and LTD induction in cerebellum  highlighted in the SNP component. While the SZ neuropathology hypothesis based on altered brain connectivity diverges in whether the dysconnection manifests anatomically  or functionally (through aberrant control of synaptic plasticity), our observation suggested that these two factors might coexist  in the regression model and did not observe any significant effect on the FA loadings. Exploring further we found that the top paired SNP component from P-ICA was found to be the second most significant component showing group difference, replicating reliably in all subset evaluations. This could indicate the uniqueness in FA-SNP component association other than the shared group difference, bringing forth the fact that FA changes are likely due to genetic factors. However, other factors cannot be completely ruled out in this work. Though premature, we might speculate that the FA-SNP relationship is limited by the range of symptom severity measures. The range for PANSS positive and negative symptoms is 0–49 (Opler et al., In conclusion, we consider this work one step further toward understanding the genetic underpinnings of white matter abnormalities in SZ, in part as it assessed multiple genetic variants for aggregate effects. More importantly, we revealed that this local FA reduction was associated with a genetic factor enriched in CC morphology, LTD and PKA signaling. The involved genes appear to converge on developmental functions, indicating a possibility that genetic variants contribute to white matter abnormalities through disrupting neural development. The fact that these genes also participate in synaptic plasticity leads to the speculation of functional dysconnectivity, which will be investigated in a future study.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Solanum lycopersicum variety Heinz 1706 and S. pimpinellifolium accession LA 1589 using MUGSY software identified 145 695 insertion–deletion (InDel) polymorphisms. A selected set of 3029 candidate InDels (≥2 bp) across the entire tomato genome were subjected to PCR validation, and 82.4% could be verified. Of 2272 polymorphic InDels between LA 1589 and Heinz 1706, 61.6, 45.2, and 31.6% were polymorphic in 8 accessions of S. pimpinellifolium, 4 accessions of S. lycopersicum var. cerasiforme, and 10 varieties of S. lycopersicum, respectively. Genetic distance was 0.216 in S. pimpinellifolium, 0.202 in S. lycopersicum var. cerasiforme, and 0.108 in S. lycopersicum. The data suggested a reduction of genetic variation from S. pimpinellifolium to S. lycopersicum var. cerasiforme and S. lycopersicum. Cluster analysis showed that the 8 accessions of S. pimpinellifolium were in one group, whereas 4 accessions of S. lycopersicum var. cerasiforme and 10 varieties of S. lycopersicum were in the same group.Comparative analysis of the genome sequences of  Genetic marker development for tomato has been conducted over 30 years through various approaches, including restriction fragment length polymorphism (RFLP), random ampliﬁed polymorphic DNA (RAPD), ampliﬁed fragment length polymorphisms (AFLPs), simple sequence repeat (SSR), cleaved ampliﬁed polymorphisms (CAPs), and conserved ortholog sets (COSs). Most markers developed by these approaches are based on DNA or cDNA polymorphisms between wild species and cultivated tomato, which lead to the construction of the first generation reference linkage maps and isolation of genes of interests.2 However, the ability of using these markers to detect polymorphisms in cultivated tomato is limited.3 Recent efforts to develop new markers in cultivated tomato have been focus on single-nucleotide polymorphisms (SNPs) using in silico mining of expressed sequence tag database and experimental validation,7 amplicon sequencing of COS genes,9 hybridization to oligonucleotide array,10 and next-generation sequencing of transcriptome or re-sequencing of genome.13 Owing to the abundance and wide distribution of SNPs in the whole genome and the availability of automatic large-scale genotyping platform, SNPs have widely been used in association analysis,15 high-density SNP map construction,16 as well as population structure and genetic variation analysis20 in cultivated tomato.Tomato  polymorphisms are increasingly being received attention in human because they are the second abundant form of genetic variation and can influence multiple human phenotypes including diseases.in silico. We here developed a pipeline to identify InDels by comparative analysis of the two available genome sequences of LA 1589 and Heinz 1706. A total of 3029 candidate InDels were subjected to experimental validation by PCR amplification of genomic DNA in a collection of 22 tomato lines. The main objective of this study was to develop easy-using markers for genetic study and marker-assisted selection in cultivated tomato.The availability of the whole genome or transcriptome sequences provides a potential to identify InDels 2.2.1.S. lycopersicum) and its wild relatives were used to validate InDel polymorphisms. These inbred lines were selected to represent a diverse collection including eight accessions of S. pimpinellifolium, five processing varieties, one greenhouse cultivar, four fresh market cultivars, and four S. lycopersicum var. cerasiforme accessions (Table 9 The eight S. pimpinellifolium accessions were selected from the core collection or sources being used for genetic studies and were used to detect polymorphisms of candidate InDels within the species. Genomic DNA was isolated from fresh-collected young leaves of at least eight plants for each genotype using the modified CTAB method.36A panel of 22 tomato genotypes comprising of cultivated tomato (ns Table . Nine of2.2.S. pimpinellifolium accession LA 1589 (Spimpinellifolium_genome.contigs.fasta.gz) and S. lycopersicum variety Heinz 1706 (S_lycopersicum_chromosomes.2.40.fa.gz) were downloaded to a local computer from the SOL Genomics Network . The genomic DNA sequence contigs of LA 1589 were assigned to Heinz 1706 genome using local MUGSY37 downloaded from Sourceforge . InDel polymorphisms referring to Heinz 1706 were mined from the alignments using custom PERL scripts. Flanking sequences of 100 bp from each side of candidate InDels were extracted from Heinz 1706 sequences for insertion and LA 1589 sequences for deletion. The flanking sequences were then blasted against LA 1589 sequences for deletion or Heinz 1706 sequences for insertion using local BLASTall with an E-value of e−20 to remove hits with low similarity. The types (insertion or deletion), lengths, nucleotides, and chromosomal positions of InDels were extracted using a PERL script with the highest score of blast search.The genomic DNA sequences of 2.3.34 the average distance between two adjacent InDels would be ∼250 kb. The number of InDels to be validated was determined by the length of each chromosome ,38 downloaded from Sourceforge  with PCR product length 100–200 bp and the optimal length of primer sequence of 20 bp. Several primer pairs were designed for each InDel. The best primer pair was selected based on the optimal GC content of 40–60% and the difference of GC content between forward and reverse primers <10%. All the process was carried out using custom PERL scripts. Primers were synthesized at Sunbiotech Company  or Sangong Company .To design primers for PCR validation of InDels, flanking sequences of 100 bp for each side of candidate InDels were extracted. Primers were designed using local Primer32.4.The PCR technique was adapted to validate the candidate InDels. All synthesized primers were first used to amplify genomic DNA of tomato lines LA 1589 and Heinz 1706. Only primers that successfully amplified a product and had length polymorphisms were then used to detect polymorphisms in the 22 tomato genotypes.et al.39 Reactions were heated at 95°C for 5 min, followed by 32 cycles of 30 s at 95°C, 30 s at 50–60°C depending on the Tm values of primer pairs, and 30 s at 72°C, with a final extension of 5 min at 72°C. The PCR products were subsequently separated in 8% polyacrylamide gel and visualized using the silver-staining approach.17All PCRs were done in 10-μl reaction volume using the method described in Wei 2.5.40 was calculated for each pair of tomato genotypes using the programme in the software package PHYLIP 3.695 . An Unweighted Pair Group Method with Arithmetic Mean (UPGMA) cluster analysis was performed to develop a dendrogram.The presence or absence of each allele for each InDel was coded by 1 or 0, respectively, and scored for a binary data matrix. Allele frequency of each InDel marker was calculated for each genotype. Nei's genetic distanceThe occurrences of InDels in coding regions of genes were examined by blasting the flanking sequences of 100 bp for each side of the InDel against the tomato ITAG2.3_cds.fasta downloaded from SGN using a PERL script.3.3.1.A total of 145 695 candidate InDels were identified between the genome sequences of Heinz 1706 and LA 1589, of which 65 619 were insertions and 80 076 were deletions in Heinz 1706 Table . The ave3.2.Supplementary Table S1). The average physical distance between two adjacent InDels was 250 kb with a range of 241 (chromosome 