Misassembly signatures, created by shuffling the order of sequences while assembling a genome, can be detected by the unexpected behavior of marker linkage disequilibrium (LD) decay. We developed a heuristic process to identify misassembly signatures, applied it to the bovine reference genome assembly (UMDv3.1) and presented the consequences of misassemblies in two case studies.POLL locus, which affected the annotation of positional candidate genes in a GWAS case study for polledness in Nellore (Bos indicus beef cattle). Additionally, we found that poorly performing markers in imputation mapped to putative misassembled regions, and that correction of marker positions based on LD was capable to recover imputation accuracy.We identified 2,906 single nucleotide polymorphism (SNP) markers presenting unexpected LD decay behavior in 626 putative misassembled contigs, which comprised less than 1 % of the whole genome. Although this represents a small fraction of the reference sequence, these poorly assembled segments can lead to severe implications to local genome context. For instance, we showed that one of the misassembled regions mapped to the This heuristic approach can be useful to cross validate reference assemblies and to filter out markers located at low confidence genomic regions before conducting downstream analyses.The online version of this article (doi:10.1186/s12864-016-3049-8) contains supplementary material, which is available to authorized users. Therefore, systematic identification and correction of misassembled segments in a reference genome improves assembly quality locally, increases the power of analyses, decreases the rate of false positive results and ultimately permits researchers to formulate hypotheses on the basis of more correct findings.Studies using next generation sequencing or single nucleotide polymorphism’s (SNP) array data often rely on genomic coordinates from reference assemblies. However, as stated by Salzberg and York : “Certaiin situ hybridization [Methods relying on sequence , 3, in sdization  or opticBos taurus), Pausch et al. [Bos indicus, namely Nellore beef cattle [Evidence of misassembly signatures in the bovine UMDv3.1 reference genome have been recently reported. Bohmanova et al. , characth et al.  identifif cattle  and Gyr f cattle . Since tf cattle .Here, we report a heuristic approach based on LD analysis to identify misassembly signatures and estimate an approximate re-location for the missassembled segments in the UMDv3.1 bovine reference genome. Furthermore, we demonstrate the effect of detecting and correcting assembly errors in two case studies: i) GWAS on the Polled/Horned phenotype in Nellore cattle and ii) imputation in Nellore and Italian Holstein. Misassembled segments directly affected GWAS results and imputation performance. Re-location of misassembled segments provided new insights on the so far unsolved nature of the locus controlling horn development in cattle and substantially improved imputation accuracy locally.B. taurus (Holstein) and one of B. indicus (Nellore) with distinct production purposes , to identify unexpected LD patterns in common regions that are likely due to genome misassembly. These breeds were chosen because the largest bulk of re-sequencing data driving the design of the high density cattle SNP panel (BovineHD) were from Holstein (HOL), and most of the B. indicus specific SNPs were derived from a draft genome assembly of a Nellore (NEL) bull. Nellore genotypes were provided by the Zebu Genome Consortium (ZGC) and Italian Holstein genotypes were provided by the INNOVAGEN project - “Research and innovation in italian animal breeding II”.To minimize spurious LD patterns caused by the confounding effect of breed-specific gametic-phase disequilibrium and structural variants or epistatic loci under selection, we selected genotypes from two genetically divergent populations, one of After data filtering, 886 bulls and 564,865 autosomal SNPs and 811 bulls and 485,455 autosomal SNPs were used for estimating marker-pair LD values in HOL and NEL, respectively. Combination of the two SNP lists resulted in 675,859 markers, which covered about 2.5 Gb of the UMDv3.1 reference genome, with an average intermarker distance of 3.71 ± 5.21 kb. Although highly important, sexual chromosomes were excluded from this analysis due to their particularly complex model of inheritance. Moreover, there is not an assembly for the Y chromosome, the pseudo-autosomal region of the X chromosome is not well characterized, and the use of male data only may bias LD estimates.We applied our pipeline Fig.  to the NWe mapped the 2,906 CMMs against their contigs of origin and identified 626 Candidate Misassembled Contigs (CMCs). A graphical inspection of the LD decay of the detected CMCs allowed us to recognize three types of signatures of misassembly: i. Misplaced SNP (MisSNP); ii. Misassembled Segment (MisSeg); and iii. Partially Misassembled Contig (PMisCon). Briefly, MisSNPs are defined as single markers showing strong LD with SNPs in a segment far apart >10 Mb) in the same or in a different chromosome and no evidence of LD with SNPs surrounding it. MisSeg comprises one or more CMCs showing markers with strong LD with SNPs within the segment and with SNPs far apart in the same or in a different chromosome, but no evidence of LD with SNPs in adjacent contigs. Finally, a PMisCon is a special case of CMC with markers having a mixture of behaviors. A fraction of them had expected LD values with nearby markers in the region, while others show no evidence of LD at their sites but strong LD with another segment elsewhere in the genome. Examples of MisSNP, MisSeg and PMisCon are shown in Additional file 0 Mb in tFrom the total number of detected markers, 2,796 (96.21 %) were included in MisSeg, 76 (2.62 %) in PMisCon and 34 (1.17 %) in MisSNP. The complete list of the detected signatures of misassembly, as well as all the graphs built for the MisSNP, MisSeg and PMisCon cases, can be seen in Additional file The average size of CMCs included in MisSeg was 18.88 ± 26.02 kb. The vast majority of the CMCs  had length smaller than the N50 in UMD v3.1 (96.955 Kb). Only 2.4 % of the CMCs had length greater than the N50  with absence of horns in NEL. These markers were divided into two different signals on chromosome 1  was located at 78.66 kb, nearby the widely known POLL locus [p = 7.41 × 10−13) was located at 60.66 Mb, approximately 60.58 Mb apart from the most significant SNP in the first signal.We found 295 markers significantly associated (r2 = 0.88). In fact, the average r2 among all significant SNPs was 0.73 ± 0.18. Looking carefully at the behavior of LD decay in the second signal, we noted strong LD among markers within the region but no evidence of LD with surrounding markers. Indeed, the SNPs pertaining to the second signal are located in the R6 MisSeg  identified in our LD-based analysis of assembly errors  to high density (BovineHD) chips.The average genome-wide SNP imputation accuracy was 0.9695 ± 0.0271 in NEL and 0.9933 ± 0.0161 in HOL. After excluding all putative assembly problems, accuracies increased marginally to 0.9717 ± 0.0231 in NEL and 0.9937 ± 0.0094 in HOL. After correcting the positions, accuracies also increased marginally to 0.9740 ± 0.0231 in NEL and 0.9944 ± 0.0127 in HOL. However, the local imputation performance before and after correcting SNP locations were 0.7868 ± 0.1971 and 0.9725 ± 0.0386 in NEL and 0.8359 ± 0.1989 and 0.9909 ± 0.0520 in HOL, respectively.In order to illustrate the local effect of correcting assembly errors, we showed the local genotype prediction accuracy before and after correcting the location of the R237 MisSeg . Considering the originally assigned location of R237, we obtained average accuracies of 0.8452 ± 0.1339 in NEL and 0.8612 ± 0.1044 in HOL. After correction, SNPs in R237 increased their imputation accuracies considerably in both NEL and HOL, with averages of 0.9616 ± 0.0282 and 0.9866 ± 0.0096, respectively  dairy cattle, Boison and colleagues [B. taurus) dairy cattle. In a more detailed analysis we could break this segment down to two segments, ranging from 44.8 to 45.13 Mb and from 45.14 to 45.2 Mb. Overall, these studies reported the unusual LD and imputation behavior of SNP markers as an incidental finding arising as a side product of their main targeted analyses and avoided a systematic mapping and interpretation of the errors, as well as an assessment of their potential impact on coordinates-dependent analyses.Evidences of assembly problems in the bovine reference have been recently published in LD and imputation studies. For instance, Bohmanova et al.  assessedlleagues  found polleagues  in Nellolleagues  in FleckHere, we were able to pinpoint putative assembly errors in the UMDv3.1 bovine reference genome by using population-level LD data generated from high-density SNP genotypes of two divergent breeds of cattle. Our study revealed that, at least to the extent our LD analysis could detect, a small fraction (<1 %) of the reference genome is inadequately assembled. Also, over 97.6 % of missassembled segments were found to be smaller than the N50 in UMDv3.1, reflecting that these errors largely represent low confidence small contigs. Although the detected missassembled segments may not threat the global quality of the UMDv3.1 reference, locally these errors are deemed to affect applications that assume correct marker positions.Studies that are dependent on the positions of molecular markers, such as assessment of the extent of the linkage disequilibrium, imputation, synteny, genome wide association studies, detection of structural variants, runs of homozygosity, among others, can be greatly affected by local assembly errors. For instance, Fadista & Bendixen  sought dPOLL locus in B. taurus, which shelters some candidate genes for the trait, such as OLIG2, FOXL2 and RXFP2 [GAP43 (growth associated protein 43), located approximately 200 kb downstream of the marker. The gene is involved in innervation and development of long bones [GAP43 in horn development.To illustrate the impact of assembly errors in GWAS, we use the example of a genome-wide scan for absence of horns in Nellore cattle Fig. . The scand RXFP2 . The neang bones , 17. Recng bones , suggestGAP43, the positional association is clearly wrong. The contig containing the significant marker was estimated to be located within the POLL locus, and the region surrounding GAP43 showed no evidence of assembly errors. Moreover, the presence of portions of the POLL locus assembled somewhere else in the genome could be an important contributor to the ongoing dispute regarding the candidate gene and causal variant underlying the trait.In spite of the appealing functional candidacy of We also carried out an imputation study to assess the impact of misassembled segments in genotype prediction performance in Nellore and Holstein. As expected, the impact of assembly errors on the average imputation accuracy was negligible (0.22 % in Nellore and 0.04 % in Holstein). However, the correction of the positions of the markers using LD increased the local imputation accuracy substantially (differences of 18.57 % in Nellore and 15.5 % in Holstein). These findings show that the use of LD to estimate the correct position of misassembled segments may help to improve the local quality of the assembly, as well as to increase confidence in association, phasing and imputation analyses.Although very informative, the heuristic approach adopted here has important limitations that are intrinsic to the LD analysis. Ideally, one would require the candidate misassembled contigs to be detected in several breeds independently as a strategy to increase the likelihood that the observed pattern was caused by a problem in the reference assembly, rather than breed-specific linkage due to structural variants or epistatic loci under selection or gametic phase disequilibrium. However, due to heterogeneous marker coverage in different breeds, some signals of assembly problems could remain undetected in some breeds due to the effect of ascertainment bias, and in this case requiring the signal to appear in at least one breed could give rise to false positives.Khatkar et al.  reportedFinally, although some chromosomes exhibited no or few cases of assembly problems, all chromosomes acted as receptors of misassembled