HER2/TOP2A locus is of great importance in breast cancer. The copy number variability at this locus has been proposed to be a marker for the degree of chromosomal instability. We therefore developed a Single Nucleotide Polymorphism (SNP) assay to evaluate allelic imbalance at the HER2/TOP2A locus in three different entities of primary breast tumors.Breast cancer is a heterogeneous disease with various histological features and molecular markers. These are utilized for the prediction of clinical outcome and therapeutic decision making. In addition to well established markers such as HER2 overexpression and estrogen and progesterone receptor (ER and PR) status, chromosomal instability is evolving as an important hallmark of cancers. The Eleven SNPs were carefully selected and detected by real time PCR using DNA extracted from paired  paraffin-embedded tissues. Primary breast tumors of 44 patients were included, 15 tumors with HER2 overexpression, 16 triple negative tumors, defined by the absence of HER2 overexpression and a negative ER and PR status and 13 ER and PR positive tumors without HER2 overexpression. As controls, histologically normal breast tissues from 10 patients with no breast tumor were included.Allelic imbalance was observed in 13/15 (87 %) HER2 positive tumors, the remaining 2 being inconclusive. Of the 16 triple negative tumors, 12 (75 %) displayed instability, 3 (19 %) displayed no instability, and 1 was inconclusive. Of the 13 hormone receptor positive tumors, 5 (38 %) displayed allelic imbalance, while 8 did not.HER2/TOP2A locus using paraffin-embedded tissues. Based on allelic imbalance at this locus, both triple negative and ER and PR positive breast tumors can be subcategorized. The clinical relevance of the allelic (im)balance status at the HER2/TOP2A locus in breast cancer is subject of future study.We conclude that the SNP assay is suitable for rapid testing of allelic (im)balance at the http://www.diagnosticpathology.diagnomx.eu/vs/2086062232155220The virtual slide(s) for this article can be found here: The online version of this article (doi:10.1186/s13000-015-0289-x) contains supplementary material, which is available to authorized users. Breast cancer is a heterogeneous disease that can display a diverse variety of biological features. Several proteins are well known to be expressed excessively in these tumors. Based on the excessive presence of such proteins, breast cancer can be divided in several different biological entities. This breast cancer categorization is utilized for the prediction of clinical outcome and therapy selection .Well known examples of overexpressed proteins that play a critical role in breast cancer diagnostics and choice of therapy, are the estrogen and progesterone receptors (ER and PR). Available targeted therapies in these breast cancer cases are e.g. Tamoxifen and aromatase inhibitors –4.HER2 gene as well as the juxtaposed TOP2A gene, which is located 700 kb telomeric of HER2 [HER2 oncogene encodes the human epidermal growth factor receptor 2 protein, a 185 kDa transmembrane tyrosine kinase receptor also known as erbB-2 and neu [HER2 oncogene is amplified, HER2 overexpression occurs resulting in overstimulation of HER mediated signal transduction [HER2 is found to be amplified in 25–30 % of breast cancers [HER2 amplified breast tumors resulting in overexpression of HER2, benefit from treatment with the monoclonal antibody Trastuzamab (Herceptin). Trastuzamab is thought to abolish the HER2 mediated signaling of the HER signal transduction network [HER2 amplified breast tumors [TOP2A coamplification is the biological rationale behind these observations [TOP2A gene encodes topoisomerase II alpha, a 170 kDa enzyme that catalyzes the topological DNA changes needed during the multistep process of cell division [TOP2A occurs along with HER2 amplification in approximately 30–45 % of the HER2 amplified cases [TOP2A amplified breast tumors seem to benefit from adjuvant AC-based chemotherapy. ACs are chemotherapeutic agents that are known to have, among other mechanisms of action, TOP2A inhibiting properties. TOP2A catalytic activity is inhibited by ACs via stabilization of the intermediate complex where TOP2A tyrosine residues are covalently linked to the 5’ phosphoryl ends of the cut DNA strands during the topological changes which they catalyze. Trapping the complex in this state prevents DNA resealing and further cell cycle progress, which ultimately induces programmed cell death [TOP2A amplification show a trend towards a better disease free survival than tumors lacking this amplification. In addition, TOP2A amplification is an independent predictor of a significantly higher pathological complete response rate in these patients [TOP2A deletion, occurring in 15–40 % of HER2 amplified cases, has been associated with AC resistance [In addition to the overexpression of ER and PR, aberrations of the chromosome 17q12-q22 genomic region play an important role in breast cancer. This locus incorporates the  of HER2 –7. The H and neu . Upon li and neu . When th cancers . Patient network . In addit tumors . Howeverrvations . The TOPdivision . Coamplied cases , 17, 18.ll death , 19, 20.sistance , 22.HER2 overexpression showed minor and less frequent chromosomal instability in comparison to HER2 overexpressed and triple negative tumors that showed more extensive and more frequent chromosomal instability [HER2/TOP2A locus have been proposed to be a marker for the degree of chromosomal instability in breast cancer [The above mentioned biological characteristics are well established biomarkers utilized to categorize breast cancer types for diagnostic and treatment related purposes. More recently, high resolution array based systems, targeting Single Nucleotide Polymorphisms (SNPs) or utilizing Comparative Genomic Hybridization, have been employed to subcategorize these breast tumor types and revealed distinct profiles of gene-expression or genetic alterations –28. Hormtability –29. Copyt cancer , 31. Eart cancer , 31. DetHER2/TOP2A locus.Single Nucleotide Polymorphisms (SNP) are easily detected by real time PCR, a technique that is widely available in many laboratories that routinely employ molecular diagnostics. By analyzing a panel of heterozygous SNPs, copy number alterations and thereby allelic imbalance of the involved locus can be determined. It therefore may provide an accessible method to assess allelic imbalance at the The study was carried out retrospectively with the approval of the Board of Directors and the Scientific Advisory Board of the Jeroen Bosch Hospital. All patient samples were rendered anonymous before use in this study.Ten histologically normal formalin-fixed paraffin-embedded (FFPE) breast -tissues from 10 different patients (CT1-10) with no breast tumor were included as controls  and silver in situ hybridization (SISH) status from 44 patients were included in this study ) 16 triple negatives ) and 13 ER/PR positive and HER2 non-amplified (ERPR+) breast tumors ). To establish the germline SNP profile, a histologically normal archival FFPE tissue  with known 2) for genomic DNA extraction, one for SISH and one for IHC. The latter two were only performed for the tumor samples.Tissues were fixed and processed as previously described . Three-μTo establish the HER2 expression status, IHC was performed on all 44 tumor samples using the Ventana anti-Her2/neu(4B5)  in combination with the VENTANA system (Roche Diagnostics GmbH) according to the manufacturers protocol. IHC 0 and 1+ were considered to represent normal expression, 2+ was considered borderline with regard to expression and 3+ was considered to represent overexpression.HER2 amplification status and to assess chromosome 17 copy number status, SISH was performed on all 44 tumor samples using the INFORM HER2 Dual colour SISH DNA probe cocktail (Roche Diagnostics GmbH) in combination with the VENTANA system (Roche Diagnostics GmbH) according to the manufacturers protocol. Twenty nuclei were counted independently by 2 individuals. The mean from these 2 nuclei counts was calculated. Interpretation was as follows: a ratio (HER2 probe/centromere probe) of <1.8 represented no amplification; a ratio of 1.8–2.2 was equivocal and a ratio of >2.2 represented amplification.To determine the From each FFPE tissue, tumor as well as (reference) histologically normal tissue, a HE-slide was made. The tumor tissue containing HE slides were used to determine the area with the highest tumor cell percentage (TC%). The HE slides containing the histologically normal tissue were used to determine the location of the highest nuclei density. Subsequently, these were used as a reference for tumor and histologically normal cell macrodissection, respectively. The respective cells were scraped off the corresponding blank slides. The scrapings were used for digestion and subsequent EasyMAG NucliSens  DNA extraction as described previously .HER2 gene within the HER2 smallest region of amplification [TOP2A gene. The selected SNPs displayed an allele frequency of approximately 0.5 in Caucasian  from Utah), Chinese , Japanese  and African  populations. The latter to maximize the chance for heterozygosity, independent of the patients’ ethnicity, for only those SNPs are informative.From the TaqMan® SNP Genotyping Assays from Applied Biosystems, 11 suitable SNPs were chosen using SNP browser software Version 4.0 . When both alleles were present, a delta Rn VIC/FAM ratio could be established allele 1/allele 2). This ratio was considered to be informative with regard to the relative number of alleles present and therefore a reflection of the allelic (im)balance status. Only the SNPs that were heterozygous for a given patient were informative. A schematic representation of this experimental scheme is presented in Fig. /allele 2For each informative SNP the VIC/FAM ratios were plotted in a scatterplot. To minimize the influence of DNA concentration on VIC/FAM ratio and therefore data interpretation, the delta Rn FAM was corrected. The correction was performed by adapting the delta Rn FAM values of the histologically normal samples per SNP according to the crossing point of the trend line with the y-axis. This resulted in the translocation of the trend lines of the histologically normal tissues so that they all passed through the origin. All delta Rn FAM data of the tumor samples were subsequently corrected accordingly.th and 75th percentiles of the ratios generated by all histologically normal tissues (from the control group as well as the group of breast tumor patients) were calculated for each SNP assay using Microsoft Office Excel 2007 . The distance between these percentiles represents the interquartile range (IQR), which was used to establish cut-off values for the normal ratio distribution per SNP. The +1.5 IQR and −1.5 IQR were taken as cut-off value for normal ratio distribution for 10/11 SNPs . All ratios with an IQR of <1.5 follow normal distribution and were interpreted as genetically stable with regard to the HER2/TOP2A locus. Ratios plotted within 1.5–3 x IQR were considered equivocal with regard to HER2/TOP2A allelic instability status. Ratios plotted ≥3x IQR were considered to be representative for HER2/TOP2A allelic instability. Due to the large IQR generated using SNP 3 (varying from 2.3 to 15.1 times as large as found with the other SNPs), the cut-off values for that particular SNP were defined as follows: normal = −1 to +1 IQR; equivocal = 1–2 IQR and ≥2 IQR = indicative of allelic instability.To gain insight into the distribution of delta Rn VIC/FAM ratios , the 25When a sample generated an equivocal result for any of the employed SNPs, the analysis of that particular sample was repeated in duplicate (with non-diluted DNA and with 5-fold diluted DNA in Tris Low EDTA buffer) using the respective SNP. When 2/3 results corresponded, the sample was called accordingly . When