2) to 255 kb (chromosome 3) on 12 chromosomes. PCR results showed that 272 primer pairs could not generate PCR products from the genomic DNA of both Heinz 1706 and LA 1589 (Table 9 The InDel sizes of PCR products amplified by most primer pairs (98.5%) were as predicted. However, 23 primer pairs showed smaller and 10 primer pairs showed larger sizes than predicted (Supplementary Table S1). In addition, 485 primer pairs did not show detectable polymorphisms between Heinz 1706 and LA 1589 (Table Supplementary Table S2). The primer pairs with PCR failure or non-detectable polymorphisms were excluded, and the remaining 2272 primer pairs were used for subsequent analysis. Therefore, the actual average distance between two adjacent InDels was 334 kb with a range of 285 (chromosome 2) to 430 kb (chromosome 1) on 12 chromosomes.Using the approach described in the section ‘Selection of InDels for validation and primer design’ of Materials and methods, 3029 candidate InDels were selected and primers were designed for PCR validation  had two alleles, 10.7% had three alleles, and 2.7% had four alleles  between any accession in S. pimpinellifolium and any line in S. lycopersicum var. cerasiforme, and 7.7–33.9% (average 19.2%) between any line in S. lycopersicum var. cerasiforme and any genotype in S. lycopersicum (Supplementary Table S3).Pairwise comparisons revealed that almost all InDel markers were polymorphic between Supplementary Fig. S1), the distribution of polymorphic markers varied for three species . Solanum pimpinellifolium had a relatively even distribution of polymorphic InDels on all 12 chromosomes. Solanum lycopersicum var. cerasiforme had the similar distribution pattern of polymorphic InDels as S. pimpinellifolium on chromosomes 2, 3, 4, 5, 6, 9, 10, and 11, but clusters of polymorphic InDels occurred at some regions on chromosomes 1, 7, and 12. The distribution of polymorphic InDels varied across and within chromosomes in S. lycopersicum. Among six chromosomes with less polymorphic InDels, chromosomes 1, 8, 10, and 12 had relatively even distribution, while the long-arm ends of chromosomes 3 and 7 had more InDels than other regions. There were less InDels at one end of chromosomes 2, 4, 5, 9, and 11. However, chromosomes 5, 9, and 11 showed relatively even distribution. On chromosome 6, the short arm had more polymorphic InDels than the long arm.Although the 2272 InDels almost evenly distributed across all 12 chromosomes .The proportion of polymorphic InDels was 61.6% in 8 S. pimpinellifolium had the largest genetic variation among three species. The average genetic distance was 0.216 with a range from 0.178 (PI 128216) to 0.244 (LA1589). Accessions LA 1589 and LA 2181 had the greatest genetic distance with 0.394, whereas accessions PI 128216 and LA 0373 had the least genetic distance with 0.137. The average genetic distance slightly reduced to 0.202 with a range from 0.162 (LA 4133) to 0.237 (PI 114490) in four S. lycopersicum var. cerasiforme lines, but significantly decreased to 0.108 with a range of 0.086 (Baiguoqiangfeng) to 0.139 (M 82) in 10 varieties of S. lycopersicum. The minimum genetic distance was 0.012 between varieties Liger 87-5 and M 82, followed by 0.015 between varieties Baiguoqiangfeng and Zhongshu 5, while the largest genetic distance was 0.214 between Shijifeng and M 82.Not surprisingly, the eight accessions of S. pimpinellifolium were in Group A, and 10 S. lycopersicum var. cerasiforme cultivars and 4 S. lycopersicum var. cerasiforme accessions were in Group B. The four fresh market cultivars clustered together. However, five processing varieties, one greenhouse variety, and four S. lycopersicum var. cerasiforme accessions did not form their own clades. Of the four S. lycopersicum var. cerasiforme lines, LA 4133 clustered to three processing and one greenhouse varieties, Black cherry clustered to two processing varieties, while PI 114490 and LA 1310 stood alone.The dendrogram was constructed from the pairwise genetic distance matrices based on Nei's distance for 22 genotypes. Two distinct groups, A and B, were obtained Fig. . All 8 a3.5.Supplementary Table S4), of which 64.3% were deletions in Heinz 1706 and 35.7% were insertion in Heinz 1706. Based on the sizes of InDels, 28.6% of InDels were frame-shift mutations, because the numbers of nucleotides in the InDels were indivisible by three. The remaining 71.4% InDels did not result in frame-shift, but would cause insertion or deletion of some amino acids.Blast search of flanking sequences of 2272 validated InDels against the tomato ITAG2.3_cds.fasta data identified 56 InDels in coding regions of annotated genes  between LA 1589 and Heinz 1706, we estimated that there were 2100–28 800 InDels between any two cultivated tomato varieties, suggesting that there were abundant InDels for genetic study and marker-assisted selection in the cultivated tomato.Molecular markers are important to genetic study and marker-assisted selection. Large-scale discovery combining high-throughput genotyping of SNPs have shown its power in gene identification and breeding in tomato.50 and an InDel error rate of one per 6.4 kb was observed in tomato,34 the initial work on identification of InDels between the genomes of LA 1589 and Heinz 1706 did not count InDels of 1 and 2 bp to avoid overestimation of small InDels due to sequencing errors.34 Using a bioinformatic pipeline involving various comparative genomics tools, 9474 InDels of 15–100 bp were identified between LA 1589 and Heinz 1706, and >80% could be verified by PCR . In this study, a total of 145 695 InDels were predicted between LA 1589 and Heinz 1796, which was approximate one-fifth of 749 966 InDels Identified in Sato et al.34 The overall frequency of InDels (one per 5.22 kb) was also much lower than one per 110 bp in Sato et al.34 However, the number (9137) of InDels of 15–94 bp was close to the results of Jiang et al., though the strategies used for InDels identification were different. Owing to the lack of methodology description in Sato et al.,34 we were not able to determine the cause of the difference between two studies. Two points might be worthy of notice. First, the lengths of putative InDels identified in two studies were different with ranges of 3–300 bp in Sato et al.34 and 1–94 bp in this study. We could not identify any InDels >94 bp using our methodology. Secondly, the rate of validation (82.4%) was close to 81.7% obtained in Koenig et al.,35 though the comparisons involved in different wild species and cultivated varieties, indicating that ∼20% of predicted InDels (≥2 bp) were false due to sequencing error. All these suggested that our prediction might be more close to the real number of InDels in the currently available genome sequences of LA 1589 and Heinz 1706.Precise identification of InDels in sequence databases depends on the strategy and the parameters used for data mining as well as the quality of sequence data. Since InDels are the dominant error type generated by 454 pyrosequencingS. pimpinellifolium, but appeared non-randomly distributed across and within chromosomes in S. lycopersicum var. cerasiforme and S. lycopersicum. Domestication and selection could be one causal of this difference. For example, there were 38 and 35 polymorphic InDels at the bottom (∼11 Mb) of chromosome 2 in S. pimpinellifolium and S. lycopersicum var. cerasiforme, respectively, but only two InDels were polymorphic in S. lycopersicum. This might be due to the existence of quantitative trait loci for fruit weight and selection for large fruit in S. lycopersicum.12 In addition, several studies have proved that the introgression of disease resistance genes in many cultivars has strong influence on SNP patterns.51 This kind of introgression could also cause the difference of polymorphic InDels distribution among three species.The polymorphic InDels evenly distributed across all 12 chromosomes in 52 and modern cultivars have less genetic variation than old ones in tomato.54 In this study, genetic variation of three species was investigated using the same large set of InDel markers, which allowed us to compare genetic polymorphisms among and within species at the same time. The number of polymorphic InDels, the total number of alleles amplified by InDel markers, and the average genetic distance in 10 S. lycopersicum varieties significantly reduced comparing with those in 8 S. pimpinellifolium accessions, supported the reduction of genetic variation in cultivated tomato. The four S. lycopersicum var. cerasiforme accessions showed an intermediate amount of genetic diversity between S. lycopersicum and S. pimpinellifolium, which was consistent with previous findings.56 However, some novel alleles occurred in both S. lycopersicum var. cerasiforme and S. lycopersicum, suggesting that domestication and selection could also generate new variation.It has been suggested that domestication and inbreeding dramatically reduced the genetic variation57 A Rider mutational insertion event occurring in the first exon of the Psy1 gene causes the early termination of Psy1 transcription that results in yellow flesh in the tomato r mutant.58 A single-base deletion mutation in the coding region of SlIAA9 gene, an