segments. This indicates that our estimate of percentage of errors is likely to be underestimated, as some types of assembly errors may not produce a detectable LD signature.Assembly errors in the UMDv3.1 bovine reference genome were pinpointed by SNP LD analysis. We showed that the majority of the errors comprised small contigs that were either malformed or placed wrongly in the genome. Although the incidence of errors was low, confirming the robustness of the bovine reference sequence assembly UMDv3.1, the misassembled segments were shown to largely impact local imputation performance and the interpretation of GWAS results. Estimation of the correct location of the misassembled segments significantly improved imputation accuracy locally and excluded a functional candidate gene as a putative determinant of horn development in polled and horned Nellore cattle. Our heuristic approach can be useful in refining draft assemblies already available and as a strategy to filter out markers that could largely affect interpretation of downstream analysis.This study involved no animal experimentation and was exempt from local ethics committee evaluation because DNA was extracted from commercialized semen straws.Bos taurus) and 995 Brazilian Nellore (Beef - Bos indicus) bulls were genotyped using the BovineHD assay, according to the manufacturer’s protocol. Only autosomal markers with unique genomic coordinates presenting call rate of at least 95 %, minor allele frequency (MAF) greater than 3 % and Fisher’s exact test for Hardy-Weinberg equilibrium greater than 1 × 10−8 were considered for LD analyses. Bulls presenting call rate lower than 90 % were excluded. Supplementally, the largest sample set with the least relatedness (<0.4) was optimized using the --rel-cutoff algorithm in PLINK v1.9 [A total of 1,009 Italian Holstein  of the genotype vectors , as implemented in PLINK v1.9 [r2 > 0.5) with the focal SNP. Third, a table of tag marker counts per chromosome was created. If the largest tag marker count (i.e >50 % of the tag markers) was found in a different chromosome than the one originally assigned for the focal SNP, independently of the tag markers positions, we labeled the focal SNP as a candidate misplaced marker (CMM). Otherwise, if the previous condition failed, we computed the base pair distances between the focal SNP and the tag markers, now on the same chromosome, and classified these distances as long (≥ 10 Mb) or short (<10 Mb). Then, we built a table of tag marker counts per distance class. If the number of correlations at long distances were greater than at short distances, the focal SNP was also labeled as CMM. A scheme of this procedure can be seen in Fig. Linkage disequilibrium between SNPs was measured by the squared Pearson’s product-moment correlation  were defined as any contig presenting one or more candidate misplaced markers, as detected in the procedure described before. In order to gain insights on the possible sources of errors leading to the misplacement or malformation of contigs, we visually inspected the intra- and inter-chromosomal patterns of LD decay of every single detected CMC. For that matter, we plotted pairwise r2 with the most proximal and distal SNPs within the CMC, respectively. These estimates were taken in both breeds independently, since the allele frequency spectra and consequently the distribution of the markers covering the region could differ between NEL and HOL. Finally, we evaluated the concordance of the estimated true position by the difference of the estimated positions between breeds.The unexpected patterns of LD decay were also carefully examined in order to estimate the correct locations of CMC. Estimates for the 5′ and 3′ coordinates were based on the positions of the tag markers presenting the highest rPresence or absence of horns was scored by majority voting of image analysis performed by five observers in a subset of 481 Nellore bulls. Phenotype-genotype associations were tested using single-marker regression under a mixed model framework , as implWe evaluated the impact of excluding and correcting positions of SNP markers in misassembled segments on the global performance of imputation from medium (Bovine50k) to high density (BovineHD) chips by calculating the SNP imputation accuracy as the percentage of correctly imputed genotypes (PERC). We also evaluated the local performance of imputation after correcting the positions of misassembled segments. In order to illustrate that, we examined one of the misassembled segments, located on chromosome 26:25,715,286–26,015,674, before and after correcting the SNP locations by imputing SNPs from a medium (Bovine50k) to high density (BovineHD) panel. To correct the SNP locations we estimated the true location of the segment and then sample the new SNP positions in the segment from an uniform distribution.The analyses were performed in both breeds dividing the animals in reference and imputation sets in a 5-fold cross-validation scheme. Each group was used as imputation set once. SNPs corresponding to Bovine50k were subset from the BovineHD panel. Imputations were performed using Fimpute .


V. unguiculata L. Walp) is a climate resilient legume crop important for food security. Cultivated cowpea (V. unguiculata L) generally comprises the bushy, short‐podded grain cowpea dominant in Africa and the climbing, long‐podded vegetable cowpea popular in Asia. How selection has contributed to the diversification of the two types of cowpea remains largely unknown. In the current study, a novel genotyping assay for over 50 000 SNPs was employed to delineate genomic regions governing pod length. Major, minor and epistatic QTLs were identified through QTL mapping. Seventy‐two SNPs associated with pod length were detected by genome‐wide association studies (GWAS). Population stratification analysis revealed subdivision among a cowpea germplasm collection consisting of 299 accessions, which is consistent with pod length groups. Genomic scan for selective signals suggested that domestication of vegetable cowpea was accompanied by selection of multiple traits including pod length, while the further improvement process was featured by selection of pod length primarily. Pod growth kinetics assay demonstrated that more durable cell proliferation rather than cell elongation or enlargement was the main reason for longer pods. Transcriptomic analysis suggested the involvement of sugar, gibberellin and nutritional signalling in regulation of pod length. This study establishes the basis for map‐based cloning of pod length genes in cowpea and for marker‐assisted selection of this trait in breeding programmes.Cowpea ( V. unguiculata L. Walp., 2n = 2x = 22), native to Africa, is a worldwide important legume used as a grain, fodder or vegetable crop. Sub‐Saharan Africa and Brazil are the major producers of cowpea grain, while East/South‐East Asia is the main vegetable‐type cowpea producer , provides a major source of dietary protein for millions of people in developing countries , is characterized by its long tender pods that are harvested when immature, narrow kidney‐shaped seeds, and strong trailing and climbing growth habit that is rare in ssp. unguiculata germplasm. The asparagus bean pods can either be snapped and cooked in stew or stir‐fried, or preserved with salt and chile. They provide a good source of proteins, vitamins and minerals  provides an opportunity to further dissect pod length and other domestication traits. This array contains 51 128 SNPs that derive from WGS sequencing of 37 diverse cowpea accessions (33 grain cowpeas and four asparagus bean) (Muñoz‐Amatriaín We SNP genotyped 432 DNA samples including 132 recombinant inbred lines (RILs) and a diversity panel of 299 landraces, cultivars and breeding lines  was observed for pod length between different trials. The estimated broad‐sense heritability was 70.9% over the 2 years. A wider range of variation (12.3–74.5 cm) and a continuous distribution based on multiyear pod length data were observed for the diversity panel. Correlation coefficients between experiments ranged from 0.87 to 0.93 (P < 0.001) for this population.Zhijiang282, the male parent of the RIL population, exhibited longer pods (mean = 47.0 cm) than the female parent ZN016 (mean = 31.9 cm) in all experiments. Among the RILs, pod length displayed a continuous distribution with the population means falling between the parental values  were mapped to 697 bins in 11 linkage groups (LGs) . This map, hereafter referred to as ‘ZZ map v.2’ in consistency with its earlier version  was detected at a LOD score above 3 in the 2010HN trial, and above LOD = 6 in the 2009HN trial. It explained 11% of the phenotypic variance on average. Genome‐wide scan of digenic interactions identified four pairs of epistatic interactions, three interchromosomal and one intrachromosomal  Figure . The larY‐axis, Figure The overall heterozygosity rate of the germplasm panel estimated with 30 211 high‐quality SNPs was 2.23%, fitting the expectation for inbred plant materials. Pairwise genetic distances between the 299 accessions ranged from 0.0003 to 0.5556, with a mean of 0.253. A model‐based clustering method identified two major subpopulations in the germplasm panel  with genetic distance occurred at ~2 cM across the whole panel. LD decayed  faster in subpopulation 1 than in subpopulation 2 where more breeding lines were included  and nucleotide diversity (π) was carried out first between the two subgene pools and then between the landraces and cultivars/breeding lines from subpopulation 2. Overall FST between subpopulation 1 and subpopulation 2 was 0.262 (Table S4), indicating a moderate population divergence. The mean π value in subpopulation 1 was 0.314, more than twice that of in subpopulation 2 , suggesting a severe loss of genetic diversity in the vegetable cowpea subgene pool. A fine‐scale inspection of π and FST in a window sizing 0.15 cM  with a step size of 0.03 cM across the genome revealed variations among and fluctuations across LGs, as expected. A total of 40.5 cM of differentiated genomic regions as indicated by FST outliers were determined between the two subgroups  outliers were detected in genomic regions totalling 15.33 cM in length, representing ~1.8% of the genome  were detected in genome regions totalling 13.74 cM in length. Seven of these regions were found to be coincident with pod length QTLs, and only three with QTLs controlling other traits. These results suggest that pod length appears to be the primary selection target during improvement of vegetable cowpea after its domestication. Among the seven regions overlapping with pod length QTLs, two were specific to the improvement process.The genotypic differences of pod length might result either from different cell size or from cell number. To elucidate this, cell diameters were measured for sectioned pods among three genotypes representing the long‐ (Zhijiang282), medium‐ ZN016) and short‐podded (G314) genotypes, respectively. Their growth kinetics after anthesis are shown in Figure  and shorSWEETIE, encoding a glycosyl transferase known to involve in the regulation of sugar flux . A total of 873 and 1228 genes were found to be more abundantly expressed in long‐pod and short‐pod pools, respectively . et al., et al., et al., et al., et al., et al., et al., Vigna species such as azuki bean  between the earlier version of the ZZ map  for genotyping which may suffer from ascertainment bias  developed by single‐seed descent from the cross ‘ZN016’ × ‘Zhijiang282’. ‘ZN016’ is a landrace vegetable cowpea accession originating from southern China with medium‐long pods (~38 cm) while ‘Zhijiang282’ is a typical asparagus bean cultivar with long pods  and detailed information from Muñoz‐Amatriaín et al. . Standardized DNA for the samples, the majority at a concentration of 50 ng/μL and a small fraction 10–30 ng/μL were hybridized to the Cowpea iSelect Consortium Array according to the standard protocol. Single‐base extension was performed and the chips were scanned using the Illumina iScan. Image files were saved for cluster file analysis. The clustering algorithm of GenomeStudio Genotyping Module  was used for SNP calling. To increase the accuracy of the clustering algorithm, additional samples including synthetic heterozygous were added to the workspace. Polymorphic SNPs were identified based on