results were conflicting, the sample was called equivocal .Two patient groups were used in this study: a control group of 10 histologically normal tissues from patients (CT1-10) with no breast tumors and a group of 44 breast tumor patients with different entities of primary breast cancer  of which both tumor and normal tissues were available.Technical validation of the selected SNP assays was performed using the 10 histologically normal breast tissues from the control group. All SNP assays had a sufficient sensitivity, yielding cycle-threshold values of <35 (threshold 0.007), a cut-off above which results are routinely considered less reliable in our laboratory. In addition, little or no cross-reaction of the SNP assays’ allele 1 corresponding probes (VIC reporter) with allele 2, and of the allele 2 corresponding probes (FAM) with allele 1 was observed. This resulted in clear clustering of homozygous allele 1, homozygous allele 2 and heterozygous patients. The panel of 11 SNP markers balance, fluorescent ratios of the panel of 11 informative SNP’s were analyzed using the cut-off values based on the interquartile range of data distribution amongst the histologically normal samples.Subsequently, the delta Rn VIC/FAM ratios of all informative SNPs of the histologically normal tissues from both groups (10 tissues from the control groups and 44 tissues from the breast tumor group) were used to gain insight in normal data distribution. Combining the data of the histologically normal tissues from these groups was justified due to the mutual similarity of delta Rn VIC/FAM ratio distribution amongst both groups. To determine the As expected, all informative SNP results  of the breast tissues from the control group fell within the normal IQR range . Overall results are depicted in Additional file The 44 histologically normal samples of the breast tumor patient group generated SNP results that fell within the normal IQR range and are therefore in agreement with the normal range observed in the control group. Only 1 out of the total of 208 informative SNPs yielded an equivocal result .HER2/TOP2A locus. The results are summarized in Additional file Results of IHC and SISH are depicted in Additional file HER2/TOP2A locus. IHC scores were 3+, SISH ratios were 2.0–15.2 and TC% ranged from 40 to 80 %. SNP 1 in patient HER2+ 8 generated an equivocal result. SNP results from patient HER2+ 1 and 6 showed inconsistent results between informative SNPs. All informative SNPs were therefore subsequently repeated in duplicate for these patients yielding identical results. SNP results from patient HER2+ 1 showed allelic balance of SNPs 8 and 9, whereas results from SNPs 1 and 10 were found to indicate allelic instability. IHC yielded a 3+ score, whilst the SISH ratio was 1.1. The sample had a TC% of 30 %. SNP results from patient HER2+ 6 were also inconsistent. SNPs 1, 2 and 8 were indicative of allelic stability, while results from SNPs 4, 7 and 11 showed allelic instability. IHC, SISH ratio and TC% in this patient were 3+, 4.1 and 80 %, respectively.Within the group of patients with tumors overexpressing HER2, the SNP data of patients HER2+ 2–5 and 7–15 were consistently indicative of an instable HER2/TOP2A locus. IHC scores, SISH ratios and TC% varied from 0–2+, 0.7–1.9 and 25–90 %, respectively. Several SNPs generated equivocal results in patients TN1 , 3 , 8 , 10  and 13 . Patients TN5, 7 and 11 revealed no signs of HER2/TOP2A allelic imbalance, IHC scores were 0–1+, SISH yielded ratios of 1.1–1.6 and the samples contained 30–95 % tumor cells. In patient TN8, the 2 SNPs that were informative (SNPs 1 and 2) yielded equivocal results. IHC score, SISH ratio and TC% for this patient were 1+, 0.9 and 70 %, respectively.In the triple negative group, patients TN1-4, 6, 9, 10 and 12–16 showed SNP results consistent with instability of the HER2/TOP2A locus instability. IHC scores, SISH ratios and TC% varied from 0–1+, 0.5–1.1 and 30–70 %, respectively. Equivocal results were generated in patients ERPR+ 3  and 7 . The ratio of SNP 3 was found to be equivocal  in patient ERPR+ 7. The breast tumor tissues from patients ERPR+ 4, 5 and 8–13 showed a stable HER2/TOP2A locus, IHC scores were 0–1+, SISH ratios ranged from 1.0–1.5 and a TC% was 50–70 %. Patient ERPR+ 8 and 11 generated an equivocal result utilizing SNP 8  and 4 , respectively.From the ERPR+ group, patients ERPR+ 1–3, 6 and 7 yielded SNP results indicative of HER2/TOP2A locus in breast tissues. The assay yielded results that fell within the normal range of data distribution in the control tissues. SNP results were consistent  within most (42/44) tumor samples and were indicative of abundant allelic imbalance in HER2 amplified and TN tissues.We have shown that the presented SNP based real time PCR assay is easy to perform and can be employed to reveal allelic instability of the HER2/TOP2A allelic instability. In 2 patients conflicting results between informative SNPs were found. In one sample (patient HER2+ 1), SNP 1  and SNP 10 results showed allelic instability while no allelic instability was observed for SNP 8 and 9. SISH analysis of this sample revealed no genomic amplification of the HER2 locus. This possibly implies a false positive IHC result or HER2 overexpession not related to gene amplification, which has been reported in 3–8 % of breast tumors [HER2/TOP2A locus. These observations may be the result of a combination of relatively small genetic aberrations each affecting part of the locus.In all patients (15/15) where HER2 overexpression was observed by IHC, SNP results were found to be indicative of t tumors –36. HoweHER2/TOP2A locus. This is in line with previous findings where the complex genomic profiles, with numerous gains and losses, of these tumors were classified as “sawtooth patterns” [HER2/TOP2A locus. In one sample (patient TN8) the only 2 informative SNPs were found to be equivocal, making the overall result with regard to the stability of the HER2/TOP2A locus equivocal.In the TN group, 75 % of the tumor samples (12/16) showed results indicative of allelic instability of the atterns” –28. NineHER2/TOP2A locus, whilst 62 % (8/13) did not. Patient ERPR+ 7 showed discordant results in SNP 3, which was therefore concluded to be equivocal. Although the biological and/or technical rationale behind this observation remains unclear, we believe that it is justified to conclude that this sample harbors HER2/TOP2A allelic instability due the data of the remaining 5 informative SNPs. Beside the HER2 normal tissue, patient ERPR+ 11 also harbored HER2 amplified tumor tissue, which was most likely caused by the presence of a second tumor or tumor heterogeneity. The tissue included in this study, which had a stable HER2/TOP2A locus, was not HER2 amplified and was ERPR positive. This patient was therefore included in the ERPR+ group.Within the ERPR+ group, 38 % of the tumor samples (5/13) yielded results representative of allelic instability of the HER2/TOP2A allelic instability in the TN and ERPR+ group  roughly corresponded to the AC-based chemotherapy response rates found within these groups  [High chromosome instability, detected using FISH, in breast tumors is of prognostic value with regard to clinical outcome and may be useful in predicting response to AC-based therapy , since tctively) . A futurOverall, 8 out of 10 tissues where part of the tumor cells harbored polysomy of chromosome 17, which was detected by the chromosome 17 centromeric probe included in the SISH assay, showed SNP results indicative of allelic instability. The SNP observations however may be the cumulative effect of several genetic aberrations including the polysomia. Reflection of monosomy and trisomy of chromosome 17 in the SNP results was less evident. In all tumors where aneuploidy chromosome 17 was observed, heterogeneity was observed with regard to chromosome 17 status as well.SNP 3 cut-off values were adapted due to its large normal ratio distribution. Employing identically calculated cut-off values for SNP 3 would lead to the unjustified normal/equivocal calling of samples that clearly demonstrate a deviant VIC/FAM delta Rn ratio based on the generated scatterplot (data not shown).HER2/TOP2A locus. Since sample input of these tissues was similar to those of the tumor tissues, PCR inhibition and depletion of reagents, which may occur, do not seem to be of major impact in allelic stability vs instability calling. The same is true, after the correction of delta Rn FAM values as described earlier, for variation in DNA concentration. The single anomaly concerning SNP 11 that was found aberrant in a histologically normal sample from the breast tumor group may be caused by a small deletion, point mutation(s) or a small recombination event around the involved SNP. Although sequencing of this SNP locus of this particular patient is of interest, it is beyond the scope of this study. Although the SNP test performed well, some drawbacks may be encountered. When analyzing a heterogeneous tumor tissue, unjustified (im)balance calling may occur. The resolution per SNP is relatively low, which may lead to an inconclusive result when (a large part of) the employed SNPs are homozygous and therefore non-informative. However, this issue is easily overcome by increasing the number of SNPs to be analysed. Determining the allelic instability of a sample with the SNP assay, as described here, costs approximately €50-€75 (US $55–85) per sample. These costs are comparable to those of FISH based allelic instability testing. Additionally, the SNP test is well suited for the detection of copy number variations, but is not able to distinguish between various underlying mechanisms (e.g. gains vs. losses). SNP assays have a limited sensitivity, around 20 % “mutant” DNA extracted from whole blood is detectable in a “wildtype” background [In the histologically normal tissues, 99.5 % (207/208) of the informative SNPs yielded results indicative of a stable ckground . For FFPckground .To minimize the effect of pipetting inaccuracy and to prevent pipetting errors, manual liquid handling was reduced to a minimum by performing all initial PCR setups using a Hamilton StarLet robot. Any inaccuracies or errors with regard to pipetting could negatively influence the determination of the natural ratio distribution of the histologically normal tissues and ultimately the allelic stability vs. instability calling.HER2/TOP2A locus as a marker has clinical relevance and is of any significance for therapeutic decision making.In summary, the presented SNP methodology is easy to perform when compared to other copy number variation detection techniques such as array based systems targeting SNPs and Comparative Genomic Hybridization and corresponds well to positive SISH results. The real time PCR based SNP test may have an added value in the TN as well as the ERPR+ patient group. Within these groups, 75 % of the TN and 38 % of the ERPR+ tumors showed allelic instability with the SNP based assay. To determine such a possible added value of the presented SNP test a large-scale study should be performed. Ample size TN and ERPR+ patient groups with 5 year clinical follow-up data should be included to assess whether the determination of allelic instability of the Additional file 1: Table S1.HER2/TOP2A locus and the schematic positions of the employed SNP markers. The arrows indicate the location of the HER2 and TOP2A genes. The boxes represent all informative SNPs that were considered not affected (green)/affected (red) by allelic instability or equivocal (grey). Homozygous SNPs are not informative, and are therefore shown as a horizontal line.Patient data and SNP test results from the control group. Patient data and SNP results. The top right represents the Additional file 2: Table S2.in situ hybridization, HER2+ = HER2 overexpression observed based on IHC, TN = triple negative, ERPR+ = estrogen receptor and progesterone receptor positive. Chr17 status = chromosome 17 status with M (monosomy), D (disomy), T (trisomy) and P (polysomy) as possible outcomes supplemented with the number of chromosomes 17 observed per nucleus. Pred. = predominantly and TC% = tumor cell percentage. Failed = SISH repeatedly failed. The top right represents the HER2/TOP2A locus and the schematic positions of the employed SNP markers. The arrows indicate the location of the HER2 and TOP2A genes. The boxes represent all informative SNPs that were considered not affected (green)/affected (red) by allelic instability or equivocal (grey). Homozygous SNPs are not informative, and are therefore shown as a horizontal line.Patient data, tumor characteristics and IHC, SISH and SNP test results from breast tumor group. Patient data, tumor characteristics and IHC, SISH and SNP test results. L and B represent the use of lymph node or breast histologically normal tissue. IHC = immunohistochemistry, SISH = silver