Aux/IAA gene involving in tomato leaf morphology, converts tomato compound leaves to simple leaves.59 InDels occurring in the promoter region can also affect the gene expression.60 Here, we identified 145 695 InDels between LA 1589 and Heinz 1706, and 31.6% of them were polymorphic in cultivated tomatoes. The percentage of InDels (2.5%) occurring in coding regions of genes identified in this study was much lower than our recent work (19.7%) on comparative analysis of resistance-like genes between LA 1589 and Heinz 1706.61 Identification of specific genes in our previous work other than a random sample in this study could cause the different proportions of InDels in coding regions.The occurrence of InDels in coding regions of a gene can either cause frame-shift or amino acid InDels, which most likely alternates the gene function and results in phenotype change.In conclusion, there are abundant short InDels in cultivated tomato. Identification and validation of this kind of short InDels will not only provide molecular markers for genetic study and marker-assisted selection in breeding, but also provide useful information for gene cloning and functional analysis.Supplementary data are available at www.dnaresearch.oxfordjournals.org.National Program on Key Basic Research Projects (The 973 Program: 2012CB113900) and the National Natural Science Foundation of China (31171973).The work was partially supported by the


CYP2D6 gene is challenging to genotype due to the highly complex nature of its gene locus. TaqMan® technology is widely used in the clinical and research settings for genotype analysis due to assay reliability, low cost, and the availability of commercially available assays. The assay identifying 1023C>T (rs28371706) defining a reduced function (CYP2D6*17) and several nonfunctional alleles, produced a small number of unexpected diplotype calls in three independent sets of samples, i.e. calls suggested the presence of a CYP2D6*4 subvariant containing 1023C>T. Gene resequencing did not reveal any unknown SNPs in the primer or probe binding sites in any of the samples, but all affected samples featured a trio of SNPs on their CYP2D6*4 allele between one of the PCR primer and probe binding sites. While the phenomenon was ultimately overcome by an alternate assay utilizing a PCR primer excluding the SNP trio, the mechanism causing this phenomenon remains elusive. This rare and unexpected event underscores the importance of assay validation in samples representing a variety of genotypes, but also vigilance of assay performance in highly polymorphic genes such as CYP2D6.CYP2D6 contributes to the metabolism of many clinically used drugs and is increasingly tested to individualize drug therapy. The  CYP2D6 genotype testing is increasingly utilized to predict a subject's metabolizer status to individualize drug choice and dosing, especially in psychiatry689CYP2D6 pharmacogenetics is also highlighted by Clinical Pharmacogenetics Implementation Consortium (CPIC) guidelines for tricyclic antidepressantsCytochrome P450 2D6 (CYP2D6) is one of the most scrutinized phase I drug metabolizing enzymes due to its involvement in the metabolism and bioactivation of 20 to 25% of clinically used drugs. Among the long list of drugs and substrates are many antidepressants and antipsychotics, pain medications such as codeine and tramadol, the estrogen receptor antagonist tamoxifen, and also drugs of abuse124CYP2D locus and the highly polymorphic nature of the CYP2D6 gene itself, CYP2D6 genotype analysis not a trivial undertaking. Two recent reviews highlight the complexity of CYP2D6 gene analysis, interpretation of results, and the challenges of predicting phenotype from genotype data13i.e. what sequence variations are present and which are not, as all subsequent interpretations  are based on the test result.Due to the complexity of the human CYP2D6 pharmacogenetic (PGx) testing61315CYP2D6 gene variants is challenging due to its highly polymorphic nature and homology to pseudogenes. Assays are designed using an algorithm pipeline that includes masking of non-target SNPs in input SNV context sequences and in silico QC of assay designs to avoid underlying polymorphisms and to ensure high target specificityAt present, there are numerous commercial platforms, methods, and assays available for CYP2D6*4-defining SNP 1846G>A and homozygous calls for the CYP2D6*17-defining SNP rs28371706 (1023C>T) when genotyped with TaqMan assays. Allele nomenclature in this report is according to the Human Cytochrome P450 (CYP) Allele Nomenclature DatabaseCYP2D6*17, a reduced function allele which is commonly detected in subjects of sub-Saharan origin. This SNP, however, is also part of the haplotype of the non-functional allelic variants CP2D6*40 and *58 as well as CYP2D6*64 and *82, which have been tentatively assigned reduced or non-functional status. For simplicity, we will refer to the TaqMan assay detecting the 1023C>T SNP as the ‘CYP2D6*17 TaqMan assay’.Several DNA samples with an unusual and unexpected diplotype were independently observed at three academic centers in the US and Canada. These samples all presented with heterozygous calls for the CYP2D6*4 as no allele(s) have been described that contain both 1023C>T and 1846G>A as implied by the unexpected TaqMan assay results. Subsequent gene resequencing did not reveal the presence of any known or novel SNVs in the TaqMan primer or probe regions. Observation of this phenomenon by several independent groups in samples from multiple studies implied that the underlying genetic context may be relatively common, and warranted further investigation.To date, however, 1023C>T has not been reported as a subvariant of CYP2D6*4 allele in the 1023C>T CYP2D6*17 TaqMan assay. Understanding the underlying causes leading to this phenomenon is crucial in solving the conundrum at hand, but are also invaluable for explaining similar unpublished observations by other investigators for this or other TaqMan assays and for primer-based assay designs at large, and ultimately for accurate phenotype assignment in a clinical setting.As a common cause of unexpected homozygosity is mono-allelic amplification, the goal of this study was to characterize the apparent ‘drop out’ of the CYP2D6 genotype. Nineteen of the cases tested homozygous for the 1023C>T SNP, which was inconsistent with known haplotypes. DNA quality and/or concentration issues were ruled out as the samples in question amplified equally well compared to all other samples within a run. Contamination issues were initially considered and DNA re-isolated from the first cases observed at CMH; this, however, did not resolve the issue. As additional cases with the same miscall pattern were identified and this phenomenon was also observed in samples genotyped by independent laboratories, contamination was no longer deemed to be a likely explanation for the inconsistent genotype calls.The cases described in this report were genotyped for a number of sequence variations with TaqMan assays to determine CYP2D6*17 allele (defined by the presence of the SNP at position 1023) and a CYP2D6*4 allele (defined by the presence of SNPs at positions 100 and 1846). Cases 1–18 presented with inconsistent (i.e. homozygous) calls for 1023C>T; these subjects were eventually found to carry a CYP2D6*4 subvariant with the SNP trio. Cases 19 and 20 had consistent calls for 1023C>T; both subjects revealed the CYP2D6*4D subvariant which does not possess the SNP trio. Cases 21–25 carried a CYP2D6*4 gene duplication; four were typed heterozygous for 1023C>T similar to cases 19 and 20, while one was homozygous. An overview of CYP2D6*4 haplotypes (subvariants) is provided in Twenty-five subjects including the 19 cases with inconsistent results for the 1023C>T SNP were selected for further investigation and were grouped as following: Each individual possessed a CYP2D6 genotyping performed at three different institutions identified 16 subjects of predominantly African ancestry (CYP2D6/2D7 or 2D7/2D6) based on quantitative CNV analysis that could conceivably interfere with the assay result in question. This suggested that these subjects have a CYP2D6*4/*17 genotype with a novel CYP2D6*4 subvariant that carries the 1023C>T SNP. When using a CMH custom-made CYP2D6*17 TaqMan assay these subjects also genotyped homozygous for 1023T/T  or partial (cases 2–11) gene resequencing. Sequencing was performed on PCR products representing both alleles; no novel SNPs that could conceivably interfere with the TaqMan assay were found. It was noted however, that all subjects had three SNPs  that are commonly found on SNP trio .CYP2D6*4/*17 genotype. In contrast to cases 1–18, there were no conflicting genotyping results  was defined in cases 21–23 by genotyping a fragment that was specifically generated from the duplicated gene (fragment D). The duplication was not further specified in cases 24 and 25. As shown in These five cases were positive for a gene duplication event. A duplication on the CYP2D6*17 TaqMan assay by preventing amplification and accurate detection of 1023C (wild-type) on CYP2D6*4 when this allele was paired with CYP2D6*17. As a result, fluorescence was only produced from the 1023T-containing