Efficiency of MAS for breeding common bean is still hampered, however, due to the dominance, linkage phase, and loose linkage of previously developed markers. Here we applied I gene. This locus, in the distal arm of chromosome Pv02, contains seven putative NBS-LRR-type disease resistance genes. Two contrasting bulks, containing BCMV host differentials and ten BeanCAP lines with known disease reaction to BCMV, were subjected to in silico BSA for targeting the I gene and flanking sequences. Two distinct haplotypes, containing a cluster of six single nucleotide polymorphisms (SNP), were associated with resistance or susceptibility to BCMV. One-hundred and twenty-two lines, including 115 of the BeanCAP panel, were screened for BCMV resistance in the greenhouse, and all of the resistant or susceptible plants displayed distinct SNP haplotypes as those found in the two bulks. The resistant/susceptible haplotypes were validated in 98 recombinant inbred lines segregating for BCMV resistance. The closest SNP (~25-32 kb) to the distal NBS-LRR gene model for the I gene locus was targeted for conversion to codominant KASP (Kompetitive Allele Specific PCR) and CAPS (Cleaved Amplified Polymorphic Sequence) markers. Both marker systems accurately predicted the disease reaction to BCMV conferred by the I gene in all screened lines of this study.We physically mapped the genomic region underlying the in silico BSA approach using genetically diverse germplasm, genotyped with a high-density SNP chip array, to discover SNP variation at a specific targeted genomic region. In common bean, many disease resistance genes are mapped and their physical genomic position can now be determined, thus the application of this approach will facilitate further development of codominant and tightly linked markers for use in MAS.We demonstrated the utility of the The online version of this article (doi:10.1186/1471-2164-15-903) contains supplementary material, which is available to authorized users. Traditional plant breeding relies on the discovery, phenotypic selection, and introgression of desirable traits to develop superior cultivars . This process usually takes 7-10 years and significant economic resources , 3.Phaseolus vulgaris L.) for studies of genetic diversity, germplasm characterization, genetic mapping of major genes or quantitative trait loci (QTL) controlling agronomic traits or tolerance to abiotic stresses, and tagging and MAS of disease resistance genes  and susceptible (ii) genotypes. Systemic necrosis in genotypes with the I gene (I-) developed as early as five days post inoculation (dpi), whereas vein necrosis (II bc-12bc-12) and local lesions (II bc-ubc-ubc-22bc-22) developed within 10 dpi.Greenhouse screening for BCMV/BCMNV resistance with strain NL-3 of BCMNV accurately discriminated between resistant genotypes  for partial sequences of SW13 marker on chromosome Pv02 are approximately between 6 and 12 Mbp away from the I gene. The position of the ss715641188 SNP to the closest R-gene model (Phvul.002G323800) is ~25 kb, which suggests the chance for recombination between the SNP marker and the most proximal R-gene is very minimal.A BLAT search on the reference genome database revealed that the SW13 marker was distributed along several common bean chromosomes. The closest sequence hits  population of 98 lines, and the parental lines, were also included in this study for validation of marker-I gene cosegregation  according to the manufacturer’s protocol. For marker validation, DNA was extracted from pooled-trifoliate leaves of up to four plants per genotype before viral inoculation procedure.Five-hundred and six genotypes that compose the BeanCAP panel were previously genotyped with the Illumina BARCBEAN6K_3 Infinium SNP BeadChip, which includes allelic data for 5,398 SNP. Genotyping was performed according to manufacturer’s recommendations at the USDA Soybean Genomics and Improvement Lab, Beltsville, MD, and data generated for each genotype was used for allele calling using GenomeStudio Software v2011.1 .http://www.phytozome.net/commonbean) is known. The genomic region for the I gene was identified by querying the sequences of markers Phgp and Bng45  to the http://sgn.cornell.edu/tools/caps_designer/caps_input.pl). Several restriction endonucleases were found to recognize the SNP (A/G) and neighboring bases, and TaqI was chosen for the CAPS genotyping assay as it is commonly used and low-cost. TaqI recognizes the restriction site TCGA and cleaves the resistance allele but not the susceptible allele due to the alteration of the restriction site  by the SNP. A PCR primer pair (BCMV_48289723_CAPS) flanking the SNP was designed using Geneious R7  to produce a PCR amplicon of 311 bp. Primer specificity was checked against the common bean genome sequence database. PCR reaction mix consisted of 1X Go Taq Reaction buffer, 1.5 mM MgCl2, 0.2 mM of each dNTP’s, 0.2 μM each primer, 20 ng genomic DNA, and 1 U of Go Taq DNA polymerase, in a 25 μl final volume. All reagents were purchased from Promega . PCR was performed on an GeneAmp PCR System 9700 thermocycler  with an initial denaturation at 94°C for 5 min; followed by 35 cycles of 94°C for 30s, 58°C for 30 s, and 72°C for 30 s; final step of 72°C for 5 min. Initially, PCR products were separated and visualized on a 1% agarose gel to verify presence and correct size of PCR amplicon. A five-μl aliquot of PCR products were digested in a 20 μl reaction containing 1X FastDigest Green buffer, and 1 μl FastDigest TaqI enzyme . The reaction was incubated at 65°C for 15 min on the thermocycler, and digestion products were separated and visualized on a 2% agarose gel. The resistant allele is cleaved by TaqI generating two bands of 201 and 110 bp.For CAPS marker Table , the SNPI gene in the RIL population was calculated by χ2 analysis.After initial validation of KASP and CAPS markers, all 122 genotypes , RIL population (n = 92) and its parental lines were genotyped with the above markers. In addition, plants were also screened with the dominant SW13 SCAR marker, which generates a 690 bp PCR amplicon , using tI gene. (XLSX 536 KB)Additional file 1: Table S1: Graphical genotypes of lines screened in the greenhouse and all BeanCAP accessions determined by SNPs flanking the I gene. (XLSX 25 KB)Additional file 2: Table S2: Graphical genotypes of RILs, derived from the cross of G122 × Montcalm, determined by SNPs flanking the


Version 3 now extends functionalities in order to easily manage and exploit SNPs derived from next generation sequencing technologies, such as GBS (genotyping by sequencing), WGRS (whole gre-sequencing) and RNA-Seq technologies. Based on the standard VCF  format, the application offers an intuitive interface for filtering and comparing polymorphisms using user-defined sets of individuals and then establishing a reliable genotyping data matrix for further analyses. Namely, in addition to the various scaled-up analyses allowed by the application , SNiPlay3 proposes new modules for GWAS (genome-wide association studies), population stratification, distance tree analysis and visualization of SNP density. Additionally, we developed a suite of Galaxy wrappers for each step of the SNiPlay3 process, so that the complete pipeline can also be deployed on a Galaxy instance using the Galaxy ToolShed procedure and then be computed as a Galaxy workflow. SNiPlay is accessible at  Single nucleotide polymorphisms (SNPs) are genetic variants commonly used to identify candidate genes and genotype-phenotype association studies. With next generation sequencing (NGS), genome sequencing is becoming inexpensive and routine, and the discovery of large numbers of SNPs is facilitated. Indeed, with the availability of reference genome along with sequencing data derived from WGRS (whole-genome re-sequencing), GBS (genotyping by sequencing), RAD-Seq and RNA-Seq technologies, millions of variants including SNPs are easily released. To make exploration and large scale analyses of genomic variations simple and accessible, there is a need for applications based on efficient databases and convivial interfaces. Most of the existing tools are command-line  or dedicHere we report the version 3 of the SNiPlay application  that shoThe SNiPlay pipeline components have been updated to be able (i) to manage variants data derived from NGS technology and (ii) to process data at the whole-genome scale. One significant improvement is the ability to handle the standard VCF format  as input files. Indeed, with the recent emergence of powerful software packages dedicated to analysis of NGS data such as VCFtools  or SnpefAn overview of the process is presented Figure The different analyses are proposed to be computed either for a dataset directly uploaded by a user or for a genotyping dataset already available in the server as a VCF flat file associated to an annotated genome.All tools and workflows are incorporated into the Galaxy framework, one of the most popular analysis platforms for NGS data, that offers easy access to numerous bioinformatic applications and