Lens ervoides, a wild relative of cultivated lentil, is an important source of agronomic trait variation. Lens is a member of the galegoid clade of the Papilionoideae family, which includes other important dietary legumes such as chickpea (Cicer arietinum) and pea (Pisum sativum), and the sequenced model legume Medicago truncatula. Understanding the genetic structure of Lens spp. in relation to more fully sequenced legumes would allow leveraging of genomic resources. A set of 1107 TOG-based amplicons were identified in L. ervoides and a subset thereof used to design SNP markers for mapping. A map of L. ervoides consisting of 377 SNP markers spread across seven linkage groups was developed using a GoldenGate genotyping array and single SNP marker assays. Comparison with maps of M. truncatula and L. culinaris documented considerable shared synteny and led to the identification of a few major translocations and a major inversion that distinguish Lens from M. truncatula, as well as a translocation that distinguishes L. culinaris from L. ervoides. The identification of chromosome-level differences among Lens spp. will aid in the understanding of introgression of genes from L. ervoides into cultivated L. culinaris, furthering genetic research and breeding applications in lentil.Lentil  Lens culinaris Medik.; Lc) is the fifth most important grain legume crop globally and an important export crop for Canada. It is cultivated primarily in North America, Australia, the Middle East, Western Asia, and the Indian subcontinent , is a source of resistance to anthracnose races Ct0 and Ct1 , lentil, field pea (Pisum sativum L.) and faba bean (Vicia faba L.). Most of the genes in papilionoid legume species are likely to be found within syntenic regions ranging in size from hundreds of kilobases to several megabases with respect to any other given papilionoid species  and soybean (Glycine max) transcripts . The fact that TOGs have retained their mostly single-copy nature during speciation, they can be helpful in the study of genome structure and evolution across species    have been used to derive markers from a set of low-copy orthologous genes  present in a legume expressed sequence tag (EST) dataset of Mt, lotus  determine macrosyntenic relationships between wild and cultivated lentil and with those of closely related galegoid legumes.This study was undertaken to capitalize on the recent development of genomic resources in lentil, with objectives to: (i) sequence intron spanning amplicons from the legume TOGs in lentil, (ii) generate TOG-based SNP genotyping assays, (iii) develop a SNP-based genetic map of Le accessions IG 72815 (from Turkey) and L01-827a  and L01-827a (Le). A F7:8 RIL population of 68 individuals had already been developed  using Le genotypes IG 72815 and L01-827a, and Lc genotypes CDC Redberry and Eston as DNA templates. Sequencing from both 3′- and 5′-ends using forward and reverse primers was carried out and the resulting forward and reverse read amplicon sequences were first assembled for each genotype using CAP3 , a set of 9575 validated SNPs was submitted to Illumina for Assay Design Tool (ADT) scoring. Only SNPs with an ADT score of 0.5 or higher were considered for OPA design. SNPs were examined for informativeness based on anticipated polymorphism between Lc and Le or Le and Le mapping populations, and to maximize the number of TOGs covered. A set of 768 SNPs were selected from this filtering and sent to Illumina for the synthesis of the Le768 SNP OPA assay.2 individuals from the LR-66 population and 68 LR-59 RILs were genotyped using this Le768 OPA and the standard Illumina GoldenGate Assay protocols (http://www.illumina.com/technology/goldengate_genotyping_assay.ilmn). The products generated by this assay were read with an Illumina iScan . The resulting data were clustered for allele calling using GenomeStudio software version 2010.3 . Allele calls were manually checked for errors and corrected as necessary or scored as missing data to exclude their further use. To avoid additional technical errors, all SNPs showing unexpected parental alleles or high levels of missing data (>10%) were excluded from further analysis.The 91 Fhttp://www.lgcgenomics.com/kasp-genotyping-reagents). Assays were carried out in a 10 μL reaction volume consisting of 50 ng/μL DNA, KASP 1X Reaction Mix, and 0.17 μM KASP Assay Mix . PCR amplification was carried out in StepOnePlus™ Real-Time PCR System (Applied Biosystems) and fluorescence was analyzed using StepOne Software version 2.1 (Applied Biosystems). Allele calling was done graphically as scatter plots for each marker using SNPViewer .An additional 31 gene-based SNPs, polymorphic between L01-827A and IG 72815, were identified through a separate project  for assay design and genotyping on the parents of mapping populations, including LR-66.2 individuals were genotyped using the Le768 OPA and KASP assays. Polymorphic loci were used for mapping using the regression mapping function in JoinMap 4.0  FMedicago genome (version 3.5.1) to facilitate the comparison. Homologs between Le and Mt as well as Lc and Mt were identified using the NUCmer pipeline of the MUMmer software .A comprehensive genetic map of Lc based on the mapping population LR-18 (Lc × Lc) developed by Fedoruk et al.  was compGenotypic data for the interspecies RIL population, LR-59 , were used to further examine putative translocations. Common markers for Le linkage groups (LG) LeLG1, LeLG4, LeLG5, and LeLG7 were selected and their order across individuals the Le × Lc inter-specific population LR-59 examined visually.http://cmap.comparative-legumes.org) for chickpea and Mt. Map units for lentil and chickpea were converted to base pair distances to facilitate comparisons with Mt at an assumed factor of 250,000 bp per cM. Inter-genomic comparisons were drawn using Circos, which is a Perl script based visualization tool to facilitate the identification and analysis of synteny and differences between the genomes arising from comparative analysis . For a better comparison of the translocation on Lc . Of these, 2513 were reported as indels or multiple nucleotide polymorphisms (MNPs). The remaining 7062 SNPs were found to be polymorphic in either of the four genotypes and could be present in any of the combinations derived from these genotype  scoring, only 1470 scored higher than 0.5 and were considered for possible inclusion in the OPA design. The 1470 SNPs represented polymorphism in 765 non-redundant genes. For each gene, a single SNP, maximally polymorphic among the four genotypes, was chosen for inclusion in a 768-SNP GoldenGate assay. The majority of SNPs (81.8%) were polymorphic between at least one Lc and at least one Le genotype, 61.6% of the SNPs were polymorphic between the two Le genotypes, and 16.7% were polymorphic between the two Lc genotypes Figure . The mosp < 0.01) and all were skewed toward the Le parent, IG 72815. Of the 31 SNPs from Sharpe et al. .Of the 768 SNPs genotyped via the GoldenGate assay on the LR-66 population, 66 (8.6%) failed and an additional 61 (7.9%) were difficult to score . Only 29 marker loci (8%) were severely distorted  and could be mapped along with additional SNPs  had orthologs located on the eight chromosomes of Mt and 128 of these had orthologs that also mapped to the eight chromosomes of chickpea . When the maps of the three species were aligned, extensive stretches of shared collinearity among them were evident Figure . There iThe 128 TOGs mapped in Ca, Mt and Le, covered all Ca chromosomes with 8–22 TOGs per chromosome and corresponded to loci on all 7 Le linkage groups with 9–21 TOGs per linkage group . As was seen with the Le-Mt comparison, there were large stretches of conserved synteny between Le and Ca . The additional chromosome in Ca appears to result from a splitting into chromosomes 5 and 6 of the ancestral chromosome that corresponds to LeLG5 into Ca.Lens species.A high degree of collinearity was observed for the two lentil species in comparison with Mt, but a few key differences were also apparent Figure . Most noGenotypic data from one of the linkage groups from the Lc × Le interspecies introgression population, LR-59, were significantly distorted (69% skewed) toward the Lc parent alleles and aligned with portions of both LeLG1 and LeLG5  were the marker systems of choice for lentil molecular marker based linkage studies. With the advent of high-throughput sequence technology, thousands of SNPs have now been identified in lentil germplasm  and two Lc (cultivated lentil) accessions. These SNPs were either polymorphic in cultivated lentil (Lc-Eston vs. Lc-Reberry), wild lentil (Le-L01-827a vs. Le-IG72815) or cultivated vs. wild . As expected, due to the occurrence of the same SNP in multiple groups, the total number of the SNPs from each group has exceeded the final SNPs  that did not match with the Sanger sequence genotypes for the two accessions and a further 20 loci genotyped as heterozygous in one or other of the mapping parents but not reported as such in the Sanger data. Heterogeneity of parental seed stocks or residual heterozygosity in plants used for DNA sequencing vs. those used to develop the LR-66 population may underlie such genotypic differences. Another problem encountered with 61 loci, caused by trying to map an F2 population, was the difficulty in distinguishing heterozygotes from homozygotes. Genotypes for these loci were not included in the mapping data.In LR-66, a Le × Le F2 individuals of a cross between two wild Le accessions, L01-827a and IG 72815. The map consists of seven linkage groups, which likely represent the seven chromosomes of Le. Many of the early lentil maps were constructed using populations derived from inter-specific crosses between cultivated lentil and a wild parent from the secondary gene pool  and isozyme markers . The first 5 cM of LcLG1 have Homologs from Mt chromosome 5. There is a major gap of 16.7 cM in this region in Lc, which likely reflects a loss of chromosomal segment or re-arrangement in the Lc genome. Similarly for LcLG5, a group of markers that mapped between 64.4 cM and 100.2 cM corresponds to Homologs from Mt chromosome 1. No such translocation was evident in Le relative to Mt  to Kirstin E. Bett, Albert Vandenberg, and Sally L. Vail and from the US National Science Foundation DBI 0605251 to Douglas R. Cook.The Associate Editor Paul Gepts declares that, despite being affiliated to the same institution as authors Sally L. Vail, Noelia Carrasquilla-Garcia, R. Varma Penmetsa and Douglas R. Cook, the review process was handled objectively and no conflict of interest exists. The authors declare that the experiments of this study comply with the current laws. We confirm to have the authority to publish this work and that the manuscript has not been published before and is not under consideration for publication elsewhere. 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.