CYP2D6*17 allele mimicking a homozygous signal. To unequivocally determine the CYP2D6*4 haplotype for the exon 2 region in which these SNPs reside, allele-specific long-range PCR and sequencing was performed on CMH cases 1–9. Cases 1–3 and 5–9 indeed carried all three SNPs on their CYP2D6*4 allele; sequence analysis for cases 10–13 and 19 was performed on diploid template and identified the presence of the three SNPs and were tentatively assigned to the CYP2D6*4 allele. In contrast, these SNPs were absent in CMH cases 21–23. However, another SNP, 1039C>T, was detected in the latter cases by gene resequencing which allowed us to subtype their CYP2D6*4 allele as CYP2D6*4D. Subjects for which no information was available for 1039C>T, but genotyped correctly for 1023C>T (cases 20 and 24) were tentatively assigned as having a CYP2D6*4D allele.The preliminary sequencing results described above for cases 1–11 and 19 suggested that the presence of 974C>A, 984A>G and/or 997C>G interfered with the CYP2D6*4 alleles of cases 1 and 21 were completely sequenced. As shown in CYP2D6*4A and *4D, respectively, not considering intronic SNPs. To the best of the authors' knowledge, the majority of CYP2D6*4 subvariant definitions are based on exon and exon/intron junction sequences only; hence many do not have any annotations for SNVs located in introns or the 3′-and 5′-flanking regions.The CYP2D6*4 gene of cases 21–23 also determined absence of the 974C>A, 984A>G and 997C>G SNP; cases 24 and 25 were not available for sequence analysis.Partial resequencing of the duplicated CYP2D6*17 conundrum, we engaged in a collaboration with Thermo Fisher. The PCR primers of the CYP2D6*17 assay ID C___2222771_40 are located upstream of the 974C>A, 984A>G and 997C>G SNP trio and the fluorescent probes bind downstream of 997C>G, which is similar in design compared to the custom-made assay developed at CMH (CYP2D6*17 assay design (C___222771_A0), which differed from C___2222771_40 in the position of just the upstream primer, which was located between the 997C>G and target 1023C>T SNP. It had been observed at Thermo Fisher that the alternate assay genotyped two Coriell samples  as heterozygous for the CYP2D6*17 SNP (1023C/T) whereas the original assay genotyped these as homozygous for CYP2D6*17 (1023T/T); the two samples were also genotyped as heterozygous for CYP2D6*4. This phenomenon was not understood as there were no known SNPs within the primer or probe binding sites and sequence analysis had not revealed novel underlying SNPs. Similar to the list of mis-genotyped samples accumulated at the three study sites, sequencing revealed that these samples were indeed heterozygous for CYP2D6*17 and carried the 974C>A, 984A>G and 997C>G SNP trio. As shown in In order to resolve the d at CMH . Thermo CYP2D6*4/*4 genotype (SNP-trio positive) did amplify with the original assay and were accurately called by the TaqMan® Genotyper™ Software. Upon manual interrogation of amplification plots, it appears that there is a trend towards lower efficiency of amplification when comparing CYP2D6*4/*4 with CYP2D6*1/*1 samples. The trend towards lower amplification, is, however, still within the variability seen within a typical sample panel. Therefore, the drop-out phenomenon did not interfere with amplification to an extent that would prevent allele calling.As shown in CYP2D6*17-derived PCR product which was lower compared to those calculated for CYP2D6*4 with the SNP trio  and CYP2D6*4D (five structures) ranging from 68.5°C to 70.4°C.An analysis of the secondary structure of the TaqMan assay PCR products using mfold determined seven possible structures with melting temperatures ranging from 64.1°C to 67.7°C for a CYP2D6*17) and heterozygous for 1846G>A (CYP2D6*4) by TaqMan genotyping. Since this SNP pattern was inconsistent with the definition of known allelic variants, i.e. an allele that carries 1023T and 1846A, a genotype assignment could not be made with confidence. We initially suspected that a novel SNP interfered with the TaqMan assay and prevented the generation of a fluorescent signal from the second allele, specifically when the second allele was a CYP2D6*4. CYP2D6*4 drop-out would trigger the CYP2D6*17-derived signal to appear homozygous. A drop-out event was further supported by hetero-zygous results for 1023C>T by RFLP analysis using an assay that employs PCR primers located more distant from the SNP and having different binding sites than the commercial and CMH-designed TaqMan assays, respectively. However, the presence of a novel sequence variation could not be substantiated by sequence analysis of the gene region of interest, although we confirmed heterozygosity for 1023C>T and found a trio of three SNPs that is commonly found in CYP2D6*4 haplotypesCYP2D6*4. The four cases available for sequencing all lacked the SNP trio upstream of 1023C>T turning our attention to these SNPs as potential culprits.A growing number of subjects were observed over time which consistently presented homozygous for 1023C>T  remains elusive. The first of these SNPs (974C>A) is about 20 bp downstream of the 3′ end of the forward PCR primers of the commercial and custom-made assays and should therefore not impact amplification efficiency. Also, subjects homozygous for CYP2D6*4 (SNP trio positive) are relatively common  and do amplify and genotype correctly with the original assay  and CYP2D6*17. CYP2D6*4 allele drop-out would not be apparent in subjects with, for example, CYP2D6*1/*4, *2/*4 or *4/*10 and other *4/non-*17 genotypes, because CYP2D6*4 and non-*17 alleles are all wild-type for 1023C>T and thus genotype “accurately”.The mechanism by which the SNP trio found on the al assay . This suCYP2D6*4 and *17 allelic variants in African and African American populationsCYP2D6*4/*17 genotype, which is consistent with the number of subjects we have observed in our study populations. This phenomenon was likely observed by others as well, for instance Friedrich et al.CYP2D6*4/*17 genotypes with the redesigned assay moving forward.Based on the frequencies of the CYP2D6, 2D7 and 2D8. TaqMan Drug Metabolism Genotyping Assays undergo stringent bioinformatic and wet lab testing quality control before being commercialized by Thermo Fisher. As well, concordance experiments with samples geno-typed by other technologies have demonstrated their overall high accuracy and reproducibilityThe occurrence of the allele amplification drop-out we describe in this report was unexpected. Interactions between SNP(s) that are not within or next to primer binding sites are generally not considered to be of concern when designing TaqMan or other PCR-based genotyping assays. Often, especially when working with highly polymorphic genes such as the cytochrome P450s, choices for primer and/or probe locations are rather limited and restricted to certain areas as presented in CYP2D6*4 alleles. To that end, we tested amplification reagents from other suppliers; however, none was able to overcome the allele-selective amplification problem. The SNP trio could conceivably also facilitate secondary structures which melt at a higher temperature and are therefore amplifying less efficiently compared to alleles lacking these SNPs. Analysis with mfold determined that the PCR product generated from the CYP2D6*17 allele have lower Tm values for tentative secondary structures compared to CYP2D6*4 with the SNP trio. However, secondary structures of the PCR fragment from CYP2D6*4D (without the trio) had the highest Tm values. Should secondary structures indeed be causative further modeling and experimentation is needed to define the mechanism of the CYP2D6*4 subtype allele drop-out phenomenon.The determination of the mechanism behind the allele amplification drop-out could enable avoiding primer design to regions that may be susceptible to this phenomenon. We speculated that Taq polymerase properties may contribute to the preferred amplification of one allele in the presence of the SNP trio that is found on the majority of All methods and procedures were carried out in accordance with approved guidelines.CYP2D6 genotyping was performed were approved by the Institutional Review Boards of the Children's Mercy Hospital (CMH), Kansas City, MO, USA (n = 2), Mount Sinai School of Medicine, NY, NY, USA and the University of Illinois at Chicago (UIC), Chicago, IL, USA (n = 8), the University of California, San Diego, CA, USA (n = 2), the Ethics Committee of the Faculty of Medical Sciences, University of the West Indies, Trinidad and Tobago (n = 5), and the Research Ethics Board at the Centre for Addiction and Mental Health (CAMH), Toronto, ON, Canada (n = 3). Two liver tissue samples were from the National Institute of Child Health and Human Development-supported tissue retrieval programs at the Central Laboratory for Human Embryology at the University of Washington . The use of these tissues was classified as non-human subject research by the University of Missouri-Kansas City Pediatric Health Sciences Review Board . In addition, three DNA samples from the Coriell Institute for Medical Research  were investigated. All DNA samples for this analysis were obtained from individuals participating in IRB-approved studies