strongly supports reproducibility of analysis steps –12. In aSnpEff  was implAn option anticipates the situations where the input VCF contains variant positions on the genes instead of chromosomes. This can be the case for RNA-Seq experiments for which reads may have been mapped on coding sequences (CDS or full length mRNAs). This option activates a module that uses the GFF annotation file to locate variants on the chromosomes and recode the VCF file prior to launch SnpEff.The first one is based on VCFtools program  providinThe second method takes advantages of a new NoSQL-based application  dedicateBy establishing ‘on the fly’ a data matrix combining a subset of individuals and a subset of chromosomes or genes, variants can be filtered out using numerous criteria such as MAF (Minor Allele Frequency), type of variants (SNP or InDel) and missing data ratio. While former version of SNiPlay was built on MySQL database to store and query variants, the version 3 is now based on new technologies or approaches more efficient for the mining of millions of variants at the whole-genome level. For this purpose two methods are thus proposed:Whatever method is chosen, various genotyping file formats can be exported . In addition, the system also allows the export of FASTA sequences, by re-introducing alleles into flanking sequences extracted from reference genome.K to be tested, K being the number of subpopulations in the panel, the server provides the percentage of admixtures (Q matrix) for each K-value and plots these Q estimates into comprehensive ready-to-print images. In addition, it also plots the standard error of the cross-validation error estimates so that user can assess the best/correct value for K  method. Similarly to the principal-component analysis and in contrast to dendrogram inferring methods that generate hierarchical structures, this technique produces two-dimensional or three- dimensional plots in which the entries are spread according to their relatedness. The MDS analysis in SNiPlay makes use of the plink software  and outpIn addition, to illustrate the panel organization, a distance tree based on SNP data can also be generated from a whole allelic dataset, combining dnadist from the PHYLIP package  for distFor both representations (MDS plot or tree), subpopulation attributions derived from the model-based approach are projected and colored on the graphs. An accession is discretely assigned to a subpopulation if more than a user-defined proportion (default: 70%) of its composition came from this subpopulation.P-values along the chromosomes, as well as the QQ-plots commonly used for GWAS to show the importance of correcting for population structure and kinship information  in JBrowse , in ordeUsing VCFtools, this module provides and displays various statistics about the dataset such as annotation statistics, type of variations or heterosygosity and missing data per sample.SNP density can be investigated and compared between different species, subsets of individuals or categories of SNPs (intronic versus exonic for instance). In addition, density of SNPs is also calculated across several samples taken individually and displayed as a heatmap, reflecting thus the difference with the reference genome. This functionality can be of great interest for whole genome structure analysis.With the same philosophy, various diversity indices (nucleotide diversity (π), Tajima's D-test of neutral evolution , fixatioThe VCF format anticipates the coding of allele phasing information  in order to define haplotype blocks. Thus, if provided in the VCF, a dedicated module of SNiPlay takes advantage of the phasing information to reconstitute and output haplotype blocks of high SNP density regions for which there is a continuum of phasing at the reads level.http://toolshed.g2.bx.psu.edu/) or can be downloaded from the SourceForge code source repository (http://sourceforge.net/projects/sniplay/), and installed on a web server. More information on the different installation procedures can be found in the package. The web application is connected to an High Performance Computing (HPC) cluster that manages the jobs sent by the web server . The install procedure of the application includes a configuration file which has to be adjusted in order to indicate if jobs must be computed locally (by default) or in a remote cluster. Except for tree rendering and haplotype network, all graphical layouts are based on the Highcharts API (http://api.highcharts.com). User data results are conserved for one week on the web server so that users are able to come back and work on their data, by entering the URL including a session identifier. Afterward, user data are removed from the server.SNiPlay is written in Perl; the SNiPlay suite is available as a one-click installation from any local Galaxy instance via the Galaxy Tool Shed (SNiPlay is a flexible and user-friendly web application that rapidly explores and analyzes genetic variants including both SNPs and InDels data. What was possible with the previous release of SNiPlay for a few genes or regions can now be applied for massive genotyping data at the whole-genome level. Indeed, the version 3 allows users to easily manage and exploit SNPs derived from NGS technologies by integrating the standard VCF format as input. In addition to the various large scale analyses allowed by the application like genomic annotation of SNP, diversity analysis, haplotype reconstruction and network, linkage disequilibrium, SNiPlay3 proposes pipelines for GWAS, population stratification, distance tree analysis and visualization of SNP density. Most of the results are displayed using embedded interactive graphical viewers. The complete pipeline can also be deployed on a Galaxy instance using the Galaxy ToolShed.


Of these, 22,542 SNPs were structurally annotated in different coding and non-coding sequence components of genes. Genes with 3296 non-synonymous and 269 regulatory SNPs could functionally differentiate accessions based on their contrasting agronomic traits. A high experimental validation success rate (92%) and reproducibility (100%) along with strong sensitivity (93–96%) and specificity (99%) of GBS-based SNPs was observed. This infers the robustness of GBS as a high-throughput assay for rapid large-scale mining and genotyping of genome-wide SNPs in chickpea with sub-optimal use of resources. With 23,798 genome-wide SNPs, a relatively high intra-specific polymorphic potential (49.5%) and broader molecular diversity (13–89%)/functional allelic diversity (18–77%) was apparent among 93 chickpea accessions, suggesting their tremendous applicability in rapid selection of desirable diverse accessions/inter-specific hybrids in chickpea crossbred varietal improvement program. The genome-wide SNPs revealed complex admixed domestication pattern, extensive LD estimates (0.54–0.68) and extended LD decay (400–500 kb) in a structured population inclusive of 93 accessions. These findings reflect the utility of our identified SNPs for subsequent genome-wide association study (GWAS) and selective sweep-based domestication trait dissection analysis to identify potential genomic loci (gene-associated targets) specifically regulating important complex quantitative agronomic traits in chickpea. The numerous informative genome-wide SNPs, natural allelic diversity-led domestication pattern, and LD-based information generated in our study have got multidimensional applicability with respect to chickpea genomics-assisted breeding.The genome-wide discovery and high-throughput genotyping of SNPs in chickpea natural germplasm lines is indispensable to extrapolate their natural allelic diversity, domestication, and linkage disequilibrium (LD) patterns leading to the genetic enhancement of this vital legume crop. We discovered 44,844 high-quality SNPs by sequencing of 93 diverse cultivated  Cicer arietinum L.) is a self-pollinated and diploid (2n = 2x = 16) crop species belonging to Fabaceae  near about 10,000 years ago  and desi  representing diverse gene pools have been successfully sequenced , transcripts, genes and genome sequences of diverse desi, kabuli, and wild accessions using traditional Sanger and high-throughput next-generation sequencing (NGS) approaches  are highly preferred in plant genetic and genome analyses because of their excellent genetic attributes, such as wide genomic distribution, co-dominant inheritance, high reproducibility, and chromosome-specific location Brookes, . For a lWith advent of barcoded multiplexing and restriction enzyme (RE)-based NGS approaches, the simultaneous integration of large-scale discovery, validation and genotyping of SNPs is now possible in numerous diverse crop accessions. These NGS-based methods have evolved plenty of high-throughput and cost-effective SNP genotyping assays and applications for many small diploid and larger polyploid crop genomes. Some of these assays include reduced representation libraries (RRLs)  and also complexity reduction (targeting lower copy number of genome) ability, especially in larger