MAR (%) was introduced for NGS data analysis for the first time, evaluating allelic performance, sensitivity testing and mixture testing. FMAR values for accurate heterozygotes should be range from 50% to 60%, for homozygotes or Y-SNP should be above 90%. SNPs of rs7520386, rs4530059, rs214955, rs1523537, rs2342747, rs576261 and rs12997453 were recognized as poorly performing loci, either with allelic imbalance or with lower coverage. Sensitivity testing demonstrated that with DNA range from 10 ng-0.5 ng, all correct genotypes were obtained. For mixture testing, a clear linear correlation (R2 = 0.9429) between the excepted FMAR and observed FMAR values of mixtures was observed.SNPs, abundant in human genome with lower mutation rate, are attractive to genetic application like forensic, anthropological and evolutionary studies. Universal SNPs showing little allelic frequency variation among populations while remaining highly informative for human identification were obtained from previous studies. However, genotyping tools target only dozens of markers simultaneously, limiting their applications. Here, 124 SNPs were simultaneous tested using Ampliseq technology with Ion Torrent PGM platform. Concordance study was performed with 2 reference samples of 9947A and 9948 between NGS and Sanger sequencing. Full concordance were obtained except genotype of rs576261 with 9947A. Parameter of F Since no data of this panel has been published yet, we evaluated the panel and explored the application in Chinese HAN population this time.Since universal SNPs showing little allelic frequency variation among populations while remaining highly informative for human identification were obtained from previous studies1112The main experiments were conducted in Forensic Genetics Laboratory of Institute of Forensic Science, Ministry of Justice, P.R. China, which is an accredited laboratory by ISO 17025, in accordance with quality control measures. All the methods were carried out in accordance with the approved guidelines of Institute of Forensic Sciences, Ministry of Justice, P.R. China.Control DNA of 9947A (Life Technologies) and 9948 (Promega) were adopted as reference samples. Human blood samples involved for study were collected with the approval of Ethics Committee of Institute of Forensic Sciences, Ministry of Justice, P.R. China. Informed consent was obtained for each participant. DNA was extracted using QIAamp® DNA Blood Mini Kit . The quantity of DNA was determined by Quantifiler Human DNA Quantification Kit (Life Technologies) with 7500 Real-time PCR System (Life Technologies).Primers of 124 universal SNPs  were combined in one primer pool. For the 90 auto-SNPs, 43 Ken Kidd SNPsTM Barcode Adapters (Life Technologies). After the ligation with barcodes, libraries were purified with Agencourt AMPure XP Reagents . Then qPCR methods with Ion Library Quantitation kit (Life Technologies) was adopted for accurate library quantification.New technology of Ampliseq and according chemistry of Ion AmpliSeq Library Kit 2.0–96 LV (Life Technologies) were applied for library preparation. Ampliseq technology delivers simple and fast library construction for affordable targeted sequencing of genomic regions. The Ion AmpliSeq™ workflow is based on a transformative technology that simplifies ultrahigh-multiplex PCR amplification and library construction. Utilizing low input DNA, this single-tube workflow is as simple as setting up a PCR reaction and can avoid contamination. The library-PCR system contained 4 μL of 5X Ion AmpliSeq HiFi Master Mix and 10 μL of 2X HID-Ion AmpliSeq™ SNP-124 Panel. Except for the sensitivity and mixture testing of the panel, the initial DNA input was 10 ng for each library construction. The library-PCR parameters were as follows: 2 min at 99°, 18 cycles of 15 s at 99° and 4 min at 60° followed by a 10° hold. For sensitivity testing, 21 cycles were used when DNA amount lower than 1 ng in order to get sufficient amplification. The resulting amplicons were treated with 2 μL FuPa reagent (Life Technologies) to partially digest primers. All libraries were barcoded using Ion XpressTM Particles (ISP) containing clonally amplified DNA with emPCR technology, which performed on Ion OneTouch2 (OT2) (Life Technologies) by using Ion PGM Template OT2 200 Kit. Quality of emPCR products were evaluated with Ion SphereTM Quality Control Kit (Life Technologies). The optimal amount of library corresponds to the library dilution point that gives percent of template ISPs between 10–30%. The emPCR products were then enriched on the Ion OneTouchTM ES (Life Technologies).The accurately quantified and diluted pool library was then used to generate template positive Ion SphereTM Sequencing 200 Kit v2. Ion Chip types  varied depending on the sample size. Assuming 80% chip loading and 60% usable chip, 8, 38 and 77 samples can been sequenced on 314, 316 and 318 chip. Raw sequencing data were collected as DAT files which were processed on the Ion Torrent Suite Server (v4.0.2). Signal processing, base-calling and barcode de-convolution were performed with Server v4.0.2. HID_SNP_Genotyper.42 (v4.2), Variant Caller (v4.0-r76860) and Coverage Analysis (v4.0-r77897) plug-ins were adopted for data analysis. The reference genome was Hg19.NGS was performed on Ion Torrent PGM (Life Technologies) with Ion PGMFor the concordance and accuracy testing, female sample of 9947A (Life Technology) and male sample of 9948 (Promega) were applied as reference samples. 124 SNPs of the two reference samples were sequenced by NGS and Sanger technologies. Here, Sanger method was adopted for validation of NGS results. For the NGS fragment above 150 bp, same primer pair was used for Sanger sequencing; for the NGS fragment below 150 bp, different primer pair was designed for Sanger sequencing (listed in Control samples of 9947A (Life Technology) and 9948 (Promega) were chosen for concordance study. 124 SNPs (listed in MAR (Frequency of Major Allele Reads) was adopted here. Analysis with HID_SNP_Genotyper.42 (v4.2) plug-in can provide detail reads at each bases  for each SNP. FMAR was calculated as the biggest reads among the four bases dividing the total detected reads. For homozygotes, the optimal FMAR (%) should be equal to 100, while for heterozygotes, the optimal FMAR (%) should be 50. In previous study, Intra-locus balance (the lower peak height dividing the higher peak height for each locus) was applied to measure the balance of heterozygous alleles. According to Eduardoff et al., 50% is a ‘perfect balance’ and 40% threshold (60:40 heterozygote ratio) can give better equilibrium between gaining the highest proportion of reliable genotypes and balanced signals of SNPs. That means the FMAR (%) for accurate heterozygotes should be 50%–60%. And Intra-locus balance above 70% is desired to ensure accurate heterozygote genotyping and to facilitate mixture interpretation for STRsMAR (%) for accurate heterozygotes should be ranged from 50% to 59% (1/(0.7 + 1)). Therefore, the boundaries of FMAR (%) was set as 50–60% for ideal allelic-balance of heterozygotes in this study. And according to Eduardoff et al., SNPs with major allelic reads frequencies of 90% or greater were deemed to be homozygous for that alleleMAR (%) reach >90%, samples cannot be mistyped as heterozygotes. In other words, FMAR (%) values above 90% were recognized as ideal for homozygotes. For the 34 Y-SNPs, only one allele can been detected for each locus. So the FMAR (%) value above 90% is also essential for accurate calling of Y-SNPs. Analyzing the FMAR (%) values of MAR (%) for the detected 33 heterozygotes (except wrong genotype at rs576261) was 52.16. No values of FMAR (%) for heterozygotes was above 60%. While for the FMAR (%) data of MAR (%) values above 60%. For all the detected homozygotes and Y-SNPs, FMAR (%) values above 90%.A parameter of FMAR above 60% does not equal to wrong calling. To be fully evaluated the Panel, 10 ng initial DNA of 45 unrelated individuals with barcode ‘1–45′ were sequenced on 318 Ion chip twice for intensive study. Identical genotyping results were obtained from the two times of NGS sequencing except at rs7520386 and rs214955 (MAR (79.05%) was detected; and for the wrong genotyping of rs214955 of sample ‘A12_045′, lower coverage (<100) was observed. These suggested that attention should be paid to data with lower coverage or abnormal values of FMAR. By analyze all the NGS data of the 45 individuals, imbalance of heterozygotes were found at SNPs of rs7520386, rs4530059, rs214955, rs1523537, rs2342747 and rs576261 (MAR (%) values above 60%. Except the 6 SNPs, all the detected FMAR (%) values of the 45 individuals were plotted in MAR (%) values were 50%–60% for heterozygotes and 90%–100% for homozygotes and Y-SNPs. And a minimum threshold of 100× coverage was recommended in this study.Above concordance study demonstrated the accuracy rate of SNP typing by PGM sequencing and also suggested several noticeable SNPs. The genotype calling for heterozygote with Frs214955 . Sanger rs576261 . Among tMAR (%) values above 60% or total coverage below 100x analyzed with HID_SNP_Genotyper.42 (v4.2) plug-in should be checked or discarded. In this study, the 6 SNPs  detected with abnormal values of FMAR and SNP of rs2342747, rs12997453 with lower coverage were recognized as poorly performing SNPs. SNP rs2342747 always detected with abnormal values of FMAR and lower coverage maybe should be deleted from the panel.There is consistently high coverage with little variation between the samples. However, variation in coverage was observed among the SNPs and each SNP generally showed similarly high or low coverage across the samples. The lower coverage of auto-SNPs were always happened at SNP rs2342747 and rs12997453, which is also observed when genotyping of 9947A and 9948 . The difMAR (%) values were in the range for heterozygotes and homozygotes. Therefore, modification of the primers of ‘problematic SNPs’ may effectively improve the performance of new panel.In the previous Inter-laboratory evaluation of the HID_SNP_ v2.2 with 169-markers for ancestry inference, discordant genotypes detected in 5 SNPs  indicate these loci should be excluded from the panelSerial dilutions of 10 ng-0.2 ng of a control male DNA sample were made and dilutions were sequenced on Ion 314 chip. Concordant genotyping results were obtained from all these samples except DNA of 0.2 ng at rs2342747 with ‘N/N’. The total reads at this locus was only 20.MAR (%) values of heterozygotes of the 90 auto-SNPs for each dilution were shown in MAR (%) values for heterozygotes. Some terrible FMAR data of heterozygotes were observed when DNA ranged from 0.2–1 ng, especially when DNA below 0.5 ng , which indicated that the assay generated a loyal representation of DNA samples. Detection of mixtures with auto-SNPs is possible by analyzing the FMAR values with NGS data. NGS data can give balanced heterozygous genotypes, providing a more secure basis for analyzing mixtures. It is vital to reliably recognize SNP data as originating from a mixture and not a single profile with the commonly used SnapShot system.In this study, mixtures of two reference samples (9947A and 9948) with ratios of 100:1, 10:1, 5:1, 1:1, 1:5, 1:10 and 1:100 were studied. The genotyping results of 34 Y-SNPs of the mixtures were listed in Table S5. 14.71%, 97.06% and 100% of the Y-SNPs were detected in the 100:1, 10:1 and 5:1 mixture of 9947A/9948. For other mixture ratios, 100% of the Y-SNPs were detected.A total of 45 unrelated individuals  of Chinese HAN population were sequenced with the HID-Ion SNP124 panel on Ion 318 chip twice. All the genotypes obtained at the 7 poorly performing SNPs were checked by Sanger sequencing.−23 for the 90 auto-SNPs in Chinese HAN. For the Y-SNP analysis, 6 haplotypes were found in the 28 unrelated male individuals. These suggested that the HID-Ion SNP124 panel is suitable for personal identification of HAN population from China.Genetic analysis of these 124 SNPs was performed with SNP Analyzer SoftwareMAR (%) was applied for evaluating allelic performance, sensitivity testing and mixture testing, making the NGS data easy to interpret. Further modification of the panel can been explored based on the obtained data. This pilot study of the Ion Torrent PGM Sequencer has demonstrated considerable potential for SNP detection as a low to medium throughput NGS platform. And although capillary electrophoresis remains the gold standard and most cost-effective option for human identification with short tandem repeats (STRs)1610NGS plus Ampliseq technology have the capacity to sequence targeted regions of multiple DNA samples with high coverage simultaneously. Compared with Sanger sequencing, this technology also can reduce labor and cost on a per nucleotide bases and indeed on a per sample basis. In this study, with the commercially available SNP panel, high coverage and high throughput of 124 specified targets were detected. The parameter of FHow to cite this article: Zhang, S. et al. Parallel Analysis of 124 Universal SNPs for Human Identification by Targeted Semiconductor Sequencing. Sci. Rep.5, 18683; doi: 10.1038/srep18683 (2015).