and provided written informed assent or consent. Study protocols under which CYP2D6*17, 4 heterozygous and 2 homozygous, respectively) served as control samples to evaluate the performance of TaqMan assays.Twenty-two additional previously genotyped subjects  was isolated from whole blood and tissues using silica-based spin columns such as the QIAamp® DNA Blood Mini Kit, DNeasy® Blood and Tissue Kit from Qiagen  or by a phenol-chloroform-based procedureCYP2D6 genotype analyses were carried out with ready-to-order TaqMan assays from Thermo Fisher , CYP2D6*4 , CYP2D6*4 and *10  and CYP2D6*17 , although all subjects were tested for additional allelic variants. Allele designation is according to the Human Cytochrome P450 (CYP) Allele Nomenclature Databaseo Fisher . For theAt CAMH, 20 ng gDNA was amplified as per manufacturer's directions scaled to a total volume of 10 μL in an Applied Biosystems® Veriti® 384-well thermal cycler for each of the above assay IDs. Post-amplification products were analyzed on an Applied Biosystems® ViiA™ 7 Real-Time PCR System and genotype calls were determined manually by comparison to six No Template Controls.At UIC 15 ng gDNA was amplified as per manufacturer's directions scaled to a total volume of 10 μL in 96 well plate format using an Applied Biosystems® StepOnePlus™ Real-Time PCR System for each of the above assay IDs. Genotype calls were assessed with Applied Biosystems® TaqMan® Genotyper™ Software.CYP2D6 gene by XL-PCR  were repeated at least once.All samples with discordant results with the original TaqMan assay (CYP2D6 1023C>T was developed at CMH. Primer and probe locations are shown in A custom TaqMan assay for FokI for one hour and restriction fragments resolved by agarose gel electrophoresis. Amplicons derived from CYP2D6*17 (1023 T) remained uncut while those carrying wild-type 1023 C were cut once resulting in 392 bp and 23 bp long digestion products.Selected study subjects were re-genotyped at CMH employing a restriction fragment length polymorphism (RFLP) assay. gDNA or diluted XL-PCR fragment A served as template to amplify a 414 bp long product with primers 5′ CTCCATCACAGAAGGTGTGACC (forward) and 5′ GCTCGGACTACGGTCATCAC (reverse). The amplicon was subsequently subjected to restriction digestion with CYP2D6*4-specific XL-PCR fragment that was generated with a CYP2D6*4-specific forward primer, were sequenced in both directions with BigDye® terminator v3.1 cycle sequencing chemistry and a 3730 capillary DNA analyzer (Thermo Fisher). GenBank entries AY545216 and M33388 served as reference sequences. Sequence primers utilized at CMH and covering the immediate gene regions of interest, i.e. 1023C>T and 1846G>A, are provided in CYP2D6-specific primers. CAMH samples were re-sequenced for the area surrounding 1023C>T as follows: 24 ng gDNA was combined with 1× Fermentas PCR buffer containing KCl , 1.5 mM Fermentas MgCl2, 0.2145 μg each primer  was used at a 1× concentration and combined with 10–20 ng of genomic DNA, 0.5 μM of each primer, and 5% DMSO. Reactions were cycled for an initial denaturation at 94°C for 3 minutes followed by 35 cycles of denaturation (94°C for 20 sec), annealing (30 sec) and extension (details see http://mfold.rna.albany.edu/. Mfold is a tool developed for predicting the secondary structure of RNA and DNA using thermodynamic methodsA 164 bp long DNA sequence framed by the primer sequences shown in red in A.G. and N.F. conceived the project. A.G., N.F., T.H. and J.R.B. directed experiments at respective study sites. A.G., N.F., T.H., D.I., A.K.R. and J.S.L. analyzed and interpreted results. A.G., N.F., T.H. and A.K.R. prepared tables and figures. M.C., L.K.M.J., M.A.S. and J.R.N. provided sample materials and information. The manuscript was prepared by A.G. with critical contributions by T.H. and N.F. All authors reviewed, commented on and approved the final manuscript.


Genotyping-by-sequencing (GBS) represents a highly cost-effective high-throughput genotyping approach. By nature, however, GBS is subject to generating sizeable amounts of missing data and these will need to be imputed for many downstream analyses. The extent to which such missing data can be tolerated in calling SNPs has not been explored widely. In this work, we first explore the use of imputation to fill in missing genotypes in GBS datasets. Importantly, we use whole genome resequencing data to assess the accuracy of the imputed data. Using a panel of 301 soybean accessions, we show that over 62,000 SNPs could be called when tolerating up to 80% missing data, a five-fold increase over the number called when tolerating up to 20% missing data. At all levels of missing data examined (between 20% and 80%), the resulting SNP datasets were of uniformly high accuracy (96–98%). We then used imputation to combine complementary SNP datasets derived from GBS and a SNP array (SoySNP50K). We thus produced an enhanced dataset of >100,000 SNPs and the genotypes at the previously untyped loci were again imputed with a high level of accuracy (95%). Of the >4,000,000 SNPs identified through resequencing 23 accessions , 1.4 million tag SNPs were used as a reference to impute this large set of SNPs on the entire panel of 301 accessions. These previously untyped loci could be imputed with around 90% accuracy. Finally, we used the 100K SNP dataset (GBS + SoySNP50K) to perform a GWAS on seed oil content within this collection of soybean accessions. Both the number of significant marker-trait associations and the peak significance levels were improved considerably using this enhanced catalog of SNPs relative to a smaller catalog resulting from GBS alone at ≤20% missing data. Our results demonstrate that imputation can be used to fill in both missing genotypes and untyped loci with very high accuracy and that this leads to more powerful genetic analyses. Firstly, it has allowed researchers to decode the whole genome of many organisms. Currently, hundreds of eukaryotic genomes have been sequenced  are combined, there can be loci that are not typed at all within a population, i.e. there is no information for a SNP locus in all individuals of the population except for a few individuals that can be common to both datasets. The first type of missing data can be termed a “missing genotype” while the second is termed an “untyped locus”. There has been considerable interest in imputing such missing data based on the available data . Many toImputation is the substitution of some value for missing data, in other words, ‘filling in’ missing data with plausible values. Generally, methods of genotype imputation are based on the concept that SNPs close together on a chromosome are often inherited together. The resulting correlations among SNPs are referred to as linkage disequilibrium (LD), or association, in the genetic literature . Many meA first approach is to use regression models to impute the missing genotypes by using flanking SNPs as covariates . RegressPhase-based methods consider haplotype structure and common descent patterns . As humaIn this work, we explored the accuracy and efficiency of different imputation tools for both the imputation of missing genotypes in the context of GBS and of untyped loci in the context of combining SNP datasets obtained through different genotyping approaches . Finally, we examined the impact of using such enhanced SNP datasets in genome-wide association analyses.ApeKI digestion) and a total of 450 million 100-bp reads (~1.5M reads/line) were processed through our analytical pipeline that relies on SAMtools to call SNPs as described previously in Sonah et al. [G. max and G. soja accessions genotyped with 42,508 SNPs are publicly available on Soybase (www.soybase.org). Of these 19,652 accessions, 25 were in common with the 301 Canadian lines used for GBS. Finally, on the basis of geographic distribution and genotypic diversity, we chose 23 soybean . First trifoliate leaves from 12 day-old plants were harvested and immediately frozen in liquid nitrogen. Frozen leaf tissue was ground using a Qiagen TissueLyser. DNA was extracted from approximately 100 mg of ground tissue using the Qiagen Plant DNeasy Mini Kit according to the manufacturer’s protocol. DNA was quantified on a NanoDrop spectrophotometer. Illumina Paired-End libraries were constructed for DNA samples using the Illumina Tru-seq DNA Library Prep Kit  following the manufacturer’s instructions. DNA library quality was verified on an Agilent Bioanalyzer with a High Sensitivity DNA chip. Samples were sequenced using the Illumina HiSeq 2000 platform at the McGill University-Génome Québec Innovation Center in Montreal, QC, Canada.Illumina paired-end reads were aligned using the Burrows-Wheeler Aligner (BWA)  onto theWe used three software tools to impute missing data: fastPHASE , BEAGLE For the initial estimation of the accuracy of genotype calls in GBS analysis, we compared the called genotypes at all loci on a single chromosome  for the 23 lines common to both the GBS and WGS datasets. These GBS-derived genotypes were directly compared with the true genotypes  using an in-house script. Similarly, all imputed genotype calls  on Gm03, following imputation , were compared with the true genotypes (WGS). To verify that