complex plant genomes -based improved NGS method that has gained considerable attention of crop researchers for large-scale discovery and genotyping of SNPs simultaneously in diverse crop germplasm accessions and mapping populations at a genome-wide scale  and wild chickpea accessions at a genome-wide scale. The genotyping information of validated SNPs was further utilized to understand their functional significance and determine the molecular diversity pattern, population genetic structure and LD patterns among chickpea accessions.Keeping that in view, the present study has utilized GBS approach for the first time to discover, validate and genotype SNPs in 93 diverse [with significant seed yield (g) per plant variability] cultivated (C. arietinum desi (39 accessions) and kabuli (53), and one C. reticulatum wild accession (ICC 17160) were utilized /plant] and genotypic diversity  were selected from available chickpea germplasm collections   following the manufacturer's instructions.Ninty-three accessions belonging to 92 cultivated ApeKI and ligated to adapters containing one of 96 unique barcodes to construct 96-plex GBS libraries. These libraries were pooled together  as biological replicates (Supplementary Table http://creskolab.uoregon.edu/stacks) was utilized to examine the quality distribution of each sequence read. The reads with an average quality below 90% confidence  was digested with desi and kabuli genome were processed using reference-based GBS pipeline of STACKS to identify accurate SNPs in 96 accessions. The SNP identification process in pstacks is efficient enough for handling gapped alignments properly to accommodate InDels and soft-masked alignment fragments. This prevents the SNP model from wrongly calling polymorphisms due to InDel frameshifts. Stacks algorithm also filters paralog and high-copy loci by read coverage, assuming random coverage across loci. We removed the putative loci with more than twice the standard diversion of coverage depth to filter out repetitive elements and stacks of paralogs loci. A maximum likelihood statistical model was used in STACKS to identify valid and high-quality SNPs , supported by at least 10 sequence reads. A catalog was built to record all SNP loci identified in 96 accessions by matching each accession to that catalog and detecting accurate alleles (minimum sequence read depth: 10) that are present at every SNP locus in each accession.The sequence alignment map (SAM) files generated from each de novo genotyping approach of STACKS, the sampled SNP loci from each accession were reassembled from the sequenced reads  for creating stacks. A de novo method was employed to group all exactly matching sequences into stacks, forming putative loci from high-confidence unique stacks. To detect polymorphism and infer SNP alleles, each putative locus of one-nucleotide position at a time was examined using a maximum likelihood framework  were used to deduce accurate genomic distribution, including the structural and functional annotation of SNPs (synonymous and non-synonymous SNPs). To determine the genomic distribution of identified SNPs across chickpea chromosomes, the SNPs with synonymous and non-synonymous substitutions were plotted individually based on their unique physical positions (bp) on eight chromosomes (pseudomolecules) of desi and kabuli genomes and visualized using Circos visualization tool  of SNPs identified by reference-based GBS approaches were correlated with the GFF file containing the genome annotation of desi and kabuli genome annotations . The SNP-carrying genes were BLAST searched  against the KOGnitor NCBI database (ftp://ftp.ncbi.nih.gov/pub/COG/KOG) for detailed functional classification. Gene Ontology (GO) enrichment analysis of SNPs-containing genes was performed using BiNGO plugin of Cytoscape V2.6 , the Benjamini and Hochberg false discovery rate correction was employed.The putative functions, including conserved domains in the proteins encoded by SNP-carrying genes were predicted based on available desi and kabuli genomes were compared among accessions and analyzed following the methods of Li et al. .For estimation of Ka (number of non-synonymous substitutions per non-synonymous site)/Ks (number of synonymous substitutions per synonymous site) substitution ratio, the SNP-carrying coding sequences of genes annotated from the reference i et al.  and Devii et al. . To furtin silico SNP database of same accessions according to their nature/types and bp on the desi (ICC 4958) genome  on desi and kabuli genomes, were included in our validation study. For these analyses, the primers designed from 300-bp sequences flanking either side of 214 selected SNPs were PCR amplified using the genomic DNA of 93 chickpea accessions. The amplified PCR products were purified and sequenced. The aligned high-quality sequences were used to detect SNPs among accessions following Kujur et al. (http://www.sequenom.com) following Saxena et al.  was compared/correlated with an available r et al.  and Saxer et al. ,b. Addita et al. ,b. The Shttp://www.maizegenetics.net) to estimate the PIC (polymorphism information content) and for constructing an unrooted neighbor-joining (NJ)-based phylogenetic tree , including LD decay  in each desi and kabuli genomes, the genotyping data of SNPs that were physically mapped on eight chromosomes were analyzed using a command (–r2 −ld-window 99999 −ld-window-r2 0) line interface of PLINK  and following Saxena et al.  with accession number SRX845396 (Release date: 31st March 2015). Using both reference and de novo-based GBS approaches in 96 accessions, 44,844, including 20,439 and 24,405 high-quality SNPs  were identified from desi and kabuli genomes, respectively  used as biological replicates was observed. The transitions were more frequent than the transversions that composed slightly more than a-half  of the identified SNPs  and kabuli (29.7%) chickpea compared to A/C (11.8% in desi) and G/T (12.1% in kabuli) transversions   and minimum (29.8 kb) average marker density, respectively , with SNP submission (SS) accession numbers 974751673–974768048, which will be publicly available in the next NCBI db SNP Build (B142), year 2015 (http://www.ncbi.nlm.nih.gov/SNP/snp_viewTable.cgi?handle=NIPGR).A total of 24,405 SNPs discovered from the y Figure . The refkb Table . A genom Figures , 2B. Thedesi reference genome-based SNPs showed the presence of 9567 SNPs in 4010 genes and 4026 SNPs in the intergenic regions  specifically from desi genome  and ICC 17160 (C. reticulatum) and minimum (0.47) between ICC 7346 (kabuli) and ICC 15512 (kabuli). The Tajima's D estimate further inferred that a higher proportion (95%) of SNP-carrying genes under purifying selection with reduced D = −2.48 (D < −2) and remaining 5% genes had elevated D > 2.The structural annotation of 13,593 −31), followed by biological process  and cellular component   gene was found to be polymorphic between drought tolerant (ICC 4958 and ICCV 93954) and sensitive (ICCV 93954) chickpea accessions  and susceptible (ICC 4951) chickpea accessions  and susceptible (ICC 4951) chickpea accessions. In addition, this non-synonymous SNP revealed polymorphism between salinity tolerant (ICC 4951) and sensitive (ICC 12968) chickpea accessions  compared to that estimated in other genic SNPs (Ka/Ks: 0.84 and Tajima's D: −2.48).The functional annotation of 5588 genes with SNPs revealed their maximum correspondence to growth, development and metabolism-related proteins , followed by TFs  and signal transduction proteins , with a minimal correspondence to genes encoding hypothetical proteins   controlling drought tolerance-component root traits in chickpea (Supplementary Tables Ascochyta blight (ar2aQTL) and Fusarium wilt (FW_Q_APR_6_1) resistance QTLs mapped on kabuli chromosomes 2 (2.39 Mb: 31.58–33.97 Mb) and 6 (0.1 Mb: 29.47–29.57 Mb), respectively were discovered  reference genome-based SNPs (with MAF ≥ 0.05) discovered in 93 accessions were compared and correlated with the available resequencing-based SNP database  of four chickpea accessions . On the basis of congruent physical positions (bp), the correspondence of 8.8% (777 SNPs) of the SNPs present in our GBS data (8776 SNPs) was observed with that of SNPs mined by genome resequencing of the four accessions (Supplementary Table desi) and ICC 17160 (wild) and minimum between ICC 4958 (desi) and ICC 4951 (kabuli)  were found to be common between past genome sequencing and our present GBS data (6244 SNPs)  and MALDI-TOF mass array SNP genotyping (240 SNPs) of 454 selected non-synonymous and regulatory SNP loci discovered by reference genome- and de novo-based GBS approaches  showing polymorphism among 93 diverse desi, kabuli, and wild chickpea accessions had higher PIC , MAF (mean: 0.37), and nucleotide diversity (mean θπ: 1.30 and mean θω: 1.51)  of SNPs revealed polymorphism between cultivated C. arietinum and wild C. reticulatum species. The SNPs detected a higher intra-specific polymorphic potential within 39 desi accessions  compared with that of 53 kabuli accessions   