Illumina GenTrain score (proximity of clusters) and call frequencies across samples. All markers were manually inspected and curated based on manufacturer's best‐practice instructions. All marker sequences can be retrieved from the HarvEST database  between each SNP pair.Pairwise genetic distances were calculated using TASSEL 5.1  that corrects for both population structure (Q) and relative kinship (K). The phenotypic data used were a combined set of field data from multiple experiments. Significant SNPs were defined if showing a minus log10‐transformed  Storey, . SNPs wiπ=1−∑ini2/n2, where ni denotes the count of allele i and n = Σ ni. Population differentiation index (FST) was estimated adapting the formula FST=1−∑jnj2πjπ·∑jnj2 that accounts for unbalanced population sizes according to Nielsen et al.  was calculated using the formula n et al. . These pe et al. . One milFST or π ratio. If the known QTLs (or single associated SNPs) were detected by GWAS, their coincidence with a selective signal was determined by the overlap of the LD block where the SNP resides (2 cM downstream and upstream of the significant SNP) and the window region of the selective signal.Map position information of known qualitative genes or QTLs governing domestication and resistance traits in cowpea was assembled from a literature search. Only genes/QTLs tagged by SNP markers that are comparable to our marker system were used in the analysis. SNP markers reported to be tightly linked to qualitative genes or located around the peak regions of QTLs were extracted. A coincidence between a known gene/QTL and a selective signal was declared if the SNP markers fell into the outlier window regions of Pod samples collected at 1, 5 and 10 dpa were fixed in FAA  for 24 h and then dehydrated in a graded ethanol series, substituted with 1‐butanol and embedded in Paraplast Plus. The samples were sectioned longitudinally at 4 μm thick using a rotary microtome . The sections were stained with methylene blue and observed under a light microscope . For cell diameter comparison, ten cells from each sample were measured and the data averaged.et al., et al.  ≤0.05. Hierarchical clustering was performed with Cluster 3.0 using the average linkage method, and the results were visualized using TreeView  enrichment analyses were performed using GOrilla (Eden http://www.illumina.com/areas-of-interest/agrigenomics/consortia.html). Source data for microarray experiment were deposited in the NCBI Gene Expression Omnibus under the GEO series accession number GSE80552.All the raw data used in this study are publicly available. The Cowpea iSelect Consortium Array is available from Illumina Click here for additional data file.Table S6 Qualitative genes/QTLs or trait‐associated SNPs used for comparison with diverged regions and putative selection signals across the genome.Click here for additional data file.Table S7 Genes that were differentially expressed between the long and short‐podded pools. In red are genes more abundantly expressed in long pods and in green are genes more abundantly expressed in short pods. FC=fold change.Click here for additional data file.Table S8 List of the GO terms overrepresented between the two RNA pools.Click here for additional data file.


ZNF423 and near CTSO that were associated with breast cancer risk during SERM chemoprevention. We have now performed Next Generation DNA sequencing to identify additional SNPs that might contribute to breast cancer risk and to extend our observation that SNPs located hundreds of bp from estrogen response elements (EREs) can alter estrogen receptor alpha (ERα) binding in a SERM-dependent fashion. Our study utilized a nested case-control cohort selected from patients enrolled in the original GWAS, with 199 cases who developed breast cancer during SERM therapy and 201 matched controls who did not. We resequenced approximately 500 kb across both ZNF423 and CTSO, followed by functional genomic studies. We identified 4079 SNPs across ZNF423 and 3876 across CTSO, with 9 SNPs in ZNF423 and 12 in CTSO with p < 1E-02 that were within 500 bp of an ERE motif. The rs746157 (p = 8.44E-04) and rs12918288 SNPs (p = 3.43E-03) in intron 5 of ZNF423, were in linkage equilibrium and were associated with alterations in ER-binding to an ERE motif distant from these SNPs. We also studied all nonsynonymous SNPs in both genes and observed that one nsSNP in ZNF423 displayed decreased protein expression. In conclusion, we identified additional functional SNPs in ZNF423 that were associated with SNP and SERM-dependent alternations in ER binding and transcriptional regulation for an ERE at a distance from the SNPs, thus providing novel insight into mechanisms of SERM effect.Our previous GWAS using samples from the NSABP P-1 and P-2 selective estrogen receptor modulator (SERM) breast cancer prevention trials identified SNPs in  ZNF423 and CTSO—affected how well the drugs tamoxifen and raloxifene reduced the rate of breast cancer occurrence. Building on that finding, Weinshilboum’s team has now sequenced these two genes in 199 patients who developed breast cancer during chemopreventative drug therapy and 201 patients who did not. They identified around 4000 single-nucleotide polymorphisms across each gene, 21 of which were close to estrogen response element (ERE) motifs to which ERα binds. Functional studies pointed to molecular ways in which some of these gene variants alter drug activity.DNA sequencing has revealed genetic variants that explain inherited variation in responses to preventative drug therapy for breast cancer. Richard Weinshilboum from the Mayo Clinic in Rochester, Minnesota, USA, and colleagues previously showed that genetic variations in or near two genes— Treatment of the approximately 70% of women with estrogen-receptor (ER) positive breast cancer with the selective estrogen receptor modulator (SERM) tamoxifen can reduce disease recurrence by nearly 50%.6 That GWAS identified two top single nucleotide polymorphism (SNP) signals that mapped to the second intron of the zinc finger protein 423 (ZNF423) gene on chromosome 16 and 5’ of the cathepsin O (CTSO) gene on chromosome 4. Functional genomic studies demonstrated SNP-dependent estradiol (E2) induction of the expression of both ZNF423 and CTSO and, downstream, of BRCA1, offering a mechanistic explanation related to the role of BRCA1 in DNA double strand break repair for the association of these SNPs with risk for breast cancer during SERM therapy.6 Alteration in the regulation of BRCA1 might be expected to influence carcinogenesis for ER-positive tumors as well as ER-negative breast cancers. In addition, and surprisingly, the SNPs in ZNF423 displayed SERM and SNP-dependent “reversal” of the E2 induction of both ZNF423 and BRCA1—with induction of expression in subjects with variant SNP genotypes during SERM exposure that was associated with decreased breast cancer risk, and the opposite effect in subjects with a “wild type” (WT) SNP genotype for rs9940645, a SNP that mapped 200 bp distant from an estrogen response element (ERE). This same pattern, was reflected by an identical SNP-dependent difference in expression for BRCA1  near that gene disrupted an ERE.6 Variant sequences for the CTSO-related SNPs were associated with increased risk for breast cancer. These results indicated that the two top SNP signals observed during the P-1 and P-2 GWAS were linked to the effects of SERM therapy and that they were biologically plausible as a result of their effect on the expression of a major breast cancer risk gene, BRCA1.6We recently performed a discovery genome-wide association study (GWAS) using DNA from women who received tamoxifen or raloxifene during the P-1 and P-2 breast cancer prevention trials with a phenotype of breast cancer occurrence during 5 years of SERM preventive therapy.7 In the present study, we performed Next Generation Sequencing (NGS) across approximately 500 kb of DNA sequence surrounding the SNP signals for both ZNF423 and CTSO using DNA samples from our GWAS study to screen for additional variants associated with breast cancer risk during SERM prevention therapy and also to study the functional implications of those variants—in particular to determine whether the striking SERM-related “reversal” of SNP-dependent variation in the expression of ZNF423 and BRCA1 shown in Fig. ZNF423.This series of observations raised the possibility of more highly individualized SERM breast cancer chemoprevention. However, even though SERMs have been approved by the US FDA for the prevention of breast cancer in high risk women, they are not widely used for that purpose because approximately 50 women must be treated to prevent one case of breast cancer and because of rare but serious side effects.ZNF423 gene on chromosome 16 and the CTSO gene on chromosome 4. An additional goal was to determine whether the functional effect that we had observed for a SNP, rs9940645, at a distance from an ERE motif in intron 2 of ZNF423 might be generalized beyond that single SNP. Since the clinical effect of the SERMs used to treat the subjects enrolled in the P-1 and P-2 trials depended on their binding to ERα and the subsequent binding of ERα to EREs, we began by identifying putative EREs in the areas resequenced across both genes to determine whether any SNPs that we observed during resequencing might either create or disrupt an ERE—as we had observed for the rs6810983 SNP near CTSO.6 In addition, the rs9940645 SNP in ZNF423 that we observed to be associated with SERM-dependent “reversal” of the expression of ZNF423 in our original GWAS was located approximately 200 bp away from an ERE, so we also used ChIP assays to determine whether any of the SNPs that we observed during resequencing that were near  ERE motifs might alter the functional effect of SERMs in the same way that ZNF423 rs9940645 did, i.e., whether they might result in an alteration in ER binding in the presence of a SERM. We also functionally characterized all non-synonymous SNPs (nsSNPs) observed during the resequencing of both ZNF423 and CTSO. If these nsSNPs influenced protein quantity or mRNA expression they might also have functional implications.The purpose of these experiments was to use Next Generation Sequencing (NGS) to characterize genomic sequence variation in areas surrounding the two “top” SNP signals identified during a GWAS performed with DNA samples from the NSABP P-1 and P-2 SERM breast cancer chemoprevention trials [6]. Specifically, we identified and functionally characterized additional genomic sequence variants that had not been observed during GWAS genotyping by deep sequencing approximately 500 kb across both the ZNF423, SNPs that were hundreds of bp away from an ERE motif, displayed behavior similar to that of the intron 2 rs9940645 SNP that we observed during the initial GWAS,6 raising the possibility that this type of effect for SNPs at a distance from EREs might be relatively common. In addition, during resequencing we identified 10 nsSNPs in ZNF423 and one in CTSO  even though none of these SNPs was within an ERE motif. In order to determine whether the putative ERE sequence motifs that we studied in the lymphoblastoid cell lines (LCLs) would also bind to ER in breast cancer cell lines in the presence of E2, we repeated the ChIP assays in the presence of vehicle and E2 using two ER + breast cancer cell lines—ZR-75-1 and MCF-7. As shown in Supplementary Figure ZNF423 sequences in both cell lines, and for the CTSO sequences it was 6 of 9 in one cell line and 5 of 9 in the other. For the LCLs, in addition to E2 exposure, we also studied the cells after treatment with E2 plus 4OH-TAM. In those experiments, 5 of 6 functional ZNF423 and all of the functional CTSO EREs either lost ERα binding or the binding displayed a striking decrease, as anticipated  mapped 401 bp from a functional ERE in intron 5 of the ZNF423 gene, as shown graphically in Fig. P < 0.01 when compared with vehicle treatment) . This pair of SNPs is located 238,000 bp distant from the rs9940645 SNP in intron 2 of ZNF423 that we had originally shown to be associated with enhanced ERα binding for the variant allele after treatment with E2 plus 4OH-TAM in our original P-1 and P-2 GWAS.6 In addition, the two ZNF423 intron 5 SNPs (rs746157 and rs12918288) were not in LD with the intron 2 rs9940645 SNP, suggesting that any functional effects of