Modern horses represent heterogeneous populations specifically selected for appearance and performance. Genomic regions under high selective pressure show characteristic runs of homozygosity (ROH) which represent a low genetic diversity. This study aims at detecting the number and functional distribution of ROHs in different horse populations using next generation sequencing data.Next generation sequencing was performed for two Sorraia, one Dülmen Horse, one Arabian, one Saxon-Thuringian Heavy Warmblood, one Thoroughbred and four Hanoverian. After quality control reads were mapped to the reference genome EquCab2.70. ROH detection was performed using PLINK, version 1.07 for a trimmed dataset with 11,325,777 SNPs and a mean read depth of 12. Stretches with homozygous genotypes of >40 kb as well as >400 kb were defined as ROHs. SNPs within consensus ROHs were tested for neutrality. Functional classification was done for genes annotated within ROHs using PANTHER gene list analysis and functional variants were tested for their distribution among breed or non-breed groups.YES1. In the Hanoverian, a total of 18 private ROHs could be shown to be located in the region of genes potentially involved in neurologic control, signaling, glycogen balance and reproduction. Comparative analysis of homozygous stretches common in all ten horses displayed three ROHs which were all located in the region of KITLG, the ligand of KIT known to be involved in melanogenesis, haematopoiesis and gametogenesis.ROH detection was performed using whole genome sequences of ten horses of six populations representing various breed types and non-breed horses. In total, an average number of 3492 ROHs were detected in windows of a minimum of 50 consecutive homozygous SNPs and an average number of 292 ROHs in windows of 500 consecutive homozygous SNPs. Functional analyses of private ROHs in each horse revealed a high frequency of genes affecting cellular, metabolic, developmental, immune system and reproduction processes. In non-breed horses, 198 ROHs in 50-SNP windows and seven ROHs in 500-SNP windows showed an enrichment of genes involved in reproduction, embryonic development, energy metabolism, muscle and cardiac development whereas all seven breed horses revealed only three common ROHs in 50-SNP windows harboring the fertility-related gene The results of this study give a comprehensive insight into the frequency and number of ROHs in various horses and their potential influence on population diversity and selection pressures. Comparisons of breed and non-breed horses suggest a significant artificial as well as natural selection pressure on reproduction performance in all types of horse populations.The online version of this article (doi:10.1186/s12864-015-1977-3) contains supplementary material, which is available to authorized users. The modern horse population represents a particularly heterogenous group influenced over the time by various selective pressures . HoweverST-based statistics in 500-kb windows and revealed common haplotypes in the region of coat color genes, size and performance traits. The highest signature of selection was found in the Paint and Quarter Horse on ECA18 in the region of the myostatin gene (MSTN). Positively selected loci for performance have also been detected in a Thoroughbred population study based on microsatellite markers [In horses, signals of selection have been investigated in 744 horses of 33 breeds using whole-genome single nucleotide polymorphism (SNP) array data . Potenti markers . CandidaSignals of selection have also been investigated in other mammals including cattle, dog, pig and human, frequently scanning ROHs as diversity indices –11, 13. pleiomorphic adenoma gene 1 (PLAG1). Further breed specific ROH analyses for Chinese and Western pigs revealed loci under selection important for high-altitude adaption in Tibetan pigs as well as a coat color locus in the region of endothelin receptor type B (EDNRB) in Chinese belted pigs [In domestic animals, especially performance related phenotypes and breed specific characteristics were mainly in focus of ROH analyses , 20. A wted pigs .Signatures of selection affecting coat color and body size traits could also be observed in genome-wide ROH scans for dogs . It was In this study, whole-genome sequences of ten horses were used for analysis of ROHs in a sliding window approach. 50-SNP and 500-SNP windows were chosen for reliable detection of ROHs of different sizes. ROHs exclusively found in individual horses or breeds were further investigated for their gene content potentially affected by targeted selection for specific appearance and function.Whole-genome sequences of a Dülmen Horse, two Sorraia, an Arabian, a Saxon-Thuringian Heavy Warmblood descendent from the Old-Oldenburg breed, a Thoroughbred and four Hanoverian were obtained using NGS. Mapping to the reference genome EquCab2.70 resulted in a mean coverage of 19.90X for the Dülmen Horse, 17.34X and 17.55X for the two Sorraia, 19.14X for the Arabian, 20.06X for the Saxon-Thuringian Heavy Warmblood, 5.92X for the Thoroughbred and 15.66X-35.18X for the four Hanoverian , metabolic processes (GO:0008152) as well as biological regulations (GO:0065007), localization (GO:0051179) and developmental processes GO:0032502) in all private ROHs of the analyzed ten horses (Table 2502 in adyslexia susceptibility 1 candidate 1 (DYX1C1), protein phosphatase 1, regulatory (inhibitor) subunit 14C (PPP1R14C), cilia and flagella associated protein 61 (CFAP61/C20orf26), cysteine sulfinic acid decarboxylase (CSAD), TBC1 domain family, member 30 (TBC1D30) and ALX homeobox 4 (ALX4), which were shown to be related by direct genetic interactions or co-expression , fraser extracellular matrix complex subunit 1 (FRAS1), interleukin-1 receptor-associated kinase 1 binding protein 1 (IRAK1BP1), pleckstrin homology domain interacting protein (PHIP), acyl-CoA synthetase short-chain family member 3 (ACSS3), protein tyrosine phosphatase, receptor type, f polypeptide, interacting protein (liprin), alpha 2 (PPFIA2) and did also cover a gene-rich region which included spermatogenesis associated 25 (SPATA25), acyl-CoA thioesterase 8 (ACOT8) and troponin C type 2 fast (TNNC2). ROH detection in 500-SNP windows revealed seven common ROH regions for non-breed horses. They were located on horse chromosomes 22 and 28 in or near by ROHs already found in 50-SNP windows. The largest region showed a size of 576,454 base pairs. In contrast to non-breeds, the whole group of breed horses  revealed only three common ROHs in 50-SNP windows and no common ROHs in 500-SNP windows .Analysis of shared private ROHs in specific breed horses revealed 18 ROHs common in all four Hanoverian but not in the other analyzed horses in 50-SNP windows and no shared ROHs in 500-SNP windows Table . The 18  CFAP61/C0orf26, cKIT ligand  was predicted to be deleterious (SIFT score 0.01) whereas the other three variants located in Elongation Of Very Long Chain Fatty Acids Protein 4 (ELOVL4), Phosphotyrosine Picked Threonine-Protein Kinase (TTK) and ACSS3 were proposed to be tolerated . In contrast to non-breeds, breed horses harbored no variants with high or moderate effects in private ROHs. Nevertheless, the four Hanoverians could be shown to harbor four SNPs in their private ROH regions in the genes DYX1C1, CSAD and in the novel gene ENSECAG00000004438 -knockout mice, a reduce response to repeated morphine injections suggested an important role of KEPI in the regulation of analgesic tolerance [KEPI was shown to be expressed in brain regions of drug reward, locomotor control and nociception [CFAP61 which was shown to affect cilia and flagella motility [YES1 could be shown to be an essential protein tyrosine kinase for self-defensive mechanisms in spermatocytes [YES1 was supposed to antagonize apoptotic processes to maintain spermatogenic differentiation and male fertility. In addition to that, it was even more intriguing that ROH analyses in non-breed horses also suggested a high positive selection for reproduction in mainly naturally selected horses. One of the largest ROHs could be shown to harbor the fertility related gene SPATA25 which is known to be mainly expressed in testis in human. Studies of obstructive azoospermia revealed a significantly reduced expression level in affected patients in comparison to fertile persons [FRAS1 deficient mice revealed phenotypic defects affecting embryonic epithelial basement membranes and internal organs [ [(NEURL2) [SFRP2 [SFRP2 plays an important role in myocardial survival and is involved in ischemic injury repair of cardiomyocytes. The assumption of a potential non-breed specific effect on myocardial regulation for greater endurance in free range conditions was supported through the detection of a functional variant with a possibly deleterious impact on HRC. It was suggested that different expression levels of HRC can affect CA2+ homeostasis and contractile function of the heart [In contrast, the four Hanoverian sport horses in our study showed a low number of ROHs and relatively low values for Fand mice , 36. Neuigration –39. The  peptide . Compara[TBC1D30 . In KEPIolerance . KEPI waiception , 43. Furiception . A signimotility , 46. It atocytes . During  persons . Other cl organs . Further, ACOT8) , 51 and (NEURL2) , 53 coul) [SFRP2 . It was he heart , 56. In he heart .Despite the specific genetic features in non-breeds as well as breeds and the general differences in the number and length of ROHs in various horse breeds, a functional enrichment of genes affecting cellular, metabolic and developmental as well as immune system and reproduction processes could be shown in ROHs in all ten horses.These results suggest that despite the low number of individuals in some breeds or non-breeds these ten horses presumably represent a general phenomenon in horse populations. We assume that regions of genes involved in fundamental processes essential for development and sustainment of individuals and populations underlie high selective pressures and accordingly limited variations. A main focus which could be found in all breeds, specific breeds (Hanoverian) and also in non-breeds was a potential selection for traits of reproduction. Essential genes for processes affecting fertility, embryonic development and birth varied among different horse populations but could be assumed to play a key role in artificial or natural selection as well. Reproduction performance has been shown to be of high economic importance in breeds and of vital importance for non-breeds to ensure survival in the wild , 58. VarKITLG, also known as Mast Cell Growth Factor, Stem Cell Factor or steel factor [KITLG locus to be a breed-specific signature in the Berkshire [KITLG has fundamental impact on various essential processes affecting melanogenesis, haematopoiesis and gametogenesis [KITLG and its receptor KIT were shown to affect multiple cell formation stages parallelly during embryonic development and in fully-grown mice [KITLG locus resulted in anemic black-eyed mice of white color with pigmented ears and scrotum and caused sterility in females. In human, a significant association for male infertility could be detected in KITLG affecting sperm count in patients [KITLG (KIT) was suggested to encode the dominant white (W) locus and to initiate severe disorders in haematopoietic system which might be responsible for the lethal consequences of homozygous W/W-genotype [KITLG mutations particularly affecting traits of reproduction and development have led to a strong positive selection of this region in horses that resulted in long ROHs.This assumption is supported by our detection of three consensus ROHs in all