was almost comparable to that of kabuli chromosomes  (Table desi (mean θπ: 1.33 and θω: 1.69) and kabuli chromosomes 4 (1.31 and 1.72).In total, 23,798 SNPs  revealed a wider range of genetic distances, from 0.13 (ICC 7346 and ICC 15512) to 0.89 (ICC 15264 and ICC 17160), with an average of 0.56 (Supplementary Table desi and kabuli accessions within the cultivated (C. arietinum) species varied from 0.12 to 0.76, with a mean of 0.45. A relatively less genetic diversity in 53 cultivated kabuli accessions (mean genetic distance: 0.38) as compared to the 39 desi accessions (0.43) was evident (Supplementary Table kabuli and desi) in accordance with their pedigree relationships and parentage , POP II , and POP III (40 accessions)  (log-likelihood) value (showing the greatest apparent inflection)  was observed between POP I and POP III (0.62), followed between POP II and POP III (0.43) and minimum between POP I and POP II (0.30). The FST-based population differentiation was highest within POP II (0.21), followed within POP III (0.16), whereas it was least within POP I (0.10). All 93 accessions clearly belonged to a structured population with 78% inferred ancestry derived from one of the model-based population and the remaining 22% had admixed ancestry. Admixed ancestry was maximum in POP II (16%), followed by POP III (11%) and minimum in POP I (5%). Highest admixed ancestry was obtained between POP III and POP II (19%) compared to that between POP III and POP I (13%). The admixture was not detected in 9.7% (nine accessions) of chickpea accessions, whereas 37.6% (35) and 47.3% (44) of accessions showed up to 20 and 10% admixed ancestry, respectively.The population genetic structure determination among 93 accessions with genotyping data of genome-wide  96 SSR  and extent of LD decay using all possible pair-combination of genome-wide 3321 and 8592 desi and kabuli SNPs  were calculated among 93 chickpea accessions constituting three populations  and a diversity panel. In the whole population, 64.3% global marker-pairs exhibited significant LD (P < 0.0001), indicating the existence of an extreme LD level in a panel of 93 accessions (Table P < 0.0001) was observed for 65 and 54.8% of the linked and unlinked marker-pairs, respectively (Table desi chromosome 3 (67.3%) and kabuli chromosome 4 (58.4%)  and kabuli  chromosomes are comparable to each other, whereas these parameters for unlinked markers were higher in the chromosomes of desi  than kabuli   was observed in any population up to a 1000 kb physical distance for the desi and kabuli chromosomes  showed a shorter LD decay (~400 kb in desi chromosomes and ~600 kb in kabuli chromosomes) than that of the less diverse populations of POP I (~700 kb in both desi and kabuli chromosomes) and POP II (~750 kb)  and integrating both reference genome- and de novo-based GBS pipelines for genome-wide discovery and genotyping of SNPs in chickpea. By implementing these novel approaches, many  non-erroneous and high-quality SNPs with wider genome coverage were identified in chickpea. In contrast to the conventional TASSEL GBS pipeline (maximum SNP detection limited to 64-bp sequence reads), the use of integrated reference genome (desi and kabuli)–and de novo-based GBS approaches would be of heightened interest for SNP discovery and genotyping. Consequently, our study for the first time identified SNPs in high-quality longer sequence reads  by involving a broader range of filtration tools , thereby enriching the chickpea genome with reduced computational time. The advantages of this integrated GBS approach for discovery and genotyping of SNPs at a genome-wide scale is well-documented in many crop plants, including soybean and rice  through amplicon resequencing and MALDI-TOF mass array SNP genotyping assay based on strong sensitivity (93–96%) and specificity (99%) were also apparent. These findings reflect the major strength and reliability of GBS assay in rapid discovery and high-throughput genotyping of accurate SNPs in chickpea genome with sub-optimal use of resources. Henceforth, this highly versatile integrated GBS approach developed in our study could find multidimensional applicability for genome-wide discovery and genotyping of SNPs in small as well as large genome plant species. The identification of a relatively higher frequency of SNPs showing transition substitutions (57%) than transversions is consistent with previous genome-wide SNP discovery studies in crop plants, including chickpea  compared with kabuli chromosomes  could be attributed to the greater (~2.8 times) differences in pseudomolecule assemblies/lengths between desi (124.37 Mb) and kabuli (347.24 Mb) genomes  resulting in sequencing of different regions of genomes between desi and kabuli chickpea  identified from desi and kabuli genomes could prove useful for various marker-based applications of chickpea genetics, genomics, and breeding.The physical mapping of a lesser number of SNPs on eight desi (9567 SNPs in 4010 genes) and kabuli  genomes. Furthermore, the structural and functional annotation of 12,773 SNPs (submitted to NCBI dbSNP) detected in 5588 non-redundant chickpea genes and 3296 non-synonymous and 269 regulatory SNPs in 2225 genes provided a faster mode of selection of functional gene-based SNPs to establish marker-trait linkages and identify a number of genes/QTLs governing qualitative and quantitative agronomic traits in chickpea. The estimated mean ratio of non-synonymous to synonymous SNPs (Ka/Ks: 0.84) in chickpea genes is comparable with that obtained (Ka/Ks < 1) from orthologous chickpea genes/transcripts . The identification of non-synonymous and regulatory SNPs in protein-coding genes,  showing polymorphism between abiotic /biotic (Fusarium wilt) stress tolerant/resistant and sensitive/susceptible accessions, have documented the evolutionary/adaptive advantages of these non-synonymous SNP loci  sequence components of protein-coding genes individually predicted from desi (mean θπ: 1.20 and θw: 1.33) and 53 kabuli (θπ: 0.96 and θw: 1.07) accessions was comparable to that obtained using genome-wide SNP markers –and inter-specific polymorphic potential and wider genetic diversity /functional molecular diversity  among 93 accessions were identified. They could assist us in selecting desirable diverse accessions/inter-specific hybrids in cross-breeding program (introgression breeding) for chickpea varietal improvement.The intra (49.5% and PIC: 0.38)- and inter (50.5% and 0.40)-specific polymorphic potential detected by 23,798 SNPs among 93 cultivated and wild chickpea accessions was much higher than that estimated previously with SNP markers  was consistent with their expected pedigree relationships and parentage. A significant deviation from accurate population assignment based on pedigree relations was more pronounced in cultivated desi and kabuli accessions belonging to POP II. The geographical origin and adaptive environment, rather than parentage, of these accessions had a greater effect on their assignment to a specific population group. Higher (22%) admixed ancestry among accessions reflected their multiple domestication at archeological sites of south eastern Turkey ~10,000 years ago, followed by a complex breeding history involving inter-crossing/introgression coupled with strong adaptive selection pressure. The inclusion of diverse non-recurrent desi and kabuli germplasm lines as common parental lines in crop improvement breeding program to develop/breed most of the selected chickpea accessions in our study for valuable agronomic traits and higher yield might have influenced their population group assignment and domestication pattern, resulting in numerous admixtures among these accessions  in three populations of 93 accessions and furthermore, across eight desi (0.56) and kabuli (0.52) chromosomes using linked SNP-pairs compared with unlinked SNPs, reflected the direct correlation of LD patterning with the physical linkage of SNPs on chromosomes and the density required to cover genomic regions. The extensive LD estimates and extended chromosomal LD decay (~1000 kb) calculated in three populations of self-pollinated chickpea, were much higher than the cross-pollinated  regulating important agronomic traits in chickpea. Moreover, our studies regarding the molecular diversity, population genetic structure and LD mapping among chickpea accessions (three population groups) using genome-wide SNPs could essentially identify trait-influencing target genomic regions (candidate genes) across chromosomes/genomes that possibly got influenced by strong selection pressure during domestication.A greater LD estimate (mean de novo-based GBS assays. The structurally and functionally annotated SNPs in diverse coding and non-coding sequence components of genes could have potential to be utilized for various large-scale marker-based genotyping applications in chickpea (Supplementary Figure We identified 44,844 non-erroneous SNPs in chickpea at a genome-wide scale using reference genome- and 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.