the intron 5 SNPs would be independent of functional effects mediated by the rs9940645 SNP in intron 2. We next tested the possible effect of the rs746157 and rs12918288 SNPs on the expression of ZNF423 and, downstream, the expression of BRCA1.We observed that, in the presence of E2 alone, 4 SNPs near 4 ERE motifs in the ene Fig. , and 4 Sene Fig.  displayeted Fig. . Howeverype Fig. —similar nt) Fig. . It shouZNF423, as shown graphically in Fig. ZNF423 might also be capable of regulating ZNF423 transcription. The results are shown in Fig. ZNF423 intron 2 to make it possible to isolate the effect of rs746157 and rs12918288 SNPs in intron 5 separate from the effect of the intron 2 SNP. These results can be compared with those shown in Fig. ZNF423 intron 2. In the case of the intron 5 SNPs, there was not a SNP-dependent difference in the E2-dependent induction of ZNF423 by E2, but there was significantly greater induction of ZNF423 mRNA expression in LCLs homozygous for variant compared with those homozygous for WT SNP genotypes when 4OH-TAM was present in addition to E2  near the ERE motif in intron 5 of  E2 Fig. . FurtherZNF423 and 1 in CTSO which would encode variant allozymes and, as a result, could potentially have functional implications, both for the protein expression of these two genes and for their downstream effect on BRCA1 expression. Therefore, we determined whether the nsSNPs might alter protein quantity, a common effect of nsSNPs.8 To do that, we performed site-directed mutagenesis using COS-1 cells and protein expression was quantified by Western blot analysis  substitution, resulted in a significant decrease in ZNF423 protein expression   change in the encoded amino acid sequence failed to alter CTSO protein expression (data not shown).We also identified 10 nsSNPs in 05) Fig. . The sin11 To test that possibility for the R617Q ZNF423 allozyme, MG132, a proteasome inhibitor, and the autophagy inhibitor 3MA were incubated with transfected COS-1 cells to determine whether they might alter expressed protein level for the ZNF423 Q617 variant allozyme. However, we failed to observe significant alteration in protein expression as a result of the inhibition of protein degradation by either MG132 or 3MA   Fig. , suggest4 and both drugs have been approved by the FDA for the prevention of breast cancer in these women—primarily on the basis of the results of the NSABP P-1 and P-2 clinical trials.4 As a result, the US Preventive Services Task Force and NICE in the UK have recommended that all women at high risk for breast cancer consider preventive therapy with one of these drugs. However, neither of these SERMs is widely prescribed to prevent breast cancer, in part because approximately 50 women must be treated for each case of breast cancer prevented and because of rare,12 but potentially serious side effects of SERM therapy.13 In an effort to determine whether it might be possible to more highly “individualize” SERM breast cancer prevention, we recently performed a GWAS using DNA samples from women who participated in the P-1 and P-2 clinical trials. That study identified SNP signals in or near the ZNF423 and CTSO genes. We also showed that both genes regulated the expression of BRCA1 in a SNP and SERM-dependent fashion. In the case of ZNF423, that process involved a novel mechanism during which SERMs, in the presence of a variant SNP genotype, “reversed” the E2 dependence of ZNF423 and BRCA1 induction, i.e., mRNA expression from both ZNF423 and BRCA1 was induced by E2 when the WT, but not the variant SNP sequence was present, but the reverse was true in the presence of a SERM  might be; and what the implications of this SNP genotype-dependent, drug-dependent effect might be for variation in clinical SERM effect, i.e., what the implications might be for SERM pharmacogenomics.Tamoxifen or raloxifene can reduce the occurrence of breast cancer in women at increased risk for this disease by approximately 50%,The present study also has implications with regard to the use of deep sequencing in pharmacogenomics. Next Generation DNA sequencing provides a comprehensive view of the human genome and offers a wealth of information on novel biology. A major rationale for performing this type of study has involved the possible role of rare variants for variation in the phenotype of interest. The present study raises the possibility that an additional application of deep sequencing for pharmacogenomics might involve the definition of variation within DNA binding sequence motifs, in the present case ERα binding motifs. However, a similar approach might also be applied to any nuclear receptor or other transcription factor that binds to specific DNA sequence motifs. Next Generation DNA sequencing, with adequate coverage, might help make it possible to move beyond a focus on individual SNPs, to include variation in sequences encoding transcription factor binding motifs, as demonstrated in this report for EREs, or for SNPs near or hundreds of bp distant from those binding motifs.15Many of the functional studies described in this series of experiments began with the use of an LCL model system that made it possible to select cell lines for study with virtually any common genotype or combinations of genotypes, something which would not be possible using breast cancer cell lines. However, we also used ZR-75-1 and MCF-7 ER positive breast cancer cell lines to confirm the LCL data shown in Fig. CTSO and ZNF423 and subsequent functional genomic studies. The present results also verify and extend a novel pharmacogenomic mechanism by which SNPs at a distance from a transcription factor binding motif, EREs in this case, can have striking effects on the functional implications of binding. Of perhaps greatest importance is the possibility that these SNP-associated effects might include either the alteration, or even the reversal, of drug effect. All of these possibilities will have to be pursued in the course of future studies if we are to approach the ultimate goal of more highly individualized breast cancer chemoprevention.In the present study, we identified additional SNPs that might contribute to inherited variation in SERM breast cancer prevention therapy by deep sequencing across The protocol for this study was reviewed and approved by the Mayo Clinic Institutional Review Board (reference number: MC0831). All cases and controls received a study-specific identifier (an anonymization code number) that permitted the data derived from the DNA samples to be linked to clinical data from the applicable P-1 or P-2 dataset using an “honest broker agent” who was independent from the study. Confidentiality was maintained for all study participants. This study was conducted in accordance with the appropriate protocols. All women whose DNA samples were used in this study gave their consent for participation in the study and for publication of the study results in a peer-reviewed scientific journal.Because the case and control samples used in the present study were obtained from our GWAS, the design of that study will be summarized briefly. Cases were women who experienced invasive breast cancer or ductal carcinoma in situ (DCIS), and controls were women who did not experience those breast events during SERM therapy. A nested matched case-control design was used, with matching on the following factors: 1) trial and treatment arm ; 2) age at trial entry; 3) 5-year predicted breast cancer risk based on the Gail model , 4) history of lobular carcinoma in situ; 5) history of atypical hyperplasia in the breast; and 6) time on study (controls had to be on study at least as long as the time to diagnosis of the breast event for the case). Because 94.2% of the participants treated with tamoxifen or raloxifene were Caucasian, our study was restricted to Caucasian participants to minimize population stratification. Genotypes were determined at the RIKEN Center for Integrative Medical Science with the Illumina Human610-Quad BeadChip. A total of 592,236 SNPs were genotyped for the GWAS and 547,356 SNPs were carried forward for analysis after quality control.20 particularly when the GWAS SNPs used to stratify samples are in linkage disequilibrium with novel variants detected by sequencing.21 Sample preparation, DNA sequencing, library production, capture sequencing and sequence analysis are described in detail in the Supplementary Methods.In our original GWAS, the minor allele of the rs6835859 SNP on chromosome 4, and the major allele of the rs8060157 SNP on chromosome 16 were each associated with increased risk for breast cancer. Because of the cost required to resequence all 1763 of the DNA samples included in the GWAS, we selected a total of 400 samples for resequencing, 199 from cases and 201 from controls. To select case and control samples optimally while also using information with regard to genotypes for the chromosome 4 and 16 SNPs observed in our GWA study, we stratified the GWAS sample according to case-control status as well as joint genotypes for the SNPs on chromosome 4 and 16 and randomly sampled participants from those 18 strata (see Supplementary Table http://meme.nbcr.net/meme/fimo-intro.html to identify ERE motifs. Sequence Alignment/Map (SAM) tools (http://samtools.sourceforge.net/) were used to extract the 500 bp flanking regions from the human reference genome at those sites and to build FASTA files to submit to the FIMO tool. Together with the FASTA file, the FIMO software was also provided with a canonical ERE motif sequence22 to identify putative ERE motifs in which a SNP might create, disrupt or change the function of an ERE. After the FIMO results were obtained, we used BEDTools23 to calculate the distance from variant sites to the locations of ERE motifs.We used motif-based sequence analysis tools to identify ERE motifs that were either overlapping with or were less than 500 bp away from a SNP. Specifically, we applied the Find Individual Motif Occurrences (FIMO) tool 30 Its use in the present study made it possible to evaluate associations between ZNF423 and CTSO SNP genotypes, ZNF423 expression and the downstream effect on the expression of BRCA1. Specifically, LCLs with known genotypes for the ZNF423 or CTSO SNPs that were associated with breast cancer occurrence were cultured as described previously.6 Before E2 and/or 4-hydroxytamoxifen (4OH-TAM) treatment, the cells were cultured for 24 h in RPMI containing 5% (v/v) charcoal-stripped FBS, followed by 24 h of starvation in FBS-free RPMI media. Cells were harvested after treatment for 24 h with E2 (0.01 nM) and/or 4OH-TAM (10−6μM). ChIP assays were performed using the EpiTect® ChIP One-Day Kit . The anti-ERα antibody used for immunoprecipitation was obtained from Thermo Scientific . Each purified ChIP DNA sample (2 μl) was added to PCR reaction mixtures with a final volume of 20 μl. The sequences of primers used to amplify target sequences that contained the SNPs of interest are listed in Supplementary Table The lymphoblastoid cell line (LCL) model system used in these studies has been utilized repeatedly to generate and/or test pharmacogenomic hypotheses arising from clinical GWAS.ZNF423, and three cell lines homozygous for the variant genotypes for those two SNPs were used to perform these experiments. All LCLs used in these experiments were homozygous for the WT genotype for the intron 2 rs9940645 SNP to prevent confusion based on possible effects of the ZNF423 intron 2 SNP. Before E2 treatment, the LCLs were cultured as described above. After serum starvation, 2 × 106 cells from each cell line were cultured for 24 h in 6-well plates in RPMI-1640 media that contained 0, 0.0001, 0.001, and 0.01nmol/L E2. 