this chromosome was representative of the broader genome, we estimated the overall genotype accuracy (GBS-derived and imputed SNPs) for all chromosomes (Gm01 to Gm20) using BEAGLE only and at MaxMD≤80% and MinMAF = 0.003.To assess the accuracy of imputation at untyped loci when combining GBS and SoySNP50K datasets, i.e. when the SoySNP50K data were used as a reference panel to impute genotypes at loci not common to both datasets we extracted the genotypes at all loci on chromosome Gm03 for three lines  for which WGS, GBS, and SoySNP50K data were available. Imputed SNP genotypes were compared with the true genotypes revealed by WGS.Similarly, to assess the accuracy of imputation at untyped loci that were imputed using the WGS dataset, we used the WGS SNP data from 22 of the 23 resequenced lines as a reference panel to impute these SNPs onto the GBS or GBS + SoySNP50K data. The remaining line was kept for validation of the imputed SNPs. We performed three permutations where a single line was kept aside to estimate imputation accuracy . We thenp) was used to establish a significance threshold [A subset of 139 soybean lines were used in the GWAS analysis. Phenotypic data (seed oil content) for these lines were originally described by Sonah et al. . All thehreshold , 26.We first explored the impact of two key filtering steps central to the production of SNP catalogs derived from GBS analysis: the maximal amount of missing data allowed  and the minimal minor allele frequency (MinMAF). A set of 301 Canadian soybean lines was subjected to GBS analysis and a total of 450 million 100-bp reads (mean of ~1.5M reads/line) were processed through our analytical pipeline that calls SNPs using Samtools  at the most permissive MinMAF level (0.003). As can be seen in To examine the quality of the SNP data obtained using GBS, we first assessed the accuracy of the SNP genotypes initially called by GBS, prior to any imputation. To achieve this, we performed whole-genome resequencing on a representative subset of 23 soybean lines at a mean depth of coverage of 9x (genome coverage of 96%) . A totalIn a second step, to estimate the accuracy of imputed SNP data (i.e. formerly missing genotypes), we performed imputation at all levels of MaxMD and MinMAF on the entire set of 301 lines. Once again, we used the resequencing data as a reference and, as shown in As illustrated above, the proportions of called and imputed SNP genotypes did vary at different MaxMD levels and thus impacted the overall accuracy of the resulting SNP catalog. The accuracy of the entire GBS-derived SNP dataset (after imputation) was measured and is illustrated in Finally, although all three software tools performed equally well in terms of accuracy of imputation, computational speed varied considerably . WhereasThe existence of multiple genotyping approaches offers the opportunity to exploit already existing haplotype information to further enhance marker density and to facilitate the integration of data obtained from different genotyping platforms. We first wanted to test whether the publicly available SoySNP50K array data obtained on 19,562 USDA soybean accessions could be used to impute additional (“untyped”) SNPs in our GBS-derived catalog of SNPs. In a first step, we identified 25 accessions common to our set of 301 lines and the USDA collection. By comparing the SNP data for these common accessions, we found that only 7% of markers  were shared between the GBS and SoySNP50K data. As these two datasets have a limited overlap, this offered the potential of adding a large number of untyped SNP loci through imputation. In a second step, we used the SoySNP50K data as a reference panel to perform imputation of genotypes at the untyped loci in our GBS-derived catalog. As shown in Another source of haplotype information resided in our WGS data on the subset of 23 resequenced Canadian lines. We therefore tested how useful this information could be in terms of imputing an even larger set of untyped loci. As described above, a total of 3.6M SNPs were identified among these 23 lines. We removed all redundant markers, i.e. SNPs that were in perfect LD with at least one other SNP, thus reducing this reference panel to 1.4M tag SNPs. We then used BEAGLE and IMPUTE2 for imputation using the SNP data from 22 lines as a reference panel and keeping the last line (Gaillard) for the estimation of accuracy. As shown in Finally, to ensure that these results were broadly applicable to the larger set of 23 lines, two additional permutations were done where a different set of 22 lines was used as a reference panel and the remaining line (Mandarin or OAC-Lakeview) used for validation. Here again, the accuracy of imputation proved highly similar to the results described above, ranging between 87.9% and 92.4% .To explore the impact of the size of this reference panel on the accuracy of imputed SNPs, we performed imputation with reference panels representing subsets of 5, 10, 15 or 22 of the 23 lines for which WGS data were available. As can be seen in p = 9.6×10−3 and q = 0.09) with seed oil content. In contrast, using the enhanced SNP catalog . Interestingly, the peak SNP in both cases was the same (Gm19_41742182), but its association with oil content exhibited a much higher p-value (3.1×10−7) and lower q-value (0.01). This demonstrates that the increased number of informative SNP loci, obtained through the imputation of both missing GBS data and untyped loci from additional sources of SNP haplotype information, can prove highly beneficial in studying the genetic architecture of complex traits.To determine if the enhanced SNP catalogs obtained through imputation could provide increased power in genome-wide association scans, a subset composed of 139 soybean lines was used to perform an association analysis for seed oil content. This subset was used because phenotypic data were available only for these lines. One analysis was conducted using a “basic” GBS catalog of 7,152 SNPs obtained at MaxMD = 20% and MinMAF = 0.05, while the other was performed using an enhanced catalog resulting from imputation of missing GBS data (at MaxMD = 80%) and untyped loci from the SoySNP50K dataset. At MAF≥0.05, a total of 83,532 SNPs were retained within this combined dataset. As can be seen in  catalog  and a muA first key element to come out of this work is that MaxMD is the most important factor determining the number of SNPs in GBS analysis. As seen in this study when increasing MaxMD from 20 to 80% incrementally, the number of SNPs called increased from 12,712 to 62,643. As previously described, one of the unique features associated with GBS is the generation of highly incomplete SNP genotype data –30, largAs described, the number of SNPs is also affected by MinMAF, but the overall proportion of missing data is hardly affected. The effect of MinMAF on the number of SNPs has been described in several reports. The number of SNP increases as the minor allele frequency decreases ,35,37. TArabidopsis thaliana:~3–4 kb; humans <5kb; cattle <10kb) [Somewhat counterintuitively, a second key result of this work was that imputation of missing data was more accurate when performed on datasets with a higher proportion of missing data. Indeed, at MaxMD = 80%, 94% of SNP genotypes were correctly imputed, whereas at MaxMD = 20%, the accuracy decreased to 86%. Upon reflection, however, it seems logical that a larger number of SNP markers  better captures the diversity of haplotypes that are present within a collection of lines. Increased imputation accuracy at MaxMD = 80% is likely achieved through increased LD between markers. As documented by Zheng et al. , imputate <10kb) –44. HighA novel aspect of this work is that the measurement of the accuracy of imputation was assessed by comparing directly to whole genome resequencing data obtained for a subset of the lines. In many previous studies, estimates of the accuracy of imputation have been achieved by masking a subset of the data, imputing these missing genotypes, and then comparing the imputed genotype with the original data ,35–37. FFurthermore, although the threshold for retaining a SNP marker at MaxMD = 80% would suggest a tremendous amount of missing data, we showed that, averaged across all markers kept at this threshold, a mean of 50% missing data was obtained. When we considered jointly both the called and imputed markers comprising the final dataset at the various missing data levels, all were highly accurate (96–98%). This is because the genotypes initially called via GBS analysis are themselves highly accurate (98.4%). At MaxMD = 20%, these high-quality SNPs are combined with a small proportion (7%) of SNPs imputed with what we term a “good” accuracy (84%). At the other end of the missing data spectrum (MaxMD = 80%), the original set of GBS-called SNPs is combined with an equal amount (~50%) of SNPs derived from imputation with an only slightly lower accuracy (94%). Thus, catalogs of called and imputed SNPs retain a constant, high level of accuracy (~97%) across a broad range of missing data thresholds.A third key finding of this work is that different and highly complementary marker datasets can be successfully combined via imputation at untyped