Ribes nigrum and Rubus cultivated varieties at different harvest stages. In blackcurrant bud-extracts the most important class was organic acids (50.98%) followed by monoterpenes (14.05%), while in blackberry preparations the main bioactive classes were catechins (50.06%) and organic acids (27.34%). Chemical, pharmaceutical and agronomic-environmental knowledge could be important for obtaining label certifications for the valorization of specific genotypes, with high clinical and pharmaceutical value: this study allowed to develop an effective tool for the natural preparation quality control and bioactivity evaluation through the chemical fingerprinting of bud preparations.It is well known that plants are important sources for the preparation of natural remedies as they contain many biologically active compounds. In particular, polyphenols, terpenic compounds, organic acids, and vitamins are the most widely occurring groups of phytochemicals. Some endemic species may be used for the production of herbal preparations containing phytochemicals with significant bioactivity, as antioxidant activity and anti-inflammatory capacities, and health benefits. Blackberry sprouts and blackcurrant buds are known to contain appreciable levels of bioactive compounds, including flavonols, phenolic acids, monoterpenes, vitamin C, and catechins, with several clinical effects. The aim of this research was to perform an analytical study of blackcurrant and blackberry bud-preparations, in order to identify and quantify the main biomarkers, obtaining a specific phytochemical fingerprint to evaluate the single botanical class contribution to total phytocomplex and relative bioactivity, using a High Performance Liquid Chromatograph−Diode Array Detector; the same analyses were performed both on the University laboratory and commercial preparations. Different chromatographic methods were used to determine concentrations of biomolecules in the preparations, allowing for quantification of statistically significant differences in their bioactive compound content both in the case of  Rubus cultivated varieties) sprouts and blackcurrant (Ribes nigrum L.) buds are known to contain appreciable levels of vitamins, terpenic and phenolic compounds, including flavonols, phenolic acids and catechins [Plants are important sources for the preparation of natural remedies, food additives, and other ingredients, as they contain many biologically active compounds as polyphenols, vitamins , terpenes, organic acids, and other very important phytochemicals ,2. For tatechins ,5. The matechins ,7. On thatechins ,9.Phytotherapy is the study of natural extracts used as health-promoting products for medical care . The ideGemmotherapy is the most recent of therapeutic techniques developed on the basis of the plant medical properties. It uses the properties of extracts obtained by the maceration in ethanol and glycerol of fresh meristematic plant tissues, mainly buds and sprouts, for medicinal purposes. The products are commercially known as bud-preparations. In herbal preparations, due to the large quantity of bioactive compounds, many of which act synergistically, there is a preference to attribute the pharmacological effect to the “phytocomplex” , rather than to any single active compound, as in the case of standard medicine .In recent years phytotherapy has become a fully fledged medical discipline, as the knowledge gleaned from folk medicine has since been subjected to methodical scientific assessments in order to provide evidence of its efficacy . HoweverThe lack of information on the intrinsic and extrinsic factors that determine the quality and effectiveness of bud-preparations indicates the need to extend research on this topic, however, due to the variability and complexity of bud-preparations, it is very difficult to control their product quality . The keyGiven the above, the aim of this research was to perform an analytical study of blackcurrant and blackberry bud-preparations using simple, sensitive and reliable HPLC–diode array detector (DAD) methods in order to identify and quantify the main phytochemicals (biomarkers) and to be able to obtain a specific botanical fingerprint for the assessment of the single bioactive class contribution to total bud preparation phytochemical profile The influence of genotype and harvest stage on these bioactive substances in the bud-extracts was also analysed. The same analyses were performed both on laboratory preparations and on commercial preparations.Recently, many screening studies of different plant materials have been performed in order to find naturally occurring antioxidant compounds for use in food or medicinal preparations as replacements for potentially harmful synthetic additives . PhenoliThe chemical composition of secondary plant metabolites is highly dependent on several factors such as climatic conditions, harvesting time, and plant genotype ,36,37. TFW, bud break) than Rozenthal cultivar .Blackcurrant bud-preparations have been identified as herbal products with a high health-value . The secFW, bud break) and wild variety  presented a greater total bioactive compound content (TBCC) than Black Pearl cultivar . As reported in similar studies [vs. RRC1 and RC2 vs. RRC2), but there were no differences between companies (RC1 vs. RC2 and RRC1 vs. RRC2)  , confirmR. nigrum bud-preparation phytocomplex  showed the prevalence of organic acids (50.98%) and polyphenols (29.39%), followed by monoterpenes (14.04%) and vitamins (5.98%). In Rubus cultivated varieties bud-extracts phytocomplex  or HPLC coupled to mass spectrometry is necessary.Ribes nigrum L. (buds) and Rubus cultivated varieties (sprouts) were collected in 2014, in three different phenological stages , from two germplasm repositories in Turin Province , Grugliasco (Rubus cultivated varieties) and San Secondo di Pinerolo (R. nigrum). Different genotypes were sampled, in order to test the genotype effect on the final product chemical composition . Buds and sprouts were used fresh to prepare herbal preparations; HPLC samples were analyzed after being stored for a few days at normal atmosphere (N.A.), at 4 °C and 95% relative humidity (R.H.). Commercial products from two different Italian herbal companies were also analyzed: the companies are located in San Gregorio di Catania , and Predappio . University lab preparations and commercial preparations were evaluated. Samples of Ethanol, hydrochloric acid, formic acid and all the organic acid standards were purchased from Fluka Biochemika . Analytic HPLC grade acetonitrile, methanol, glycerol, all the polyphenolic and terpenic standards, potassium dihydrogen phosphate, 1,2-phenylenediamine dihydrochloride (OPDA) and phosphoric acid were purchased from Sigma Aldrich . Milli-Q ultrapure water was produced by using a Sartorius Stedium Biotech mod. Arium apparatus . Cetyltrimethylammonium bromide (cetrimide), ascorbic and dehydroascorbic acids were purchased from Extrasynthése .The extraction solution was prepared based on the protocol of bud-preparations detailed in the monograph “Homeopathic preparations”, quoted in the French Pharmacopoeia, 8th edition, 1965 . The budAn Agilent 1200 High Performance Liquid Chromatograph, equipped with a G1311A quaternary pump, a manual injection valve, and a 20 μL sample loop, coupled to an Agilent GI315D UV-Vis diode array detector , was used for the analysis. Five different chromatographic methods were used to analyze the samples, two for polyphenols and one for monoterpenes, organic acids, and vitamins, respectively. In all of the used methods, bioactive compound separation was achieved on a KINETEX–C18 column . Different mobile phases were used for a specific bioactive compound identification and UV spectra were recorded at 330 nm (A); 280 nm (B); 210, 220, 235, and 250 (C); 214 nm (D); 261, and 348 nm (E). The chromatographic conditions of each method were reported in All the single compounds were identified in samples by comparison and combination of their retention times and UV spectra with those of authentic standards in the same chromatographic conditions. The external standard method was used for quantitative determinations. Twenty μL aliquots of each standard solution were used for HPLC analysis and injections were performed in triplicate for each concentration level. For reference compounds, the limit of detection (LOD) and the limit of quantification (LOQ) were experimentally determined by HPLC analysis of serial dilutions of a standard solution to reach a signal-to-noise (S/N) ratio of 3 and 10, respectively. The main analytical method validation data are summarized in −1) to reach 100% of the test concentration.All samples were analyzed in triplicate, and standard deviations are given in order to assess the repeatability of the used methods. Accuracy was checked using the recovery test by spiking samples with a solution containing each bioactive compound . Results were subjected to analysis of variance (ANOVA) test for mean comparison  and HSD Tukey multiple range test  and terpenic compounds and indicated that secondary plant metabolite concentration in bud preparations is highly dependent on harvesting time and plant genotype. For this reason, the concentrations of main bioactive compounds in buds, and consequently in bud-preparations, can be opportunely defined on the basis of chemical-pharmaceutical, agricultural and environmental knowledge. The differences in the phytocomplex chemical composition of blackcurrant and blackberry justify the different medical uses of these preparations; in blackcurrant bud-extracts the most important class was organic acids (50.98%) followed by monoterpenes (14.05%), while in blackberry preparations the main bioactive classes were catechins (50.06%) and organic acids (27.34%).In this study, The HPLC methods used in this study are simple, sensitive and reliable, and could be used for the quality evaluation and control of bud-extracts and natural medicines. The results of this research show that the assessment of chemical composition of the plant-derived products could help in find out new sources of natural antioxidants and other health-promoting compounds which could be used as natural medicines, food additives, functional foods and botanical ingredients in order to develop a new generation of standardized and effect-optimized preparations with high values of quality and safety. Chemical, genetic and environmental knowledge could be a useful tool for obtaining label certifications for the valorization of specific genotypes, with high clinical and pharmaceutical value: chromatographic fingerprinting could be an effective tool for herbal product characterization and authentication, natural preparation quality control (against contamination and adulteration), bioactivity evaluation of bud preparations, and standardization of all the supply chain steps.