4OH-TAM was then added to the same media containing 0.01nmol/L E2 with final 4OH-TAM concentrations of 10−9, 10−8, 10−7, 10−6, and 10−5μmol/L, and the cells were cultured for an additional 24 h. Total RNA was then isolated from the cells using the RNeasy® Plus Mini Kit (Qiagen). mRNA expression was determined by qRT-PCR.Three LCLs homozygous for WT genotypes for the intron 5 SNPs rs746157 and rs12918288 in The wild type (WT) human ZNF423 NM_015069) and CTSO (NM_001334) cDNAs were obtained from OriGene Technologies, Inc.  and were subcloned into the eukaryotic expression vector pCMV6-XL4. The inserts were sequenced in both directions to verify their sequences. Site-directed mutagenesis was then performed using the QuikChange II kit  to create expression constructs for each of the variant allozymes that had been observed. Sequences of the primers used to perform site-directed mutagenesis are listed in Supplementary Table 5069 and ZNF423 WT and R617Q variant allozymes as described below for the protein degradation studies. After 24 h, whole cell lysates from the same cells were prepared for Western blot analysis. Specifically, cell lysate supernatants from the transfected cells were assayed for ß-galactosidase activity, and the samples were subjected to electrophoresis on 7.5% TGX gels. After electrophoresis, proteins were transferred to PVDF membranes , and the membranes were incubated with rabbit polyclonal anti-ZNF423  or purified mouse anti-CTSO antibody  diluted 1:500, followed by incubation with a secondary antibody . Immunoreactive proteins were detected using the ECL Western Blotting System  and were quantified using the ChemiDoc™ XRS + System . Data were expressed as percentages of the intensity of WT ZNF423 or CTSO protein on the same gel. ZNF423 mRNA level was quantified by qRT-PCR using the Power SYBR® Green RNA-to-CT™ 1-Step Kit .COS-1 cells were transfected with plasmids encoding ZNF423 Arg617Gln (R617Q) variant allozyme, an allozyme that displayed a significant decrease in protein quantity when compared to WT, was used to perform protein degradation studies. 24 h after transfection with ZNF423 WT or Q617 variant constructs, COS-1 cells were transferred from 100mm plates into 6-well plates. To test for possible proteasome-mediated degradation, the cells were then treated for 6–8 h with either DMSO or 20 µM MG132, a proteasome inhibitor, dissolved in DMSO. To determine whether autophagy might participate in degradation of the variant protein, transfected cells were also treated for 48 h with 10 µM 3-methyladenosine (3MA). Immunoreactive ZNF423 protein was quantified by Western blot analysis after correction for transfection efficiency on the basis of β-galactosidase activity.The All data supporting our findings can be found in the main paper or in supplementary files.Supplementary MethodsSupplementary TablesSupplementary Figures


Arachis hypogaea (peanut), an allotetraploid, in which 1,674 haplotype-based SNPs were included. Results of the array show that 74% of the haplotype-based SNP markers could be validated, which is considerably higher than previous methods used for peanut. The haplotype method has been implemented in a standalone program, HAPLOSWEEP, which takes as input bam files and a vcf file and identifies haplotype-based markers. Haplotype discovery can be made within single reads or span paired reads, and can leverage long read technology by targeting any length of haplotype. Haplotype-based genotyping is applicable in all allopolyploid genomes and provides confidence in marker identification and in silico-based genotyping for polyploid genomics.Accurate identification of polymorphisms from sequence data is crucial to unlocking the potential of high throughput sequencing for genomics. Single nucleotide polymorphisms (SNPs) are difficult to accurately identify in polyploid crops due to the duplicative nature of polyploid genomes leading to low confidence in the true alignment of short reads. Implementing a haplotype-based method in contrasting subgenome-specific sequences leads to higher accuracy of SNP identification in polyploids. To test this method, a large-scale 48K SNP array (Axiom Arachis2) was developed for  The identification of functional variation controlling traits of interest relies on the ability to discern all true variation between accessions with discrete genotypes. The power of next-generation (short reads) and third-generation (long reads) sequencing is the ability to identify all variants. The size and complexity of polyploid genomes have led to the reliance on single nucleotide polymorphism (SNP) arrays and complexity reduction sequencing strategies such as genotyping-by-sequencing (GBS) and restriction site-associated DNA sequencing RADSeq; . These min silico of a polymorphism between accessions that is only between subgenomes. This problem is confounded by variance in sequence coverage across genomic loci.With the cost of sequencing continuing to plummet and long read technologies increasing in efficiency and accuracy, the ability to generate sequence on whole populations is increasing. Having access to all potential polymorphisms can increase the resolution of genetic mapping and genome-wide association studies. In polyploids, confidence in sequence-based prediction of genotypes is confounded by the uncertain alignment of short reads in the genome. Mapping of homeologous reads causes confusion by the appearance A method of untangling subgenomes in mapping experiments has been proposed and implemented in cultivated peanut [sliding window extraction of explicit polymorphisms (SWEEP)] and octoploid strawberry . Bassil Arachis, Axiom Arachis2. Finally, a pipeline was developed to utilize the haplotype-based genotyping method as an easy-to-use one command program. Haplotype-based genotyping should be broadly applicable across allopolyploid species.We propose a method of sequence-based genotyping in polyploids that instead of applying a filter to individual sites collects observed haplotypes from sequence reads and contrasts those haplotypes between accessions to identify polymorphic markers. To demonstrate the accuracy of the method, haplotype-based markers were validated on a new 48k SNP array for Arachis hypogaea accessions were re-sequenced to 10X coverage (1). The models used for machine learning were trained using the true and false SNP sets from a previous array, Axiom Arachis v1  sequencing with an expected sequence coverage of 10X. A coverage-based strategy would suggest that any sites be ignored with coverage above 15 reads and below four reads for each accession. Using the diploid progenitor genome sequences, an estimate can be made of the number and location of polymorphisms between subgenomes by fragmenting one genome into overlapping short sequences and mapping them to the alternate genome. Doing this with Arachis duranensis (A genome) fragments mapped to Arachis ipaensis identifies potentially 8,605,615 polymorphic sites that represent potential false positive SNP calls. After calling SNPs between the three accessions and filtering for expected coverage, there are 2,898,744 of those sites that fall within the expected coverage. In an experiment using coverage-based filtering, all these SNPs are potential false positives. Further, there are 25,459 sites where at least one of the three genotypes is scored as “homozygous” which would be considered a true SNP. Looking at each accession separately and filtering for expected coverage, there are 213,791, 113,340, and 81,120 false SNP sites where only one allele is represented and would be called SNPs, but are false positives. Given that the potential true polymorphisms between accessions are low, these potential false positives would drown out the true signal in a sequence-based genotyping experiment. Even when filtering for higher than expected coverage, the potential is high that many of the SNPs identified will be false positives.The question becomes how to differentiate Recently, a pipeline was developed called SWEEP . This apFigure 1). To demonstrate this method, we developed HAPLOSWEEP, which collects all observed overlapping haplotypes within a user-specified base pair window and contrasts them between accessions to identify those haplotypes that are polymorphic between accessions .Accurate identification of polymorphisms from sequence data in polyploids can be done after contrasting collected observed haplotypes within contiguous sequence reads that align to a common locus. A polymorphic haplotype is identified by containing at one position an “anchor” base that is the same in all observed haplotypes and a polymorphic position that is observed in at least one accession  of the haplotype-based markers could be validated  were validated on the array. This result confirms that the problem of the multiple mapping of short reads is not fully solved by using a polyploid reference assembly.The remaining markers were identified using a machine learning approach outlined in Korani et al. (unpublished). These markers were validated with a true positive rate (TPR) of 75%.The true and false SNP sites obtained from both SNP arrays provide a resource for estimating validation rates. The sets provide 44,087 validated true SNP sites and 42,767 validated false SNP sites. These sets can be used to estimate validation for called haplotypes. Using whole genome resequencing from accessions used to design the array, haplotype markers were called in sets of two, three, four, and five accessions  within a distance that can be observed on a contiguous short read. In peanut, there are potentially 8,605,615 polymorphisms between the A and B subgenomes. These polymorphisms were identified by mapping fragmented, overlapping sequences from Increased sensitivity and utility can be accomplished by incorporating paired read information. With that aim, a separate module was designed called HAPLOSWEEP_LONGRANGE. This module uses the same methodology to contrast observed haplotypes between accessions, but collects them differently. The data structure stores information on every read’s status as a pair and the aligned base at every queried position. After haplotypes are captured that occur within single reads, an additional step is done to collect all haplotypes that occur between paired reads. Then the paired haplotypes are contrasted in the same way as haplotypes occurring within single reads. Utility for this module is that large insert size libraries can be used to identify polymorphic markers across long distances.The haplotype-based genotyping framework can be applicable to diploid crops as well. Using long read technologies or large insert size libraries, long haplotypes can increase the resolution of genotypes and precision of mapping. An additional module, HAPLOSWEEP_DIPLOID, was designed to contrast haplotypes of any length. As haplotypes are observed, the program collects all haplotypes within the user-specified window up to the maximum haplotype length specified. When the window is not large enough to observe a maximum length haplotype, shorter haplotypes are collected.The modules described above are a part of a one-command pipeline that takes as input a vcf file of called SNPs and bam files of alignments. Maximum utility is achieved if no filtering of the vcf file is done beforehand as the haplotyping method needs to have access to all possible polymorphisms. The user can call any of the three modules based on needs of the experiment and set window size (length of reads) and max haplotype length. Additionally, given a set of called haplotypes, a population of individuals can be genotyped with the output a matrix of called genotypes for each individual.A. ipaensis genome is estimated to show 99.96% similarity to the cultivated B subgenome  or strawberry (octoploid), the pipeline should be applicable. It is designed to find at least one contrasting haplotype. As long as the haplotypes are the same on the other subgenomes, the markers will be identified. In a situation where there are more than two alleles at a specific site, these sites may be filtered out in a higher order polyploid. The pipeline is not designed to work for autopolyploids.Arachis SNP arrays, estimates of TPRs are above 89% for novel marker discovery. In order to allow all users access to this method, we developed a one-command pipeline that will call haplotype-based markers and use them to genotype populations. Shown to be accurate in allotetraploid peanut, this method should provide accurate marker identification in all allopolyploid species.Large-scale validation using the Axiom Arachis2 48k SNP array has shown that haplotype-based genotyping in polyploids is more accurate than other methods. By leveraging the true and false SNP sites derived from the version 1 and version 2 JC, PO-A, and SJ conceptualized the research. JC and WK performed the experiments, conducted data analysis, and curated data. JC wrote the original draft and was responsible for data visualization. SJ, PO-A, and JC revised the manuscript.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.