ten horses which harbor only one annotated gene, the l factor , 60. Scaerkshire . Due to ogenesis , 62, 63.own mice . The Stepatients . In horsgenotype . It was The results of our study suggest that despite significant differences in-between breed and non-breed horses with regard to functional traits, all horse populations show strong signatures of selection in the region of genes affecting traits of reproduction.All animal work has been conducted according to the national and international guidelines for animal welfare. The EDTA-blood sampling was approved by the Institutional Animal Care and Use Committee (IACUC), the Lower Saxony state veterinary office at the Niedersächsisches Landesamt für Verbraucherschutz und Lebensmittelsicherheit, Oldenburg, Germany .Sequencing analysis was based on data from two Sorraia, one Dülmen Horse, one Arabian, one Saxon-Thuringian Heavy Warmblood, one Thoroughbred and four Hanoverian. Among these horses, six whole-genome sequences from two Hanoverian (SRX389480/SRX389477), one Arabian (SRX389472), one Sorraia (SRX389475), one Dülmen Horse (SRX384479) and one Thoroughbred (SRR1055837) were obtained from the Sequence Read Archive (NCBI). The remaining samples of a Sorraia mare, a Saxon-Thuringian Heavy Warmblood and two Hanoverian stallions were prepared for whole-genome sequencing. DNA was extracted from white blood cells derived from EDTA-blood sampling using Invisorb Spin Blood Mini kit according to the manufacturers’ protocol . Paired-end libraries of the two Hanoverian were prepared using the Illumina DNA sample preparation kit . DNA-samples were sheared on the Covaris  and purified with Agencourt AMPure XP beads . The remaining two samples (Sorraia and Saxon-Thuringian Heavy Warmblood) were prepared using the Illumina Nextera DNA Sample Prep Kit according to the manufacturers’ protocol and purified with Agencourt AMPure XP beads as well. The whole genome of both Hanoverian was sequenced using an Illumina HiSeq2000 (Illumina) in paired-end mode (2 × 101 bp reads), whereas the Sorraia and Saxon-Thuringian Heavy Warmblood were run on an Illumina MiSeq with v2 Reagent Kits (2 x 250 bp reads) four times paired-end on a single lane flowcell to reach an adequate coverage for whole genome sequencing.http://picard.sourceforge.net, version 1.130). Local realignment around INDELs, quality score recalibration and SNP calling was performed using GATK [Quality control of FASTQ-files was done using fastqc 0.11.3 . Reads wing GATK . In ordeing GATK . The VCFing GATK .www.animalgenome.org (10horses.recode.vcf.gz). Raw data can be downloaded at the NCBI Sequence Read Archive (http://www.ncbi.nlm.nih.gov/sra), BioProject ID PRJNA291776 (Submission ID: SUB1048258).The data file for all ten horses re-sequenced is available at http://pngu.mgh.harvard.edu/purcell/plink/, [ROH) were estimated for each horse dividing the size of ROHs in bp by the length of the genome  covered with SNPs.ROHs were detected using a trimmed dataset of 11,325,777 SNPs with a minimum read depth of 3, a maximum read depth of 60 and a minimum mean read depth of 12 for all ten samples. The X chromosome was omitted for this analysis. We defined ROHs as homozygous regions in sliding windows of 50 SNPs in a first run and 500 SNPs in a second approach using PLINK, version 1.07 . HomozyIn addition to that, theta estimations and neutrality test statistics Tajima’s D, Fu&Li F’s, Fu&Li’s D, Fay’s H, Zeng’s E were obtained using ANGSD version 0.902 . AnalyseIn addition to whole-genome sequencing, two horses (Hanoverian) of a previous study  and threhttps://usegalaxy.org/) [Gene lists of horse specific ROH regions were obtained using SAS/Genetics for filtering PLINK summary files and Galaxy intersection tool (xy.org/) –77 for gxy.org/) , 79. PANxy.org/)  was perfxy.org/) .Functional variants with high or moderate effects were evaluated using SAS/Genetics for filtering SNPEff predictions categorized into high, moderate and low variant impacts. We determined the distribution of genotypes in relation to breed or non-breed groups and detected SIFT  predicti


In contrast to currently used single nucleotide polymorphism (SNP) panels, the use of whole-genome sequence data is expected to enable the direct estimation of the effects of causal mutations on a given trait. This could lead to higher reliabilities of genomic predictions compared to those based on SNP genotypes. Also, at each generation of selection, recombination events between a SNP and a mutation can cause decay in reliability of genomic predictions based on markers rather than on the causal variants. Our objective was to investigate the use of imputed whole-genome sequence genotypes versus high-density SNP genotypes on (the persistency of) the reliability of genomic predictions using real cattle data.Highly accurate phenotypes based on daughter performance and Illumina BovineHD Beadchip genotypes were available for 5503 Holstein Friesian bulls. The BovineHD genotypes  of each bull were used to impute whole-genome sequence genotypes  using the Beagle software. Imputation was done using a multi-breed reference panel of 429 sequenced individuals. Genomic estimated breeding values for three traits were predicted using a Bayesian stochastic search variable selection (BSSVS) model and a genome-enabled best linear unbiased prediction model (GBLUP). Reliabilities of predictions were based on 2087 validation bulls, while the other 3416 bulls were used for training.Prediction reliabilities ranged from 0.37 to 0.52. BSSVS performed better than GBLUP in all cases. Reliabilities of genomic predictions were slightly lower with imputed sequence data than with BovineHD chip data. Also, the reliabilities tended to be lower for both sequence data and BovineHD chip data when relationships between training animals were low. No increase in persistency of prediction reliability using imputed sequence data was observed.Compared to BovineHD genotype data, using imputed sequence data for genomic prediction produced no advantage. To investigate the putative advantage of genomic prediction using (imputed) sequence data, a training set with a larger number of individuals that are distantly related to each other and genomic prediction models that incorporate biological information on the SNPs or that apply stricter SNP pre-selection should be considered.The online version of this article (doi:10.1186/s12711-015-0149-x) contains supplementary material, which is available to authorized users. Genomic selection is increasingly applied in breeding programs for livestock and plant species, e.g. –4. GenomOn average, increasing the number of SNPs in a panel increases the level of LD between a SNP and a QTL and this should be beneficial for genomic prediction. Studies using real data, have shown that genomic prediction using an array with approximately 777,000 (imputed) SNPs resulted in a small gain in genomic prediction reliability compared to an array with approximately 50,000 SNPs –10. HoweHowever, identifying the QTL and obtaining a higher persistency of reliabilities of genomic predictions over generations probably requires a large training set of thousands of sequenced individuals. Without a large number of training individuals, QTL effects might be estimated with too much error and thus, there will be little advantage of using sequence data . SequencThe 1000 bull genome project  aims at Drosophila melanogaster, although the size of the training set size (~120 observations) was relatively small. Moreover, the advantage of Bayesian methods over GBLUP was shown to be greatly influenced by the size and distribution of the simulated QTL effects [Many methods are available for genomic prediction, most of which are based on linear regression  the reliability of genomic predictions based on imputed whole-genome sequence genotypes versus 777k SNP genotypes for real dairy cattle data.EBV, represents the effective number of daughters with phenotypes that contributed to the estimated breeding value of a bull [α ∗ RELEBV/(1 − RELEBV), where RELEBV is the published reliability for EBV and α = (4 − h2)/h2, where h2 is the heritability of the trait. EDCprog = EDCEBV − EDCPA, where EDCPA = αRELPA/(1 − RELPA) and RELPA = (RELsire + RELdam)/4 [EBV increase. The average EDCEBV (and its range) for animals in the training set was equal to 266 (24–971) for SCS, 643 (47–4851) for IFL, and 245 (24–693) for PY. The pedigree information for the 5503 bulls in this study included 39,917 animals.De-regressed proofs and associated weights  were available for somatic cell score (SCS), interval between first and last insemination (IFL), and protein yield (PY) for 5503 Holstein Friesian bulls provided by CRV . De-regressed proofs (DRP) were calculated according to VanRaden et al. :\documenf a bull  and was f a bull  as α ∗ RELdam)/4 . As the In total, 551 bulls were genotyped with the Illumina BovineHD BeadChip  and the other 4952 bulls were genotyped with a 50k SNP panel and imputed to BovineHD . Imputation from the 50k to the BovineHD SNP panel was performed with Beagle 3.3.0 , 29, usiWhole-genome sequence data 28,336,153 SNPs) of 429 animals that were provided by the 1000 bull genomes project (Run 3.0) were used as reference data for imputation. All these animals, except two, were males and originated from 15 dairy and beef breeds , among which there were four major breeds, with 121 Holstein, 87 Simmental, 54 Angus, and 43 Brown Swiss animals. Each animal was sequenced with the Illumina HiSeq System . Alignment, variant calling, and quality controls were as described by Daetwyler et al. [3 SNPs ofAfter imputation, non-informative SNPs were removed from the dataset, i.e. SNPs with less than two alleles, SNPs with a minor allele frequency lower than 0.005 and SNPs with an estimated imputation reliability lower than 0.05 (only for the imputed sequence data). Imputation reliability was predicted by Beagle software as the estimated squared correlation between the estimated allele dosage (0∗P(AA) + 1∗P(AB) + 2∗P(BB)) and the true allele dosage (estimated from posterior genotype probabilities) . In geneTo evaluate the effect of imputation on genomic prediction, a third genotype panel (ImputedHD) was generated by randomly selecting SNPs from the imputed sequence data. The number of selected SNPs per chromosome was the same as for the BovineHD genotype dataset, and did not include SNPs that were in the BovineHD genotype dataset.GEBV for the three traits were predicted based on two sets of genotypes: the original BovineHD genotypes and imputed whole-genome sequence genotypes. In both cases, the most likely genotypes were used for prediction. Genomic prediction was performed using two types of linear regression models: GBLUP and BSSVS.y is the vector of de-regressed proofs of all individuals, μ is the overall mean, 1 is a vector of ones, Z is an incidence matrix that links records to bulls, g is a matrix of the genomic breeding values of all individuals, and e contains the random residuals. Genomic breeding values were assumed to be distributed as g|GRM, σg2 ~ N, where GRM is the genomic relationship matrix, and σg2 is the additive genetic variance picked up by the markers. Diagonal and off-diagonal values of the GRM were calculated following Yang et al. [Gijk is the estimated relationship between individuals j and k at locus i, and N is the number of SNPs. The SNP genotypes (xi) were coded as 0, 1 or 2, and pi is the allele frequency of the allele for which the homozygote genotype was coded as 2. Residual effects were assumed to be distributed as e|D, σe2 ~ N, where D is a diagonal matrix containing 1/EDCEBV on the diagonals, and σe2 is the residual variance.The GBLUP model was:g et