Triticum aestivum L.). Four enzymes are involved in fructan synthesis in higher plants, and 6-SFT is a key enzyme in fructan biosynthesis. In this study, thirteen single nucleotide polymorphisms were detected in 6-SFT-A2 in 24 wheat accessions, forming three haplotypes. Two cleaved amplified polymorphic sequence markers developed based on polymorphisms at sites 1870(A-G) and 1951(A-G) distinguished the three haplotypes. 6-SFT-A2 was located on chromosome 4A, between markers P2454.3 and P3465.1 in a doubled haploid (DH) population derived from the cross Hanxuan 10 × Lumai 14. The DH population comprising 150 lines and a historical population consisting of 154 accessions were used in a 6-SFT-A2 marker–trait association analysis. The three haplotypes were significantly associated with thousand-grain weight (TGW) under rainfed conditions. HapIII had a significant positive effect on TGW. There were significant differences between the Hanxuan 10 and Lumai 14 genotypes in both rainfed and irrigated environments. The average TGW of Lumai 14 (HapIII) was higher than that of Hanxuan 10 (HapI). The frequencies of 6-SFT-A2HapIII in cultivars released at different periods showed that it had been strongly positively selected in breeding programs. The preferred HapIII for TGW occurred at higher frequencies in Gansu, Beijing, Shanxi, and Hebei than other regions in northern China.As a class of water-soluble, fructose-based oligo- and polysaccharides, fructans are major nonstructural carbohydrates and an important carbon source for grain filling in wheat (The online version of this article (doi:10.1007/s11032-015-0266-9) contains supplementary material, which is available to authorized users. Triticum aestivum L.) is one of the most important staple food crops globally. With an increasing world population, it is estimated that the global demand for wheat will increase by a further 40 % before 2020 , sucrose–sucrose 1-fructosyltransferase (1-SST), fructan–fructan 1-fructosyltransferase (1-FFT), and fructan–fructan 6G-fructosyltransferase (6G-FFT), are involved in fructan synthesis in higher plants  by Gao et al.  develop a functional markers for 6-SFT-A2 and map it on a chromosome; (3) reveal relationship between the marker and important agronomic traits by association analysis using a doubled haploid population and a historical population; and (4) identify superior 6-SFT-A2 haplotypes for marker-assisted selection in wheat breeding program.The genomic sequence of o et al.  from TriT. urartu (AA) accession UR 203, Ae. speltoides  Y2041 and Ae. tauschii (DD) Y215, and T. durum (AABB) DS 6 were used for identifying genomic origins. A set of nulli-tetrasomic lines developed in Chinese Spring was used for chromosomal location of the target gene.Three diploid progenitor species of common wheat, The 150 doubled haploid lines (DHLs) derived from cross Hanxuan 10 × Lumai 14 were used for genetic mapping and gene–trait association analyses. Both parents Hanxuan 10 and Lumai 14 are Chinese wheat cultivars  released during different decades was used for investigating the distribution of target gene haplotypes, and 154 of them were used for functional validation of the 3 ha−1 at four growth stages: pre-overwintering, jointing, flowering, and grain filling, if there was not sufficient rainfall in each corresponding period. All accessions were planted in 2- or 4-m four-row plots with 30 cm spacings. Field management followed local practices. One thousand-grain weights (TGWs) were recorded after harvest.The DHLs were planted in the wheat growing seasons of 2001, 2005, 2006, 2009, 2010, and 2011, and the historical population was planted in 2008 and 2009 at Changping, Beijing , the experiment station of the Institute of Crop Science, Chinese Academy of Agricultural Sciences. The experimental field was managed under drought stress (DS) and well-watered (WW) conditions. DS treatment was represented by rainfed conditions. The rainfalls from the beginning of October (at seeding) to mid-June (harvest) were 181, 100, 124, 155, 188, 131, and 180 mm for each growing season in the order of years. The WW treatment was irrigated with 750 mGenomic DNA was extracted from young leaves of 10-day-old seedlings using the phenol/chloroform method  was designed. Primers were designed by the software Primer Premier, version 5.0 , and all primers were synthesized by Beijing Augct Biological Technology Co., Ltd (http://www.augct.com).Based on the known sequence . The PCR procedure was initial denaturation at 95 °C for 5 min, followed by 35 cycles of 95 °C for 1 min, annealing (55 °C) for 45 s, and extension at 72 °C for 3 min; with a final extension of 72 °C for 10 min. The PCR products were separated by electrophoresis in agarose gels, and the target bands were recovered and cloned into the pEASY-Blunt simple vector (TransGen Biotech Co. Ltd). Twelve positive clones of each accession were sequenced.PCR were performed in total volumes of 15 μL, including 3 pmol of each primer, 100 μmol of each dNTP, 30 ng genomic DNA, and 1.5 unit 6-SFT-A2, both M13 and two pairs of overlapping primers  were designed for sequence walking. Thus, each clone was divided into six overlapping contigs, which were assembled and extended to one sequence with the SeqMan program, and the 12 clone sequences were aligned and then re-assembled to one or more copies for each accession with the SeqMan and MegAlign program.DNA sequencing was performed on a 3730XL DNA Analyzer (ABI) with the following program: initial denaturation at 96 °C for 1 min, 30 cycles of 96 °C for 10 s, 50 °C for 5 s and 60 °C for 4 min. To screen the whole sequence of SNPs were identified using the DNAStar software.The genotypes of 150 DHLs were determined by PCR–RFLP, and the software Map Manage QTLb20 was used for the gene mapping.P value for individual polymorphisms was estimated based on 1,000 permutations of the dataset, both for GLM and logistic regression. Polymorphisms with P < 0.05 were considered significantly associated with the traits.Population structure of the historical population was analyzed using Structure 2.3.2 software based on the data of 83 SSR markers  was designed for chromosome location. The A, B(S), and D genomic donor species of common wheat, and the nulli-tetrasomic lines of Chinese Spring were used to gain insights on the origin and evolution of 6-SFT. PCR results showed that 6-SFT-A2 was located on chromosomes 4A .As a young polyploid species, hexaploid wheat has a complex genome. Based on sequence differences in 6-SFT-A2 sequences were identified in a small diversity panel consisting of 24 wheat accessions selected from the historical population by molecular markers. The full length of 6-SFT-A2 is 3,149 bp, including four exons and three introns. Primer F2/R2 covers a 2,663 bp region of 6-SFT-A2, including intron 2 (596 bp), exon 3 (858 bp), intron 3 (486 bp), exon 4 (703 bp), and 3′-UTR (20 bp). Eleven SNPs were identified in the whole region of 6-SFT-A2 with an average of 1 SNP/242 bp (Supplementary Table S2). Two InDel were present. The frequency of SNP in the noncoding region (1 SNP/276 bp) was slightly lower than that in the coding region (1 SNP/223 bp). Among the detected SNPs, eight were transitions and three were transversions. The transition/transversion ratio was 2.7(Supplementary Table S3).6-SFT-A2 alignment spanning the entire 2,663 bp included 13 sites with alignment gaps (Supplementary Table S3). Four and three SNPs were detected in exons 3 and 4, respectively. Four SNPs were identified in intron 2 and intron 3, and two InDel were in intron 3. Three 6-SFT-A2 haplotypes, designated HapI, HapII, and HapIII, were identified based on the 13 SNP/InDel sites .The 6-SFT-A2 produced two restriction enzyme sites. One was a MboII recognition site at SNP-1870-G, but not at SNP-1870-A; the other was a BsgI recognition site at SNP-2951-G, but not at SNP-2951-A  markers were developed based on the SNPs at 1,870 bp and 2,951 bp to distinguish the three haplotypes  at 1,870 bp of HapI) and the Lumai 14 genotype (HapIII) in both water regimes except for irrigated conditions in 2001 and 2011  and P3465.1 (29.1 cM) . Using the same DH population, QTL for TGW were also identified in the same or adjacent interval flanking by markers P2454.3 and P3465.1 on chromosome 4A . The released time of the population accessions was more than six decades (Supplementary Table S1). In landraces and cultivars released before 1960 (pre-1960), the frequency of HapIII was 23.1 %. Over subsequent periods, the frequency of HapIII gradually increased to 64.1 % in cultivars released during the 2000s , 55.8 % (Beijing), 50.0 % (Shanxi), 47.1 % (Hebei), 46.2 % (Shandong), 45.0 % (Henan), 40.0 % (Jiangsu), and 33.3 % (Shaanxi). With increasing latitude, HapII frequencies displayed a decline trend, while HapI frequencies showed a slight increase.The 272 accessions were collected from eight provinces in northern and central China. The geographic distribution of 6-SFT-A2 sequence three haplotypes, HapI, HapII, and HapIII, were identified among wheat accessions. Association analysis revealed that 6-SFT-A2 was associated with TGW both in a historical population and in a DH population. HapIII possessed a significantly positive effect on TGW and, therefore, should be a beneficial allele for improving grain yield. Xue et al.  transfers fructosyl residues to fructan in (2–6) linkages in wheat; these are by far the prevailing linkages in wheat fructans. Therefore, most of the carbon flux from sucrose to fructan in wheat is mediated by 6-SFT  for TGW were identified in the same or adjacent region on chromosome 4A , sucrose transporter (SUT), and starch synthase I (SSI), were correlated with changes in grain yield Supplementary material 2 (DOC 81 kb)Supplementary material 3 (DOC 114 kb)Below is the link to the electronic supplementary material.