MC1R, Y-chromosome sequence polymorphisms, and genome-wide SNP genotyping.The Ashanti Dwarf Pig (ADP) of Ghana is an endangered pig breed with hardy and disease resistant traits. Characterisation of animal genetic resources provides relevant data for their conservation and sustainable use for food security and economic development. We investigated the origin and phylogenetic status of the local ADP of Ghana and their crosses with modern commercial breeds based on mtDNA, MC1R alleles of both Asian and European origin. European alleles for spotting are present at a low frequency in the sample set, and may account for the occurrence of spotted piglets in some APD litters. PCA analysis of SNP data revealed a strong location and breed effect on clustering of local Ghanaian pigs. On a global level, Ghanaian local pigs cluster closely with European pigs of commercial origin, but we identified intervals via FST analyses that may elucidate loci for ADP specific traits.The study involved 164 local pigs sampled from the three agro-ecological zones of Ghana. Analyses of the mitochondrial D-loop region and Y-chromosome sequences revealed both European and Asian genetic signatures, with differences between the geographical zones. Black coat colour is the most predominant within the breed, with black The presence of both European and Asian contributions, with differences between geographical zones probably reflects trading and colonial influences. Understanding the effects of admixture on important adaptive and economic traits of the ADP and other local breeds in Africa is critical for developing sustainable conservation programmes to prevent the decline of these genetic resources.The online version of this article (doi:10.1186/s12864-017-3536-6) contains supplementary material, which is available to authorized users. Sus scrofa) display enormous phenotypic diversity in terms of shape, colour, size, production and reproduction abilities. Indigenous pig breeds in China, for instance, are well-known for their unique reproductive and lactation performance, good meat quality, strong adaptability and disease resistance traits ..17].SRY) gene which is responsible for sex determination in mammals ). mammals –27. MitoMC1R) locus is the most consistently polymorphic  in a total volume of 20 μl containing 25 ng genomic DNA, 1.0 Mm MgCl2, 50 Mm KCl, 10 Mm Tris–HCl, pH 8.3, 200 μM dNTPs, 0.5 units Ampli-Taq Gold [Perkin Elmer  9600], and 0.5 μM each of forward and reverse primer. To activate AmpliTaq Gold, initial heat denaturation was carried out at 94 °C for 10 min followed by 32 cycles each consisting of 45 s at 94 °C, 45 s at 53 °C and 45 s at 72 °C. The final extension lasted for 7 min at 72 °C. Sequencing reactions were purified, run and analysed as above.Two primer pairs were used to amplify the majority of the single exon SRY gene were used to amplify DNA products from male pigs as previously described ) to perform the PCA analysis. 61565 SNPs were present at the start of the analysis. The filtering parameters were as follows: the maximum missing rate per SNP was set at 10%, minimum allele frequency at 5%, and maximum individual missing rate at 10%. 3305 variants were removed due to missing genotyping data, 6951 due to minor allele frequency threshold, and 4 samples due to individual missing SNP rate. After filtering the 51309 SNPs remained, with 67 individuals successfully typed.The SNP genotype calls were exported and loaded in PLINK  for anis (as in  with thehttps://david.ncifcrf.gov/) [p < 0.05) from the DAVID analysis are shown in Additional file Intervals identified by the analysis were scrutinised for gene content in Ensembl. The comparative genomics options in the web interface allowed identification of any transcripts not fully annotated in the current pig genomic build Sscrofa10.2) by comparison with human GRCh38p7). Gene lists created from these intervals of interest are shown in Additional file 0.2 by corf.gov/) , 38. Owip7. Gene


NGS) and bioinformatics tools have greatly facilitated the characterization of nucleotide variation; nonetheless, an exhaustive description of both SNP haplotype diversity and of structural variation remains elusive in most species. In this study, we sequenced a representative set of 102 short‐season soya beans and achieved an extensive coverage of both nucleotide diversity and structural variation (SV). We called close to 5M sequence variants  and noticed that the number of unique haplotypes had plateaued within this set of germplasm (1.7M tag SNPs). This data set proved highly accurate (98.6%) based on a comparison of called genotypes at loci shared with a SNP array. We used this catalogue of SNPs as a reference panel to impute missing genotypes at untyped loci in data sets derived from lower density genotyping tools (150 K GBS‐derived SNPs/530 samples). After imputation, 96.4% of the missing genotypes imputed in this fashion proved to be accurate. Using a combination of three bioinformatics pipelines, we uncovered ~92 K SVs  and estimated that over 90% of these were accurate. Finally, we noticed that the duplication of certain genomic regions explained much of the residual heterozygosity at SNP loci in otherwise highly inbred soya bean accessions. This is the first time that a comprehensive description of both SNP haplotype diversity and SV has been achieved within a regionally relevant subset of a major crop.Next‐generation sequencing ( Geneticet al., et al., et al., et al., et al., NGS has facilitated greatly the development of methods to genotype very large numbers of nucleotide variants such as single nucleotide polymorphisms (SNPs) , a palaeopolyploid (diploidized tetraploid)] to identify both nucleotide and structural variants using a combination of several bioinformatics tools. We then measure the accuracy of these variants through validation experiments and describe their distribution in the soya bean genome. We also show the impact of joint analysis of nucleotide and structural variants in elucidating the cause of residual heterozygous genotypes observed in inbred lines that are expected to be fixed at all loci.In this study, we describe the WGS of 102 short‐season soya bean accessions  , to compare and identify the novel variants detected in this study.Two SNP‐calling pipelines were used: SOAPsnp ]  were noted. To detect SVs that had a likely functional impact on genes, we proposed four possible scenarios: (1) a SV was located inside a gene, (2) a SV began in an intergenic region (upstream) and terminated in a gene, (3) a SV began in a gene and terminated in an intergenic region (downstream) and (4) a SV encompassed the gene completely  and 38 random SVs with a focus on translocations and inversions for a PCR‐based validation. Primers were designed using Primer3Plus  repository with the SRP# Study accession, SRP094720 [Project name: Fast‐WGS.https://bitbucket.org/jerlar73/fast-wgs.Project home page: Operating system: Linux.Programming language: Bash and Python.License: GNU GPL v3.Any restrictions to use by nonacademics: No.DT, JL, and FB conceived the project. JL and DT contributed to programming. LO, EC and IR contributed to sample selection. AT evaluated SV validation by PCR. DT and FB contributed to writing the manuscript.Data S1 Description of Fast‐WGS. Bioinformatics analytical pipeline for whole‐genome sequencing analysis.Data S2 Significant contribution to the public SNP dataset (dbSNP) for Glycine spp.Figure S1 Cladogram of 441 short‐season soybean accessions from Canada produced using a set of close to 80k SNP markers. Arrows indicate the samples selected for whole‐genome sequencing.Figure S2 Distribution of allele frequency for sequence variants located in coding regions and predicted to have a high impact on gene functionFigure S3 Population genetics analysis.Figure S4 Correlation between number of SVs and chromosome length. Deletions (DEL), insertions (INS), copy‐number variations (CNV), duplications (DUP), inversions (INV), and translocations (TRANS).Figure S5 Different cases used to identify structural variants that could directly impact the function of a gene.Figure S6 Visualized example of PCR‐based genotyping of 10 samples for E4 gene. E4 is the wild type form and e4 resides an insertion.Table S1 Information of sequenced short‐season soybean accessions with name and number of trimmed reads (Phred score >32).Table S2 List of genes containing variants predicted to have a high impact on gene function.Table S3 PCR‐based validation of SVs called on the basis WGS data.Table S4 Concordance of WGS‐based genotyping and PCR‐based genotyping results for a deletion in E3 gene and an insertion in E4 gene.Table S5 Primers used for PCR‐based SV validation.Click here for additional data file.