al.  as:\docuAfter calculation of the genomic relationship matrix, the GBLUP model was fitted using the ASReml 4 software . ASReml u is a vector that contains the polygenic effects of all bulls , where A is the numerator relationship matrix derived from the pedigree), X is a matrix that contains the allele dosage  for all SNPs (rows) for all bulls (columns), α is a vector that contains the (random) allele substitution effects for all SNPs. The prior for μ was a constant and both σu2 and σe2 had a flat, uninformative prior distribution.The BSSVS model  was:\docj (αj) depends on the variance for the allele substitution effects (σα2) and the QTL indicator Ij, which is sampled for each locus j and takes the value 0 (1) if the SNP was included in the model with a small (large) effect:An important aspect of the BSSVS is that the prior distribution for each allele substitution effects for each locus Ij was: p(Ij) = Bernoulli(1 − π). For both the BovineHD and the imputed sequence datasets, the same number of SNPs (885) was assumed to have a large effect, therefore π was assigned a value equal to /ntotal, where ntotal is the total number of SNP effects. The prior distribution for σα2 was: p(σα2) = χ− 2, with va = 4.2 degrees of freedom [The prior distribution for  freedom , 21, andg2ntotal .g2) was estimated as the sum of the posterior mean variances explained by the SNPs (σSNP2) and estimated variance of the polygenic effect included in the BSSVS model (σu2), where The conditional posterior densities of the BSSVS model are described in Additional file ribed in , and wasribed in . ConvergFor comparison, BLUP based on pedigree information only was also performed. Following the notation above, the model was:Similar to GBLUP, the BLUP model was applied using ASReml 4 software .The reliability of genomic prediction was evaluated by assigning all 5503 bulls to either the training or validation set based on year of birth, according to the protocol used to validate genomic prediction in practice. Bulls born before 2001 (3416 bulls) were assigned to the training set and bulls born between 2001 and 2008 (2087 bulls) to the validation set. The validation animals were split into smaller subgroups (see below) to ensure that the number of animals in these subgroups was sufficient, and a relatively large number of validation animals were chosen. Reliability of genomic prediction was calculated for the validation animals as the squared correlation between de-regressed proof and the EBV for the different traits. Furthermore, the regression coefficient of the DRP on the EBV was calculated to evaluate the bias of predictions. A regression coefficient of 1 indicates no bias.Persistency of the reliability of genomic prediction across generations was evaluated by splitting the validation bulls into three non-overlapping groups based on the presence of close relatives in the training set. The first group consisted of 1643 bulls with their sire and maternal grandsire in the training set (SMGS); the second group consisted of 113 bulls with their sire in the training set, but no maternal grandsire (SIRE); and the third group consisted of 329 bulls with no sire in the training set, but had one or both grandsires in the training set (GS). Two animals had no sire and no grandsires in the training set, and therefore were excluded from these analyses.After editing SNPs for MAF and imputation reliability, the final BovineHD and ImputedHD genotype dataset consisted of 631,428 SNPs and the imputed sequence genotype dataset of 12,590,056 SNPs. In the final datasets, the average minor allele frequency (MAF) was equal to 0.27 with a median of 0.28 for the BovineHD dataset, 0.17 with a median of 0.13 for the ImputedHD dataset and 0.19 with a median of 0.16 for the imputed sequence dataset. The distribution of SNPs across the different classes of MAF is in Fig. Prediction reliabilities ranged from 0.26 to 0.52 on average Table . OverallTo evaluate the reliability of genomic predictions across generations, the validation bulls were divided into groups based on the presence of (grand)parents in the training set: sire and maternal grandsire (SMGS); only sire (SIRE); no sire, but one or two grandsires (GS). As expected, in most cases, the SMGS group had the highest prediction reliability and the GS group the lowest Table . Overallσg2) for BSSVS using BovineHD data, ImputedHD data, and imputed sequence data. These Manhattan-plots do not show similar genome-wide association results as typically seen from single-SNP analyses. Instead, the Manhattan-plots represent the variances explained by a single SNP, conditional on fitting all other SNPs simultaneously. Therefore, SNP effects are much smaller than those obtained when only one SNP is fitted. Still, the figures show that when BovineHD data and ImputedHD data are used for SCS and PY, it is possible to detect some regions on the genome that explain greater levels of variance, e.g. on chromosomes 15 and 22 (SCS) and chromosome 14 (PY). For BovineHD data, 26 SNPs had a SNP variance greater than 0.003 %, with a maximum of 0.05 %, most of these SNPs were located in a 1.8 Mb region at the beginning of chromosome 14. With imputed sequence data, no clear region could be detected with large SNP effects on the traits, but it should be noted that with imputed sequence data, there are 20 times more SNPs. For a fair comparison with BovineHD data, SNPs in the imputed sequence data were grouped in windows of 20 neighboring SNPs and the sum of the variances of the neighboring SNPs per window was plotted. However, still we did not detect any clear regions with an increased level of explained variance (results not shown).For both genomic prediction methods, the (persistency in) reliabilities were highest when BovineHD genotype data were used compared to imputed sequence data. However, the additive genetic variances explained when imputed sequence data or BovineHD data was used were similar Table . In FigsOur objective was to investigate the reliability of genomic prediction based on imputed whole-genome sequence genotypes versus high-density SNP genotypes using real cattle data. Our hypothesis was that the use of sequence data in genomic prediction would result in higher reliability and higher persistency of reliability across generations. The rationale was that sequence data include the causal mutations that underlie QTL and that the effects of these mutations are estimated directly and not via effects of associated SNPs. This has been shown using simulated data –13 but nOur results did not show an advantage of using imputed sequence data compared to BovineHD genotype data for genomic prediction. Using imputed sequence data, both genomic prediction methods missed some QTL or QTL regions e.g. Figs. , 3 and 4p) was much larger than the number of observations (n), which might be a second limitation of the current training set. With a dataset that is too small, the QTL effects might be estimated with too much error, which reduces the advantage of using sequence data compared to SNP genotypes for genomic prediction [In this study, 3416 individuals were used to estimate the effects of over 12 million SNPs. Thus, the number of SNPs (ediction . The Manediction  might beediction . These a2) between a BovineHD SNP and the QTL. To test the possible effect of imputation, genomic prediction using a dataset of randomly selected SNPs from the imputed sequence data (ImputedHD) was compared with genomic prediction using the BovineHD dataset. Depending on the trait and method, a reduction of 0.01 to 0.03 in prediction reliability was found. A reduction in the reliability of GEBV with imputed genotypes has also been reported for studies on dairy cattle that used imputation from a few hundred SNPs to 50k SNPs, e.g. [Apart from the size and structure of the training dataset, the quality of the pre-imputation step could also impact the advantage of using sequence data for genomic prediction. To really benefit from imputed whole-genome sequence data compared to BovineHD data, imputation accuracy should be greater than the LD . With imputed sequence data, each cycle included about 12 million SNPs with a small effect. Combined together, these small SNP effects might explain a very large part of the variance and, thus, the larger QTL remained undetected by the model. A way to decrease the variance explained by the SNPs with a small effect could be to include only SNPs with large effects and set all other SNP effects to zero as:A third reason why imputed sequence information did not improve prediction reliability could be parameterization of the BSSVS model. In the BSSVS model used here, we assumed that the prior distribution for Ij is sampled to be 0. Also, instead of two distributions, with large and (close to) zero effects, it might be useful to derive SNP effects from more distributions, which is done in methods such as BayesR [This model is also known as BayesC . Compares BayesR .π was larger for the imputed sequence dataset compared to the BovineHD dataset. However, due to LD between closely linked SNPs, the number of SNPs with a large effect might be larger for imputed sequence data than for the BovineHD data. Therefore, it might be better to use the same π for analyses using imputed sequence data as that for BovineHD analyses. Ultimately, the combination of the chosen π value and the parameterization of the model defines a priori the distribution of the effects [π value of 0.999 were up to ten times as large as those achieved with BayesC with a π value of 0.9 [π could be treated as unknown [It was assumed that both genotype panels had the same number of underlying QTL, i.e. the chosen  effects , and thee of 0.9 . To over unknown .Due to the computation requirements of genomic prediction applied to imputed sequence data, it was unrealistic to test many different settings and models. For example, with the BSSVS model, one chain of 80,000 cycles took approximately 85 days on a High Performance Linux cluster containing Intel(R) Xeon(R) CPU E5-2660 with a clock speed of 2.20 GHz. GBLUP was less time demanding (~6 h), but required ~600 GB of RAM to store the genotypes. Due to efficient storing of the genotypes in the right-hand-side algorithm , the BSSWith 12 million SNPs, convergence of the Gibbs sampler can be rather low. Convergence of the BSSVS model was visually inspected by plotting the total SNP variance for each cycle of the Gibbs sampler  and one with large variance (the slab). The GBLUP model was based on equally weighted markers and did not include the pedigree separately. Therefore, the comparison between BSSVS and GBLUP involves not only two different models but also two different input sets and this could make interpretation of the results difficult. However, we tested the GBLUP model by including a polygenic component for SCS using the three types of genotype data  (Chr14:1802266) was selected, since DGAT1 is known to have a major effect on milk production traits in Holstein Friesian cattle [DGAT1.To test if reliability of genomic prediction increased by giving certain SNPs a higher prior, we included some SNPs as fixed effects in the GBLUP model. For SCS, the three SNPs  that explained the most variance in the BovineHD analysis  sequence data compared to BovineHD genotype data can be an advantage for genomic prediction, the use of a large set of animals with small average relationships, along with other properties of the training set used, should be considered. Genomic prediction models that incorporate biological information of the SNPs, or use a stricter SNP pre-selection procedure, might also increase the advantage of using (imputed) sequence data for genomic prediction.