N = 1152), we successfully genotyped eight candidate SNPs from the previous GWAS, two of which were significantly associated with several growth and fillet traits measured at harvest. The genes proximal to these SNPs were identified by alignment to the salmon reference genome and are discussed in the context of their potential role in underpinning genetic variation in salmon growth.Understanding the relationship between genetic variants and traits of economic importance in aquaculture species is pertinent to selective breeding programmes. High-throughput sequencing technologies have enabled the discovery of large numbers of SNPs in Atlantic salmon, and high density SNP arrays now exist. A previous genome-wide association study (GWAS) using a high density SNP array (132K SNPs) has revealed the polygenic nature of early growth traits in salmon, but has also identified candidate SNPs showing suggestive associations with these traits. The aim of this study was to test the association of the candidate growth-associated SNPs in a separate population of farmed Atlantic salmon to verify their effects. Identifying SNP-trait associations in two populations provides evidence that the associations are true and robust. Using a large cohort ( Gjedremet al.  indicateet al. , highligPiscirickettsia salmonis [The development of high throughput sequencing technologies has expedited the discovery of millions of genome-wide SNPs, particularly for salmonid species, which have high economic values; e.g., Atlantic salmon ,6, rainbsalmonis  and earlsalmonis  have beesalmonis ,16, whicsalmonis ,18. The salmonis . Nonethesalmonis .The heritability of growth traits, such as body weight and length, in Atlantic salmon is moderate to high e.g., ,20,21); ,21; 20,2r~0.96 to 0.99), but with little correlation between weight traits and fillet colour (r = −0.08). A summary of the heritability estimation and general statistics are given in The population used in the analysis was a random subset of a larger population (Population 2) measured for overall and component weight traits, colour and fat content. Heritabilities of fillet-related traits were moderate to high (0.52 to 0.53), whereas the waste weights  were approximately 0.3. The heritability of fat percentage and fillet colour was slightly lower (0.14 to 0.18). The phenotypic and genetic correlations were high for all of the weight-related traits (Based on the results of the 2007 year group (Population 1) GWA analysis, 16 nominally significant SNPs were selected for genotyping in the 1999 year group (Population 2). These SNPs were chosen from QTL regions on chromosomes 16, 21 and 28 for weight and chromosomes 5, 16, 17 and 20 for length . Assays p < 0.05) with most of the fillet and waste traits, including a suggestive association with fat content (p < 0.1). The adenine allele corresponds to higher trait means for the carcass weight and fatness traits. The SNP AX88141678 was associated with overall harvest weight, head weight and gutted weight (p < 0.05). At this SNP, the adenine allele was also associated with higher trait means for the carcass and overall weight traits. The estimation of the additive genetic variation explained by the SNPs indicated that AX88270804 explained a small percentage of the overall variation in fillet traits (~1%), waste traits (2% to 3%) and fat percentage (4%). The SNP AX88141678 explained approximately 1% of the additive genetic variation in the weight-related traits (The SNP AX88270804 was significantly associated (d traits . To accoMEP1A (meprin A subunit beta-like) and PCNT (pericentrin), respectively. AX88270804 was located in an exon (non-synonymous), whereas AX88141678 was in a non-coding region , and the putative genes proximal to the SNPs were identified, indicating that the loci AX88141678 (chr. 5) and AX88270804 (chr. 16) were located within g region . The detet al. [The main results of the GWA analysis in Population 1 are given in Tsai et al. . HoweverAbundant SNPs discovered by modern sequencing technologies and bioinformatics tools have allowed us to better understand the association between genomic variation and production traits in aquatic species . In a rePCNT gene and showed a significant association with several muscle and skeletal growth traits (p < 0.05) in Population 2 (current study) and growth traits (p~10−4) in Population 1 [PCNT gene encodes the centrosome protein pericentrin, which contributes to the organisation of the mitotic spindle for the segregation of the chromosomes during cell division, thus influencing cell cycle progression. Mitotic centrosome dysfunction caused by pericentrin mutations can be expected to cause disturbances in cell division and is known to result in seriously stunted growth of the body and brain [PCNT gene in salmon also has a suggestive association with fat percentage, explaining approximately 4% of the genetic variation. As expected, the allele associated with faster growth is also associated with increased fatness ) has been shown to result in marked expression changes of MEP1A in the intestine [MEP1A expression was shown to differ between domesticated and wild brook char  and its putative effect on growth factors was postulated to be the underlying mechanism for the higher expression in selected fish [MEP1A gene and growth traits and its postulated functional connection to the growth traits raise the possibility that the causative effect underlying this association may be mediated via the MEP1A gene itself. It is worth noting that the genotype means for the SNP suggest an overdominance effect, which may explain why the additive variation explained is very small . A genotype by environment interaction has been shown to be evident for the direction of association of individual SNPs . Alterns  comprising the 1999 year group of LNS broodstock that were measured for weight and other fillet traits at harvest (“Population 2”). The 1152 genotyped fish were across 191 full sibling families from 131 sires and 185 dams. The phenotypes were measured by LNS at harvest (approximately 3 years old), including overall harvest weight (kg), gutted weight (kg), deheaded weight (kg), fillet weight (kg), head weight (kg), gut weight (kg), body waste weight (kg) and total waste weight (kg), fat percentage ) and fillet colour , ranging from 20 (Yellow) to 34 (Red)). The body waste weight was calculated as deheaded weight minus fillet weight , and total waste weight was by head weight plus body waste weight. Details of the population and phenotype measurement are given in Tsai et al. and Peñaloza et al. [The GWAS used to identify the SNPs with putative association with growth in commercial salmon populations was based on the 2007 year group population of the Landcatch Natural Selection  broodstock that were measured for weight and length at the end of the freshwater period  . To testa et al. . An adiphttp://www.responsiblebreeding.eu/) Code of Good Practice for Farm Animal Breeding and Reproduction Organizations.All animals were reared in accordance with all relevant national and EU legislation concerning health and welfare. Landcatch is an accredited participant in the RSPCA  Freedom Foods standard, the Scottish Salmon Producers Organization Code of Good Practice and the EU Code-EFABAR (p~10−3) were selected. Secondly, chromosome 20 was identified as containing loci affecting growth and fillet-related traits in Population 2 [p~10−2) [The candidate SNPs were selected based on two relevant studies ,20. Firs[p~10−2)  from thi[p~10−2) ) for genY represents the observed phenotype, μ is the overall mean of the trait, G is the fixed effect of the SNP genotype, A is the additive genetic effect and e is the residual error. For estimating heritability, the equivalent model was used, but without the SNP effect (G) using the model:2a is the additive genetic variance and σ2p is the total phenotypic variance. The analysis was performed by ASReml 3.0 [The heritability of the traits was calculated as described previously . The simReml 3.0 .p is the frequency of the most frequent allele, q is the frequency of the minor allele and VA is the total additive genetic variance of the trait obtained when no SNP effects are included in the model.The allelic substitution effects of informative SNPs were estimated using Model (1) performed by ASReml 3.0 . The SNPTo identify candidate genes near the significant SNPs, the flanking sequence was aligned to the Atlantic salmon reference genome assembly (GCA_000233375.4), and the corresponding genome contig and position of the SNPs were noted. Approximately 20 kb of sequence surrounding the SNPs were repeat masked (retrieved from ), and a In genome-wide association studies of complex and polygenic traits, the significant SNPs identified are likely to contain a mix of true associations and false positives. Therefore, verification of GWAS findings in a separate population is an important validation step, and SNP associations identified in more than one population are more likely to be reflecting real QTL. We identified two (out of eight successfully genotyped) SNPs that showed an association with growth traits in two different populations, and two different lifecycle stages, in Atlantic salmon. The SNPs are within the pericentrin and meprin alpha genes, which both have potentially relevant functional connections to the growth and harvest traits studied. Further investigation of these candidate genes may be merited to identify putative causative variation.