Prostate cancer (PCa) is one of the most significant male health concerns worldwide. Single nucleotide polymorphisms (SNPs) are becoming increasingly strong candidate biomarkers for identifying susceptibility to PCa. We identified a number of SNPs reported in genome-wide association analyses (GWAS) as risk factors for aggressive PCa in various European populations, and then defined SNP-SNP interactions, using PLINK software, with nucleic acid samples from a New Zealand cohort. We used this approach to find a gene x environment marker for aggressive PCa, as although statistically gene x environment interactions can be adjusted for, it is highly impossible in practicality, and thus must be incorporated in the search for a reliable biomarker for PCa. We found two intronic SNPs statistically significantly interacting with each other as a risk for aggressive prostate cancer on being compared to healthy controls in a New Zealand population. It has become one of the most significant male health concerns worldwide2. An individual is diagnosed as having high-risk or aggressive PCa based on the classification by the American Urological Association3, when the clinical T stage ≥cT2c, and/or the Gleason score ≥8, and/or the serum prostate serum antigen (PSA) level >20ng/ml4.Prostate cancer (PCa) is highly prevalent, and around 1 in 6 patients are at risk of developing the aggressive form of the disease5, various studies have also shown that genetic interactions with biological and behavioral factors play an important role in the overall risk and prognosis of PCa8. Variations in the genome are a major contributor to the differences in disease susceptibly amongst individuals9. Single nucleotide polymorphisms (SNPs) are the most commonly identified variations in a genome.Although a hereditary aspect is well known for this disease10 is very appealing among researchers working on risk factors for various cancers13, including prostate cancer14. Here we have identified a SNP-SNP interaction as a risk factor for aggressive PCa, by comparing the data generated after carrying out SNP genotyping using the SEQUENOM MassARRAY iPLEX® assay, and the TaqMan® assay (depending on the gene of interest) from the DNA extracted from blood samples. These samples were taken from a New Zealand cohort of men with self-reported European ethnicity that have been clinically diagnosed with aggressive and non-aggressive PCa, and healthy controls with no reported symptoms of the disease. Symptoms include increased urination during night time along with a frequent urge to urinate problems maintaining a steady flow of urine, hematuria and dysuria15. Our results indicate a strong influence of gene x environment interaction in overall gene expression and epistasis.Analysing the role of SNP-SNP interactions and epistasisPatients with a clinically established diagnosis of PCa (aggressive and non-aggressive) from the Auckland Regional Urology Registry , and certain private practices in the Waikato region of New Zealand were sent invitations along with the written consent forms to participate in this study between the years 2006 and 2014. Eventually, a total of 254 patients with various grades of PCa voluntarily participated in our study after providing us with written informed consent. . Additionally, 369 males from the Auckland region of New Zealand with no reported symptoms of PCa were considered as healthy controls for this study , recruited by advertising in and around the University of Auckland. Written informed consent for participation in the study was also obtained from the male healthy controls.16, care was taken to invite men to participate in this study that were between the ages of 40 to 90 years at the time of diagnosis for patients with PCa, and at the time of recruitment for healthy controls. (17). The average age of men with aggressive PCa was calculated to be 66 years, 67 years for men with non-aggressive PCa and 58 years for healthy controls.Each individual participating in this study completed a demographic and lifestyle questionnaire. Because of the influence of age in this diseasentrols. 17. The Patient blood samples were collected at respective outpatient clinics at the Auckland, North Shore and Counties Manukau Hospitals, New Zealand. The blood samples of healthy controls were collected at the Faculty of Medical and Health Sciences, the University of Auckland, New Zealand and the New Zealand Blood Bank, Great South Road Centre, Epsom, Auckland New Zealand.® tubes (Becton Dickinson) containing EDTA by a trained phlebotomist. DNA was extracted using a QIAamp genomic DNA kit  following the manufacturer’s protocol, with the aid of a fully automated QIAcube . The DNA samples were diluted to 5.0ng/μl as per requirements of the SEQUENOM MassARRAY iPLEX® assay protocol.Blood samples from each participant were collected in Vacutainer® assay or by TaqMan® SNP genotyping assay, as discussed in Vaidyanathanet al., 201718.136 SNPs, located in 66 genes and some undefined chromosomal locations were identified by a thorough literature search of GWAS for both aggressive PCa and PCa. Care was taken to select SNPs that were identified as significantly associated with risks for PCa and aggressive PCa in European populations only. Research papers were only considered if published in and after the year 2000, in order to be in concordance with the current trends in PCa research. The final SNPs selected to be genotyped were at the research team’s discretion, either by SEQUENOM MassARRAY iPLEX® assay for the candidate SNPs was carried out in the Auckland UniServices Sequenom Facility at The Liggins Institute, Auckland, and AgResearch Limited, Mosgiel, New Zealand, using a custom-designed multiplex gene panel and iPlex chemistry. The genotype calling was carried out by using the standard post-processing calling parameters from the SEQUENOM Type 4.0 software.SNP genotyping by SEQUENOM MassARRAY iPLEX® SNP genotyping  was carried out on a panel of genes that failed to be genotyped using the SEQUENOM MassARRAY iPLEX® assay. The primers used were either obtained pre-designed from ABI or were custom-made using Assay-by-Design service by ABI, and the protocol provided by the manufacturers was followed20.SNP genotyping using the TaqMan17). SNPs that failed the HWE in patients with PCa were still considered for analysis, as the SNPs may have failed to be in equilibrium in the patient population due to the influence of the risk allele and hence should not be ignored from a case-control study like ours22. The statistical significance was set to p≤0.000123.26 SNPs were removed for being in linkage, and a further 5 SNPs were removed for failing the Hardy-Weinberg Equilibrium (HWE) in the healthy controls, thereby reducing the total SNPs analyzed to 105  clustering, is based on pairing up the SNPs based on similarity of genetic identity. This IBS clustering is used in order to test if the SNPs of two individuals belong to the same population or not18. Following this stratification, we performed a standard case-control association test using the Cochran-Mantel-Haenszel test (1 degree of freedom) to analyse the SNP-disease association that is conditional on the clustering18. The slower ‘--epistasis’ command was used to test for epistasis using logistic regression23. It is the most accurate test to define SNP-SNP interactions using PLINK23.Analysis of the data for SNP-SNP interactions associated with aggressive PCa was carried out using PLINK software version 1.07FGF10) gene24, has been identified to be associated with the SNP rs4809960, an intronic SNP present in chromosomal position 20q13 near the gene cytochrome P450 family 24 subfamily A member 1 (CYP24A1)25, such that the latter SNP raises the odds of having the prior.Raw data from the current studyClick here for additional data file.Copyright: © 2017 Vaidyanathan V et al.2017Data associated with the article are available under the terms of the Creative Commons Zero "No rights reserved" data waiver (CC0 1.0 Public domain dedication).Epistasis results after analysis of the data for SNP-SNP interactionsClick here for additional data file.Copyright: © 2017 Vaidyanathan V et al.2017Data associated with the article are available under the terms of the Creative Commons Zero "No rights reserved" data waiver (CC0 1.0 Public domain dedication).11. We believe this is the first study on SNP-SNP interactions associated with aggressive PCa carried out with patients from a New Zealand population.Epistatic effects that are crucial to define various biologically-intuitive models of interaction between two SNPs have already been observed in a variety of speciesCYP24A1 has been reported by Holtet al., (2010) to be associated with prostate cancer-specific mortality, and was not evolutionarily conserved25. It was also found to have an effect on the body mass index (BMI), but due to a small sample size the hazard ratios for the BMI strata were not considered reliable enough to be reported25. The protein encoded byCYP24A1 initiates the degradation of the physiologically active form of Vitamin D3 (VD3)26. VD3 is an important hormone that is actively involved in regulating cell proliferation in the prostate, and has also been identified to have increased expression in PCa cell lines27. It is well established that, with ageing, the skin cannot synthesize VD3 as effectively as desirable and the kidney’s ability to convert VD3 to its active form decreases28. This is of relevance because PCa has always been considered as a disease of elderly men29 who have had less exposure to sunlight and thereby Vitamin D330. It is even more intriguing for the other epistatic SNP to be identified inFGF10.The SNP rs4809960 in the geneet al. (2013), during mesenchymal development, eFGF10 protein can trigger PCa development through increased androgen receptor expression in the neoplastic epithelium31. It is also worthy to mention that FGF10 is closest to FGF7 based on its evolutionary history32, and according to Emotoet al. (1997), is suggested to have no activity for fibroblasts32. We do not agree with this, because fibroblasts in certain organs, senesce due to aging33, and can promote tumour invasion34. This logical progression of ageing-led senescence and promotion of tumour invasion holds true for ageing and risk of aggressive PCa16 as well.According to PaulFGF10 may be causing alterations in gene expression, perhaps due to the prevalent external/environmental conditions in the elderly men with PCa. Our theory is based on the recent discovery in a study by Zhanget al. (2007) that even intronic SNPs (such as the ones identified inFGF10 andCYP24A1) can change the outcome and usage of exons36. This unique and novel epistatic finding emphasizes the fact that intronic SNPs (and SNP-SNP interactions) can also have a significant effect on the risk of diseases such as aggressive PCa, and need to be investigated further.We suggest that the intronic SNP rs2121875 in the geneThe data referenced by this article are under copyright with the following copyright statement: Copyright: © 2017 Vaidyanathan V et al.Data associated with the article are available under the terms of the Creative Commons Zero "No rights reserved" data waiver (CC0 1.0 Public domain dedication).10.5256/f1000research.11027.d15860517Dataset 1: Raw data from the current study. DOI,10.5256/f1000research.11027.d15860637Dataset 2: Epistasis results after analysis of the data for SNP-SNP interactions. DOI, FGF10) gene24, has been identified to be associated with the intronic SNP rs4809960, an intronic SNP present in chromosomal position 20q13 near the gene cytochrome P450 family 24 subfamily A member 1 (CYP24A1). The protein encoded by CYP24A1 initiates the degradation of the physiologically active form of Vitamin D3 (VD3) 26 which is an important hormone that is actively involved in regulating cell proliferation in the prostate, and has also been identified to have increased expression in PCa cell lines. The epistatic effect of the SNP-SNP interactions suggested by the authors may be relevant in view of many recent studies showing intronic mutations which can exert their effect on protein coding exons. The recent observation of decreasing Vitamin D3 levels worldwide further support the role of environmental factors in these gene environment interactions. Future studies may help in understanding the role of SNPs and environmental interactions.The authors have carried out SNP genotyping in from a New Zealand cohort of men with self-reported European ethnicity that have been clinically diagnosed with aggressive and non-aggressive PCa, and healthy controls. They have identified a number of SNPs from the GWAS from various European populations and described SNP-SNP interaction as a risk factor for aggressive PCa in a New Zealand cohort. The intronic SNP rs2121875, an on chromosomal position 5p12 near the fibroblast growth factor 10 (We have read this submission. We believe that we have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. The manuscript identified a number of SNPs in GWAS as risk factors for PCa in various European populations as well as defined SNP-SNP interactions, using PLINK software, with nucleic acid samples from a New Zealand cohort. The approach which authored used to find a gene x environment marker for aggressive PCa gene x environment interactions can be adjusted statistically, however, it is highly impossible in practicality. The manuscript compiled most of recent literature available on the subject and propose justified discussion on the research question.  Since I do not have sufficient expertise in statistical analyses used to elaborate the key findings of the manuscript, I am reluctant to comment on the authenticity and validity of conclusion.I have read this submission. I believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard.