loci. We showed that SNP catalogs derived from two high-throughput genotyping techniques, GBS and a SNP array (SoySNP50K), could be fused through the imputation of a large number of untyped loci. Because of the different composition of the two initial catalogs, only 7% of the GBS markers were present in the SoySNP50K set. This is because most 90%) of the SoySNP50K markers are present in genic regions , while m0% of theAlthough all imputation softwares use the same fundamental phenomenon of LD across the genome, the algorithms employed by each package differ. Likewise, each package offers differing strengths and weaknesses. Therefore, it is a good idea to use more than one software package, compare results, and investigate any major discrepancies . To perfWhen performing imputation at a much larger scale, using the 1.4M tag SNPs identified in our resequencing effort, the accuracy of imputation of this large number of untyped loci was dependent on the number of lines included in the reference panel. When increasing the number of lines composing the reference panel from only 5 to a maximum of 22, imputation accuracy increased from ~60% to close to 90%. Similarly, in humans, Li et al.  showed tA final key finding of this work is that the much increased marker coverage achieved through a better exploitation of available GBS and SoySNP50K data is highly useful in the genetic dissection of complex traits. The availability of higher density marker coverage enables researchers to more accurately determine which regions to investigate further and actually narrow down each region on which they should perform fine mapping. As illustrated in our analysis of seed oil content, the use of an enhanced SNP catalog (~6 fold larger) allowed us to capture more significant marker-trait associations around candidate QTLs and the significance level of such associations was also much higher. These results are consistent with recent work in both animals and plants that have demonstrated the benefits of marker imputation for GWAS ,49. In tAs seen in this study, genotype imputation represents an essential tool in the analysis of high-throughput genotypic data. One of the most common criticisms regarding GBS is the presence of a substantial amount of missing data. Our data show that this can largely be overcome in soybean thanks to highly accurate imputation of missing genotypes. Furthermore, genotype imputation is particularly useful for combining results across studies that rely on different genotyping platforms. As different groups may use different genotyping tools, it is highly important to be able to produce integrated datasets that include all such markers to facilitate the exchange of knowledge and information. It is important to remember, however, that imputation accuracy will be affected by the extent of LD in the population/species studied. Finally, a further benefit of such imputation is that it increases the power of individual scans thanks to more extensive marker coverage. In the coming years, we expect these imputation-based analyses will become a key tool in the analysis of massively parallel shotgun sequence data enabling geneticists to rapidly deploy these technologies to analyze large samples and dissect the genetic basis of complex traits.S1 Table(DOCX)Click here for additional data file.S2 TableMissing data were imputed using BEAGLE at MaxMD = 80% and MinMAF = 0.003.(DOCX)Click here for additional data file.S3 TableGenotype data (1.4M SNPs) for 22 resequenced soybean lines were used to impute genotypes at loci that were untyped in the GBS SNP dataset while a 23rd resequenced line  was set aside for validation. The imputed genotypes were compared to the result of the resequencing in the validation line. Three different permutations of the data (each time leaving out a single resequenced line from the reference panel) were performed.(DOCX)Click here for additional data file.


Considering the obvious advantages of single nucleotide polymorphisms (SNPs) and insertion-deletion polymorphisms (InDels), expressed sequence tags (ESTs) were analyzed Gossypium hirsutum and G. barbadense, mining G. hirsutum unigenes, and analyzing 3′ untranslated region (3′UTR) sequences. The marker polymorphisms were investigated using the two parents of the mapping population based on the single-strand conformation polymorphism (SSCP) analysis. Of all the markers, 137 (10.16%) were polymorphic, and revealed 142 loci. Linkage analysis using a BC1 population mapped 133 loci on the 26 chromosomes. Statistical analysis of base variations in SNPs showed that base transitions accounted for 55.78% of the total base variations and gene ontology indicated that cotton genes varied greatly in harboring SNPs ranging from 1.00 to 24.00 SNPs per gene. Sanger sequencing of three randomly selected SNP markers revealed discrepancy between the in silico predicted sequences and the actual sequencing results.A total of 1,349 EST-based SNP and InDel markers were developed by comparing ESTs between In silico analysis is a double-edged blade to develop EST-SNP/InDel markers. On the one hand, the designed markers can be well used in tetraploid cotton genetic mapping. And it plays a certain role in revealing transition preference and SNP frequency of cotton genes. On the other hand, the developmental efficiency of markers and polymorphism of designed primers are comparatively low.The online version of this article (doi:10.1186/1471-2164-15-1046) contains supplementary material, which is available to authorized users. Molecular markers are the foundation of modern molecular plant breeding. There are many types of molecular markers such as restriction fragment length polymorphism (RFLP), simple sequence repeat (SSR) and SNPs. In cotton which is the world most important natural fiber crop, the most prevalent marker type as of today is SSR. However, with the advent of next generation sequencing technologies that significantly reduce sequencing cost, SNP markers are becoming more and more important due to their abundance in the genome and very simple genetic mode .Cotton researchers have tried different methods to develop SNP markers. An et al.  reportedBrassica napus that is also an allotetraploid using transcriptome sequencing. This study provided some insights that could be useful in the development of cotton SNP markers.Due to its allotetraploid nature, it has been a challenge to differentiate a true SNP (within a sub-genome) from a pseudo-SNP (between subgenomes) in cotton SNP marker development. In 2009, Trick et al.  developeDirect sequencing has been a standard method to develop SNP markers, although its efficiency is low, especially in plants with complex genomes , 9. AnotArabidopsis with 141 progenies [rogenies  was usedG. hirsutum and G. barbadense downloaded from NCBI (http://www.ncbi.nlm.nih.gov/) were clustered using the wcd program [http://www.bioinformatics.nl/tools/haplosnper/) to identify SNPs between G. hirsutum and G. barbadense with default parameters. In this step, the clusters containing no SNPs or inter-homoeologous SNPs  (http://frodo.wi.mit.edu/primer3/) was used to design primers with criteria as follows: primer length 18–24 bp, optimum 20 bp; GC content 40-60%, optimum 50%; optimum annealing temperature 58°C; and PCR product size 100–300 bp. The SNP markers developed were named as HAU-SNP001 ~ HAU-SNP356.The ESTs of  program . All of s Figure a were dis Figure b or hemis Figure c were keG. hirsutum unigenes with at least four ESTs were downloaded from NCBI, and then unigenes with ESTs from the same genotype were discarded. For the remaining unigenes, HaploSNPer was used to identify SNPs with default parameters. Unigenes that contained only potential SNPs predicted by HaploSNPer were further removed  were as follows: primer length 18–24 bp, optimum 20 bp; GC content 35-60%, optimum 50%; optimum annealing temperature 55°C; and PCR product size 100–500 bp [Message RNA sequences with complete CDSs were selected from the collected 0–500 bp .Arabidopsis were kindly provided by Prof. Graziano Pesole (graziano.pesole@biologia.uniba.it). The collected genome survey sequences, nucleotide sequences and mRNA sequences of G. hirsutum were used to blast against the Arabidopsis 3′UTR sequences with E value of 1.0 × E-10 and matched sequence length of ≥100 bp. Subsequently, the redundant sequences were removed manually. The matched parts of the G. hirsutum sequences were also used to design HAU-InDel-prefixed primers following the similar methods described above.The 3′UTR sequences of PCR amplification of SNP and InDel markers was conducted according to the methods described by Lin et al. . Polymor1 linkage map [The polymorphic SNP and InDel markers were integrated into our previously published interspecific BCkage map , 31, 32.All the reliable SNPs were subjected to the statistical analysis, producing six kinds of base variations including A/- or T/-, C/- or G/-, A → G or T → C, A → C or T → G, A → T or T → A and C → G or G → C. In addition, the SNP frequency in cotton genes was evaluated by combining the gene ontology analyses of all the sequences used to design the HAU-SNP-prefixed markers. The functional annotation of nucleotide sequences was performed