Sequence analysis on ms5 and ms6 loci in Gossypium hirsutum was conducted to reveal genomic variation at these two loci between GMS and wild-type G. hirsutum inbred lines, and sequence polymorphism linked to ms5 on A12 and ms6 on D12 was revealed. A haplotype marker set that consisted of four SNPs targeting both ms5 and ms6 gene regions was developed and validated for association with GMS in cotton. Predictability of GMS phenotype by this haplotype SNP set was over 99 %. GMS haplotype marker set can serve as a high-throughput molecular breeding tool to select GMS individuals and improve hybrid production efficiency.Genetic male sterility (GMS) in cotton mediated by two homozygous recessive genes,  Ms1 to Ms19, identified in tetraploid cotton species , single recessive gene (ms2) and paired recessive genes (ms5 and ms6). Single recessive genes ms1 and ms3 reportedly conferred partial genetic male sterility  in cotton occurs in a form of non-dehiscent anthers and unviable pollens when reproductive development process fails due to possible loss of nuclear gene functions required for pollen development. Unlike cytoplasmic male sterility, genes related to GMS phenotypes can be easily transferred to various genetic backgrounds by routine crossing practice and allow the trait to be inherited at full penetrance in successive generations. There have been 19 different GMS genes, ion Basu .Gossypium arboreum to cytoplasm of Gossypium anomalum carrying diploid B1 genome. Male sterile plants were produced when CMS inducer was pollinated by pollens of G. arboreum, and fertility was restored with G. anomalum pollen  to Gossypium harknessii cytoplasm  technology is also available to produce hybrid cotton. The first cytoplasmic male sterility was developed by transferring diploid A2 genome of sm Meyer , 1975. R Stewart , and Rf2ms5 and ms6 genes were speculated to be G. tomentosum or progeny from an interspecific cross with G. hirsutum. A nectariless F3 individual from a cross of Stoneville 20 × nectariless G. tomentosum was crossed with a paternal parent, Empire WR. From this cross, a nectariless F3 individual was crossed with Gregg. An F2 individual with nectariless phenotype was crossed to Lankart 57 and a single male sterile individual with nectariless phenotype was identified in their F2 progenies  and 26 (ms6) and claimed that ms5 and ms15 are two different genes on chromosome 12 with a distance of 6 cM between loci. Rhyne  that cause pollen development failure in cotton. In Arabidopsis, causes of failure in male gamete development were classified into four cases: (1) non-functioning stamen resulting in deformed anthers, (2) failure in microsporogenesis, (3) lack of pollen-coating agent, tryphine, and (4) non-dehiscent anthers . Male sterility of each plant was determined by monitoring presence of non-dehiscent anther in five flowers at anthesis over a month. In addition, flower abortion was also observed to confirm male sterility. Plants were scored as fertile if at least one dehiscent anther was visible. The same method was applied to phenotype all genetic materials used in our study. Based on inheritance patterns of GMS within progenies of DPGh04651, two recessive genes presumably ms5 and ms6, were previously mapped to chromosomes 12 (A12) and 26 (D12), respectively, using SSR markers  by in silico comparative mapping approach. The diploid D genome sequence between 19,114,325 bp and 21,205,044 bp was used to discover genomic polymorphism linked to ms5 and ms6. PCR assays to produce sequencing templates were designed using Primer3 (http://frodo.wi.mit.edu/primer3/) with default parameters. G. hirsutum lines including TM-1, DPGh98018  and DPGh04651 (GMS donor) and diploid A  and D  as subgenomic references were used for amplicon PCR. To demonstrate homoeology between two loci on chromosome 12 and 26, respectively, chromosome nomenclature of A12 and D12 was used instead.Two recessive GMS gene, ® Taq DNA Polymerase High Fidelity manufactured by Life Technologies  following the manufacturer’s manual. The PCR was conducted by initial denaturation at 95 °C for 5 min followed by 35 cycles of denaturation at 95 °C for 1 min, annealing at 55 °C for 30 s, and extension at 72 °C for 2 min and a final extension at 72 °C for 5 min. Sequencing of PCR amplicons was performed using 3730XL DNA Analyzer , and sequence analysis and SNP discovery were conducted using CLC Bio Genomics Workbench software . All genomic polymorphisms identified by amplicon sequence analysis were converted to TaqMan® assays for further SNP validation and SNP genotyping. DNA extraction was done by a protocol developed by Dellaporta et al.  and 4 ng genomic DNA. The PCR was conducted using GeneAmp® PCR System 9700  by initial denaturation at 95 °C for 10 min followed by 40 cycles of denaturation at 95 °C for 15 s, annealing and extension at 60 °C for 60 s. SNP data collection and analysis were conducted by ViiA™ 7 Software v1.2.1. .PCR of 5 µl total volume for SNP assay validation and SNP genotyping was conducted using GTXpress™ Master Mix for SNP genotyping assay manufactured by Life Technologies following the manufacturer’s manual . Each PCR included two PCR primers , two TaqMan3 populations were generated by combining 14 different sibcross F2 populations originated from a cross between DPGh98018 and DPGh04651. Genetic linkage between SNPs and chromosome assignment of linked markers to A12 or D12 were estimated by genetic distance calculated using R-Genetics (http://cran.r-project.org/web/packages/genetics/genetics.pdf) among newly developed markers and also to the chromosome-specific markers assigned to A12 and D12  which were reportedly served as donors of recessive ms5 and ms6 alleles , Gregg (PI529094), Empire WR (PI529224) and s Weaver , and the2 siblings produced sibcross F1 progenies, and fertile-to-sterile phenotype ratios within these sibcross F1 populations were close to 1:1  was used as a reference to nominate target regions for discovery of SNPs. Presumably about 0.5-Mbp-long sequences were subjected to amplicon sequencing. Sequence polymorphism was identified in forms of single-nucleotide polymorphism or single- or multiple-nucleotide deletion among the test materials which included male fertile and male sterile inbred lines on diploid A and D genome species. Among all genomic polymorphism identified within target regions covered by amplicon sequences, 23 polymorphic sequences qualified for PCR assay design were subjected to further analysis.Whole-genome sequence of ms5 on A12 and ms6 on D12 using F3 populations originated from 14 different sibcross F2 populations segregating for male sterility. Polymorphisms of SNP markers corresponding to all 23 sequence polymorphisms identified by amplicon sequencing were tested in F3 populations, and genetic distances between SNPs and linkage to ms5 and ms6 were calculated based on genetic linkage to chromosome-specific SNPs. All 23 markers segregated in F3 sibcross populations, and all were assigned to ms5 and ms6 loci. Annotation of the putative genes carrying SNPs linked to ms5 and ms6 and their in silico comparative map positions on chromosome 08 on G. raimondii D genome (D5) are summarized in Table SNPs were tested for linkage to ms5 and ms6. Male sterility phenotypes were collected from all association test materials and the phenotype data were categorized in binary format as either male sterile or male fertile. Using logistic regression model, all possible SNP combinations between two loci were tested to explain male sterility within diverse test materials. The recessive ms5 allele was explained best by TT allele on MOGH583971 and TT allele on MOGH582973, and the recessive ms6 allele was explained best by CC allele on MOGH211275 and GG allele on MOGH493571. When these two haplotypes for recessive alleles of ms5 and ms6 were combined, phenotype predictability in the diverse test panel was 99.6 % , Gregg (PI529094), Empire WR (PI529224) and y Weaver , it is ums5 and ms6 were defined by Chen and his colleagues in 2009 using higher density SSR markers tested on backcrossing populations. By testing molecular marker patterns in Ms5ms5Ms6ms6 of male fertile and ms5ms5ms6ms6 of male sterile progenies, the authors were able to identify markers explaining dominant and recessive alleles of each gene. Markers found to be associated with the genes were further tested for genetic distance against other existing molecular markers. Using populations segregating for only one of the two genes with the second fixed to recessive homozygote is another effective approach beyond synthetic backcrossing population method used by Chen and his colleagues. However, the single gene segregating population approach will require tedious monitoring of progeny phenotypes in each family and can be very difficult to determine which gene is segregating in each population. In our study, we scanned entire genetic regions neighboring ms5 and ms6 and discovered markers polymorphic in diverse genetic backgrounds. All newly developed SNPs were quickly mapped to ms5 and ms6 by in silico comparative mapping using D diploid genome sequence and by linkage analysis using randomly segregating F3 populations. Based on known genetic locations and distances between markers, all possible pairwise combinations of markers between two different chromosomes were tested for association with male sterility using logistic regression modeling. As a result, two haplotypes consisting of 2 SNPs linked to ms5 and 2 SNPs linked to ms6 were identified that explained male fertility and sterility with 99 % accuracy.Map positions of ms5 and ms6, were expected to be homoeologs and possibly duplicated through polyploidization. However, we found haplotype markers representing ms5 and ms6 located about 150 Kbp apart from one another on the diploid D genome sequence. If genomic variation exist only on one of two homoeologs, then it is still possible to identify polymorphic markers unique to one homoeologous chromosome. Additional genomic polymorphisms at flanking regions from the other homoeologous chromosome can be used as flanking markers and allowing the two haplotypes high phenotypic prediction accuracy.Two GMS genes, AP2 (ERF domain) on D12 were contained between markers defining the haplotypes. ArabidopsisMALE STERILITY1 gene was found to be PHD-zinc-finger-like domain protein which plays a critical role in pollen development  as a negative regulator of AGAMOUS has been well established using Arabidopsis flower development mutants. Stamen, ovule and other floral organ development was significantly prohibited in the ap2 mutant (Jofuku et al. From the in silico comparative mapping of template sequences of the SNPs to diploid D genome, genomic regions targeted by male sterility haplotypes appeared to be very narrow and contained a limited number of genes. Based on putative gene annotation, PHD-zinc-finger-like domain protein on A12 and ms5 and ms6 study hypothesized the origin of the alleles from Gregg and Lankart 57. The reasoning is that following the identification of the first sterile individual, backcrossing was performed with Gregg and Lankart 57, each producing F2 segregating populations with 3:1 male-fertile-to-male-sterile ratio. Our study with haplotype markers tested on Lankart 57, Gregg, Empire WR and G. tomentosum showed that recessive ms5 allele was present only in G. tomentosum and that recessive ms6 allele was present in G. tomentosum and Lankart 57. This indicates that ms5 allele in male sterile F2 progeny was likely transmitted from G. tomentosum. However, due to absence of male sterile progenies in the F2 populations occurring before the crossing with Lankart 57, it is likely that ms6 allele causative for male sterility is found in Lankart 57 and not G. tomentosum. Although genotype test materials we used might not be identical to the ones used in the study by Weaver in 1968, the results from our study were able to explain potential sources of genetic male sterility in cotton.The initial ms5 on A12 and two linked to ms6 on D12 in G. hirsutum was developed. This haplotype marker set was able to predict male sterile phenotype at the rate of 99 % accuracy within our diverse germplasm test set. Each haplotype SNP marker can differentiate zygosity of ms5 and ms6 loci and can be used to predict GMS phenotype in G. hirsutum of diverse genetic background at any generations including inbred parents and progenies segregating randomly. With reliability as molecular markers and tight linkage to ms5 and ms6, our GMS haplotype SNP marker set can serve as a high-throughput molecular breeding tool to select GMS individuals to be used as hybrid parents and also to confirm purity of hybrids produced through larger scale commercial seed production. This new molecular breeding tool should be able to eliminate tedious hand emasculation and fertile plant removal after flower phenotyping in the field and improve genetic purity and quality of hybrid production.In conclusion, a haplotype SNP marker set that consisted of four SNPs, two linked to