Francisella tularensis is a small, Gram-negative bacterium and the causative agent of tularemia, a potentially life-threatening infection endemic in the United States, Europe and Asia. Currently, there is no licensed vaccine or rapid point-of-care diagnostic test for tularemia. The purpose of this research was to develop monoclonal antibodies (mAbs) specific to the F. tularensis surface-expressed lipopolysaccharide (LPS) for a potential use in a rapid diagnostic test. Our initial antigen capture ELISA was developed using murine IgG3 mAb 1A4. Due to the low sensitivity of the initial assay, IgG subclass switching, which is known to have an effect on the functional affinity of a mAb, was exploited for the purpose of enhancing assay sensitivity. The ELISA developed using the IgG1 or IgG2b mAbs from the subclass-switch family of 1A4 IgG3 yielded improved assay sensitivity. However, surface plasmon resonance (SPR) demonstrated that the functional affinity was decreased as a result of subclass switching. Further investigation using direct ELISA revealed the potential self-association of 1A4 IgG3, which could explain the higher functional affinity and higher assay background seen with this mAb. Additionally, the higher assay background was found to negatively affect assay sensitivity. Thus, enhancement of the assay sensitivity by subclass switching is likely due to the decrease in assay background, simply by avoiding the self-association of IgG3.The CDC Tier 1 select agent  Francisella tularensis is a Gram-negative, facultative intracellular pathogen that causes the zoonotic infection tularemia  x 100. The dose-response curves (% inhibition vs mAb concentration) for each 1A4 subclass variant then were created. IC50 values  were calculated from the sigmoidal dose-response curve model using SigmaPlot 13.0 .The LPS binding affinities of each 1A4 subclass variant were compared by a modified method of competition ELISA described by Fránek et al. . Microti450 was measured as described above.Direct ELISA was used to investigate potential antibody-antibody interactions. Microtiter plates were coated overnight at RT with 2 μg/mL of 1A4 IgG3, IgG1 or IgG2b mAb, washed with PBS-T, and then blocked with blocking solution at 37°C for 1 hour. After washing with blocking solution, the plates were incubated at RT for 1.5 hours with two-fold serial dilutions of 1A4 IgG3, IgG1 or IgG2b mAb HRP conjugate. After the incubation, the plates were washed and developed, and ODKD) was calculated using a steady-state model in BIAevaluation software version 2.0.1 . Accuracy of the model fitting was assessed by χ2 parameter.All surface plasmon resonance (SPR) experiments in this study were performed using a BIAcore X100 instrument . All analysis was conducted in HBS-EP+  as a running buffer and diluent. The steady-state affinity (F. tularensis LVS LPS was immobilized on a streptavidin (SA)-coated sensor chip ; a second flow cell surface was left unmodified for reference subtraction. The biotinylated LPS was prepared using EZ-Link Sulfo-NHS-LC-Biotin (Pierce Biotechnology). To generate sensorgrams, different concentrations of 1A4 IgG3 (10.4–333 nM), 1A4 IgG1 (1.7–8 μM) or 1A4 IgG2b (0.1–3.3 μM) mAbs were injected over the sensor chip surface with a flow rate of 30 μL/min for 180 seconds, followed by passive dissociation for 300 seconds (or 600 sec for 1A4 IgG3 mAb). Between each cycle, the sensor chip surface was regenerated with 3M MgCl2 (IgG1 and IgG2b) or 10 mM NaOH and 0.5% SDS (1A4 IgG3).SPR experiments were carried out using two different assay configurations. For the first configuration, biotinylated For the second assay configuration, anti-mouse antibody was covalently immobilized via amine-coupling reaction on a CM5 sensor chip . The immobilization and assay were performed using a mouse antibody capture kit  with the instructions provided by the manufacturer. Each analysis cycle began with antibody capture by injecting 1 μg/mL of mAb 1A4 IgG3, IgG1 or IgG2b over the sensor chip surface at a flow rate of 30 μL/min for 60 seconds; a flow cell was left un-injected for reference subtraction. LPS at various concentrations  then was injected over the chip surface at a flow rate of 30 μL/min for 120 seconds, followed by passive dissociation for 300 seconds. Between each cycle, 10 mM glycine HCl (pH 1.7) was injected for 60 seconds to regenerate the chip surface.F. tularensis LPS-specific IgG3 mAb 1A4 was produced from mice immunized with heat-inactivated F. tularensis LVS bacteria. Specificity of mAb 1A4 was evaluated by Western blotting in comparison with a commercially available F. tularensis LPS mAb FB11. The results showed that mAb 1A4 reacted with purified LPS from F. tularensis LVS in a similar ladder-banding pattern as seen with mAb FB11, suggesting that mAb 1A4 recognizes the LPS structure , confirming that mAb 1A4 recognizes the O-antigen moiety of LPS.tructure . mAb 1A4To investigate the contribution of antibody constant region to assay sensitivity, mAbs 1A4 IgG1 and IgG2b were isolated from the parental IgG3 using a modified method of sib selection described previously . cDNA clF. tularensis LPS were developed by using different combinations of the 1A4 subclass variants. The LOD (ng/mL and CFU/mL) of each mAb combination was determined and is summarized in Tables 450 in the absence of purified LPS or inactivated bacterial cells), which were used for the LOD calculations, are presented in 5 CFU/mL (Tables 4 CFU/mL) were obtained when the 1A4 IgG3 and 1A4 IgG3 HRP were not used in the assays  of 84 nM. Analysis of 1A4 IgG1 and IgG2b showed a dramatic change in binding as a result of subclass switching . Binding strength of each unlabeled mAb is then determined by its ability to outcompete 1A4 IgG1 HRP and is reported as the IC50, or the concentration of unlabeled mAb that inhibits the binding of 1A4 IgG1 HRP conjugate by half. Competition ELISA results for 1A4 IgG3, IgG1 and IgG2b are shown in 50 values, LPS binding affinity of the 1A4 subclass variants are as follows: IgG3 >> IgG1 > IgG2b.Competition ELISA was also used to study LPS binding of 1A4 subclass variants. In this assay, microtiter plates were coated with a low concentration of LPS and a saturating concentration of HRP-labeled 1A4 IgG1 was used. This assay was designed to estimate the binding affinity of unlabeled mAbs. When mixed with HRP-labeled 1A4 IgG1, unlabeled mAb will compete with the HRP conjugate for binding to LPS, resulting in a reduction in ODAntibody-antibody interactions were investigated using a direct ELISA. It was observed that 1A4 IgG3 interacted with 1A4 IgG3 itself, but no significant interaction of the IgG3 with IgG1 or IgG2b was observed . We inveF. tularensis LPS-specific mAb was isolated in order to develop a rapid POC diagnostic for tularemia. mAb 1A4 IgG3 was generated from a mouse immunized with whole inactivated LVS bacteria (subsp. holarctica). Western blot demonstrated that mAb 1A4 interacted specifically with LPS from both subsp. tularensis and holarctica without cross-reactivity with subsp. novicida or other species of Gram-negative bacteria tested in the study  [tularensis and holarctica are responsible for the majority of human tularemia, using mAb 1A4 in an immunodiagnostic would allow for detection of most tularemia cases in a clinical setting [An novicida ,33. The capsule) , an immu setting . Neverth setting . While t setting .5 CFU/mL, which is about 5-times lower than the sensitivity of a LPS-targeting antigen capture ELISA developed previously [An antigen-capture immunoassay (sandwich ELISA) format was chosen for initial mAb characterization and selection of antibodies to potentially be employed in a POC diagnostic, such as a lateral flow immunoassay (LFI) . The fireviously .It is well established that the binding affinity of a mAb has a great impact on the sensitivity of an immunoassay . BindingIn this study, IgG1 and IgG2b subclasses of mAb 1A4 were isolated from the parental IgG3 cell line. Unfortunately, 1A4 IgG2a could not be isolated because the hybridoma clone was unstable. LODs of the assays developed using different combinations of 1A4 subclass variants were determined  bound to LPS stronger than IgG1 and IgG2b , consistent with the SPR results.To further investigate whether or not the increased assay sensitivity was a consequence of improved LPS-binding affinity of mAb 1A4, the affinities of each subclass variant of the mAb were assessed using SPR. In the first SPR assay configuration, the analysis was performed using the SA sensor chip coated with biotinylated LPS and with 1A4 subclass variants injected over the chip surface . The resof 84 nM , which ieviously ,37,38. Wthe IgG3 . Altogetthe IgG3 . As demoKD values of all three subclasses were comparable  mAb showed the poorest functional affinity among the four subclasses [Pseudomonas aeroginosa, poly-γ-D glutamic acid (γDPGA) capsule of Bacillus anthracis, and capsular polysaccharide (CPS) of B. pseudomallei. In those studies, murine IgG3 was shown to have the greatest binding affinity compared to the other subclasses [Notably, in this study we report the effect of IgG subclass on the functional affinity of mAb 1A4. We found that the IgG3 subclass of bclasses . Howeverbclasses ,23,25,27bclasses . In our bclasses . Thus, i6 Daltons [Another consideration here is the potential for LPS itself to aggregate into supramolecular structures, sometimes with irregular shapes and molecular masses exceeding 1 x 10 Daltons . This phAdditionally, the study indicates that it may be advisable to avoid IgG3 mAbs for the development of some immunoassays. However, murine IgG3 is the predominant subclass in response to thymus-independent (TI) antigens like LPS or CPS . The ideF. tularensis LPS. Like many other studies, we observed the dramatic reduction in functional affinity of F. tularensis LPS mAb 1A4 as a result of subclass switching. However, the decreased functional affinities of IgG1 and IgG2b did not impact deteriorate overall assay performance. On the contrary, we observed an improvement in assay sensitivity when mAb 1A4 IgG1 or IgG2b was used instead of the parental mAb 1A4 IgG3. The improved assay sensitivity was due to a decrease in assay background, a result of reduced self-association of the IgG1 and IgG2b compared to the IgG3.In conclusion, this study reports that IgG subclass selection contributes to the improved sensitivity of an immunoassay for S1 Table(DOCX)Click here for additional data file.S2 Table(DOCX)Click here for additional data file.S3 Table(DOCX)Click here for additional data file.S1 ARRIVE(PDF)Click here for additional data file.