using Blast2GO , 34 withThe PCR-amplified products of randomly chosen SNP markers were recovered from agarose gels and purified using QIAGEN purification kits . The purified amplicons were cloned into T-Easy vector . Then at least three clones were randomly selected to be commercially sequenced from both ends using M13F and M13R primers. All above experimental procedures were according to the methods described by Li et al. . After rDetails of the 356 interspecific EST-SNP markers. SNP primer names, forward and reverse primer sequences, reference sequences used to design primers, and details of the interspecific SNPs are all listed. (XLS 2 MB)Additional file 1:Primary screening process of clusters used to develop interspecific EST-SNP markers. Four types of clusters produced after identification of interspecific SNPs using HaploSNPer. (TIFF 99 KB)Additional file 2:Flowchart of developing inter/hemi-SNPs. i: Primers amplifying inter/hemi-SNPs; h: Primers amplifying only hemi-SNPs. One hundred and thirty-four primers amplifying inter/hemi-SNPs, and 15 primers amplifying only hemi-SNPs were developed finally. (TIFF 172 KB)Additional file 3:Flowchart of developing hemi-SNPs. h: Primers amplifying only hemi-SNPs. Two hundred and seven primers amplifying only hemi-SNPs were developed finally. (TIFF 169 KB)Additional file 4:Details of the 455 intraspecific EST-SNP markers. SNP primer names, forward and reverse primer sequences, unigenes and reference sequences used to design primers, and details of the intraspecific SNPs are all listed. (XLS 757 KB)Additional file 5:Flowchart of developing intraspecific EST-SNP markers. After several steps of selection, only 8,572 of the total 21,738 unigenes were eligible for further analysis to design intraspecific EST-SNP markers, and 455 markers amplifying intraspecific EST-SNPs were developed finally. (TIFF 244 KB)Additional file 6:Details of the 415 EST-InDel markers developed by mining the 3′UTRs of publicG. hirsutumsequences. InDel primer names, forward and reverse primer sequences, reference sequences used to design primers and their Genebank numbers are all listed. (XLS 713 KB)Additional file 7:Details of the 123 EST-InDel markers developed by blasting putative 3′UTRs ofG. hirsutumagainst the 3′UTRs ofArabidopsi.s InDel primer names, forward and reverse primer sequences, and reference sequences used to design primers are all listed. (XLS 80 KB)Additional file 8:Flowchart of developing HAU-InDel-prefixed markers by blasting putative 3′UTRs ofG. hirsutumagainst the 3′UTRs ofArabidopsis. Three parts of cotton 3′UTRs were undergone blast analysis against the Arabidopsis 3′UTRs respectively. Obtained unique sequences produced 62 contigs and 111 singlets, then 123 primers amplifying cotton InDels existing in 3′UTRs were developed. (TIFF 150 KB)Additional file 9:Linkage map of 26 cotton chromosomes based on an interspecific BC1population. SNP and InDel markers reported in this research are italicized, underlined and bolded. (PDF 10 MB)Additional file 10:SNP frequencies of cotton genes on level 3. There were 4 sub-categories in cell component category (a), 24 in molecular function category (b) and 32 in biological process category (c). Among all the sub-categories, the number of SNPs/genes varied from 1.00 to 24.00. (TIFF 245 KB)Additional file 11:The electrophoresis gel of marker HAU-SNP572.(TIFF 2 MB)Additional file 12:


The objective was to conduct genome-wide association studies (GWAS) for the fatty acid traits and to evaluate the accuracy of genomic prediction for fatty acid composition using genomic best linear unbiased prediction (GBLUP) and Bayesian methods.Identification of genetic variants that are associated with fatty acid composition in beef will enhance our understanding of host genetic influence on the trait and also allow for more effective improvement of beef fatty acid profiles through genomic selection and marker-assisted diet management. In this study, 81 and 83 fatty acid traits were measured in subcutaneous adipose (SQ) and FASN), stearoyl-CoA desaturase (SCD), and thyroid hormone responsive (THRSP) genes. For the majority of the fatty acid traits studied, the accuracy of genomic prediction was relatively low (<0.40). Relatively high accuracies (> = 0.50) were achieved for 10:0, 12:0, 14:0, 15:0, 16:0, 9c-14:1, 12c-16:1, 13c-18:1, and health index (HI) in LL, and for 12:0, 14:0, 15:0, 10 t,12c-18:2, and 11 t,13c + 11c,13 t-18:2 in SQ. The Bayesian method performed similarly as GBLUP for most of the traits but substantially better for traits that were affected by SNPs of large effects as identified by GWAS.In total, 302 and 360 significant SNPs spanning all autosomal chromosomes were identified to be associated with fatty acid composition in SQ and LL tissues, respectively. Proportions of total genetic variance explained by individual significant SNPs ranged from 0.03 to 11.06 % in SQ, and from 0.005 to 24.28 % in the LL muscle. Markers with relatively large effects were located near fatty acid synthase  contains supplementary material, which is available to authorized users. Dietary fats influence risks for developing cardiovascular disease, obesity and various forms of cancer, and have led to recommendations to limit consumption of some foods including beef . Recommeet al. longissim storage  using a  storage  using Ag storage , was comP < 10−6), or in high correlation with another SNP (r ≥ 0.95) were removed. After filtering, 35,446 SNPs were kept for analyses. Sporadically missing genotypes represented 0.14 % of the total genotypes and were imputed via Beagle 3.3.2 [All animals were genotyped on the Illumina BovineSNP50 Beadchip comprised of 54,609 SNP markers. Markers with minor allele frequency less than 0.05, missing rate greater than 0.20, extremely deviated from Hardy-Weinberg equilibrium test  ~ (1 − π)N + πδ0(aj), where N is a normal distribution with mean 0 and variance σa2, and δ0(aj) denotes a distribution concentrated at zero, and (1 − π) and π are the weights for the two distributions. A latent indicator variable γj was introduced for each SNP so that when γj = 1, aj ~ N, and when γj = 0, aj = 0. Prior distribution for γj follows a Bernoulli distribution with probability (1 − π), and the joint prior density for γ is ei was assumed from a normal distribution N. The prior distribution for σa2 (or σe2) is a scaled inverse Chi-square distribution with degree of freedom va (or ve) and a scale parameter Sa2 (or Se2). The hyper-parameter va (or ve) was arbitrarily set to 4 (or 10), and Sa2 (or Se2) was set to  pj is allele frequency for marker j, et al. [th value was defined as the genome-wise significance threshold. Candidate genes in the window of 1 Mb centering the significant SNPs were obtained by querying the Ensemble gene database using SNP locations from the bovine UMD3.1 genome assembly via the SNP annotation tool in the NGS-SNP suite [A mixture distribution was assumed for n et al. . The Gibn et al. . BrieflyNP suite .Genomic best linear unbiased prediction (GBLUP) and BayesCπ methods were used for genomic prediction. A ten-fold cross validation was used to evaluate the accuracy of genomic prediction. The data was first split into 10 approximately equal-sized groups according to sires of the animals so that no sire families overlapped between any two groups. For each breed type, the number of animals in each cross-validation group was kept approximately the same so that each breed in the validation group was also represented in the training population. For each cross validation, nine groups were used for training and the remaining one was used as the validation population. For GBLUP, animals in the validation population were assumed with no phenotypic values, and the animals in the training and validation populations were then combined to estimate the breeding values for animals in the validation population using a linear animal model, which can be written as:y* is the vector of adjusted fatty acid phenotypic values from animals in the training population, μ is the overall mean, a is the vector of breeding values for all animals, e is the vector of random residuals and Z is the incidence matrix relating a to y*. The additive genomic relationship matrix for all animals was derived from the SNP markers using the first method of VanRaden [i in the validation population was predicted by summing up SNP effects over all loci as follows:aj is the estimated effect for SNP j. For comparisons, a pedigree based BLUP method (PBLUP) was also used to estimate the breeding values, assuming no phenotypic values for validation animals. However, only one generation of the pedigree was available for construction of the additive genetic relationship matrix. Realized accuracy of genomic prediction was measured as the correlation between estimated breeding values and the adjusted phenotype in the validation groups divided by square root of heritability and was averaged across the ten cross-validations.Where VanRaden , and ASRVanRaden  was usedThe data sets supporting the results of this article are included within the article and its additional files. The original data sets used in this study are available upon request as part of the data is not public.