The goal of this study was to compare significant SNP selection approaches in the context of complex traits based on SNP estimates obtained by models: a model fitting a single SNP (M1), a model fitting a single SNP and a random polygenic effect (M2), the nonparametric CAR score (M3), a SNP-BLUP model with random effects of all SNPs fitted simultaneously (M4). There were 46,267 SNPs tested in a population of 2601 Holstein Friesian bulls, four traits  were considered. The numbers of SNPs selected as significant differed among models. M1 selected a very large number of SNPs, except for a NRH in which no SNPs were significant. M2 and M3 both selected similar and low number of SNPs for each trait. M4 selected more SNPs than M2 and M3. Considering linkage disequilibrium between SNPs, for MY M2 and M3 selected SNPs more highly correlated with each other than in the case of M4, while for FY M3 selection contained more correlated SNPs than M2 and M4. In conclusion, if the research interest is to identify SNPs not only with strong, but also with moderate effects on a complex trait a multiple–SNP model is recommended. Such models are capable of accounting for at least a part of linkage disequilibrium between SNPs through the design matrix of SNP effects. Functional annotation of SNPs significant in M4 reveals good correspondence between selected polymorphisms and functional information as well as with QTL mapping results. For many years genome-wide association studies (GWAS) have been a useful tool for detecting genetic variants associated with traits in human genetics  introduced by Tibshirani  e.g., a, the elaOur study was focused on the comparison of significant SNP selection approaches in the context of complex traits. Significance of SNPs selected based on estimates obtained from a mixed model routinely used in genomic evaluation of dairy cattle in Poland was compared to three single SNP models. In particular, the genomic selection model, which considers all polymorphisms simultaneously by applying shrinkage on SNP estimates through fitting a normal distribution with a predefined variance, is compared with (i) a linear model with a fixed effect of a single SNP which accounts neither for SNP intercorrelation, nor for family relationships (ii) a linear mixed model with a fixed, single SNP effect and a random additive polygenic effect for capturing the family relationships, and (iii) the CAR score regression proposed by Zuber and Strimmer , which rThe material of our study consists of 2601 Polish Holstein-Friesian bulls genotyped with the Illumina BovineSNP50 BeadChip, which consists of 54,001 SNPs (version 1) and 54,609 SNPs (version 2). The applied SNP selection criteria comprised polymorphism, expressed by the minor allele frequency (MAF), with the minimum MAF of 0.01, and technical quality of a SNP expressed by the minimum call rate of 90 % within the analyzed sample of bulls. After quality control 46,267 SNPs were selected for further analysis.Four traits undergoing a complex mode of inheritance were considered in the study: somatic cell score (SCS) representing a trait with “pure” polygenic mode of inheritance, milk (MY) and fat (FY) yields representing traits with a polygenic mode of inheritance enhanced by single genes with large effects, and non-return rate for heifers (NRH) as a trait with a very strong environmental component expressed by heritability of 0.02. Deregressed proofs of bulls were used as pseudophenotypes. Deregression, which was performed in order to remove ancestral information from the conventional breeding values of bulls, was based on a method proposed by Jairath et al. . The cory = μ + Xβ + ε and (M2) y = μ + Xβ + Z1α + ε were solved using the ASReml3 software  where D is a diagonal matrix weighted by the effective daughter contribution for each bull and σε2 denotes a residual variance; μ denotes an overall mean. The CAR score regression (M3) proposed by Zuber and Strimmer Rempirical, where Rempirical is the empirical non-regularized correlation matrix among SNP genotypes. The CAR criterion was computed using an R package CARE. The genomic selection model (M4) y = μ + Z2g + ε, equivalent to the so called SNP-BLUP model, is routinely used for the prediction of direct genomic breeding values for the Polish Holstein-Friesian population  Strimmer  was seleStrimmer  this appβ in M2 the objective function log fy + log fα, where β and α leads to the mixed model equations  via Bonferroni’s approach, while for a multiple SNP model M4 a nominal P value was used as a selection criterion. In M3 the null distribution of the function of CAR scores κ − 1) degrees of freedom estimated by the R package fdrtool  no SNPs were identified by M1-M3. A very different pattern was observed for M4. Since all polymorphisms’ effects are estimated simultaneously with the underlying normal distribution shrinkage, the SNP selection procedure based on the 0.001 threshold chooses 0.1 % of the most significant SNPs regardless of a trait. As a consequence a very similar number of polymorphisms varying between 125 (NRH) and 182 (FY) for each trait was selected. Since, based on the very large number of SNPs selected, M1 does not seem to be a valid model for GWAS on complex traits, in further result description and discussion we confine ourselves to M2-M4.No marked differences between models were observed regarding the information content, expressed by MAF, between SNPs selected as significant by M2–M4. The average MAF of polymorphisms selected for MY by M2 amounted to 0.39, by M3 to 0.30 and M4 to 0.36 and was even more similar for FY with 0.37 (M2), 0.38 (M3) and 0.49 (M4). The average MAF in group of SNPs common between all considering models is 0.40 for MY and 0.38 for FY. On the other hand, considering MY, on average M2 and M3 selected SNPs which were more highly correlated with each other than was the case for M4 since the average LD expressed by pairwise correlation between significant SNPs was equal to 0.18 and 0.17 for M2 and M3, but only 0.08 for the genomic selection model. For FY, the average LD of 0.11 among SNPs selected by M4 was also lower from the corresponding values form M2 and M3 which were equal to 0.17 and 0.21 respectively. The highest average pairwise correlation was observed between significant SNPs which were common for models M2 and M3.Figure Genomic annotation was carried out for SNPs which were selected as significant by all three modles, i.e., M2, M3, and M3 Table , rangingAlthough some of the genes may represent spurious associations arising through high LD to DGAT1, those which are located in more distant regions of BTA14 are potential candidate genes. Special attention is to be focused on EEF1D which according to a recent study of Xie et al.  in dairyWe observed that for complex traits approaches which model the genetic component by a single SNP — here represented by M1, which are very common and successful in GWAS for disease traits, have methodological problems. Since such models do not account for the polygenic background differentiating between SNPs in high LD, especially in the neighborhood of genes with strong effects is problematic and the multiple testing correction is not sufficient to remove the SNP effect upward bias due to correlated SNPs. An intensive post-processing of SNP estimates and/or corresponding P values regarding LD is then required.p = 1/2Ne. Note, that here we used the estimated number of heterozygous QTL segregating for production traits as N = 10.73  or a multiple–SNP without any assumptions regarding the inheritance mode (like M3). In our study M2 and M3 for MY and for FY, above and beyond the M2-M3, also the genomic model (M4) selected a similar number of SNPs as the number 55 resulting from the estimator proposed by Hayes and Goddard  of the tHowever, if the research interest is to identify SNPs not only with strong, but also with moderate effects on a complex trait a multiple–SNP model – here represented by M4, is recommended. Such models are capable of accounting for at least a part of LD between SNPs through the design matrix of SNP effects. A fine tuning of SNP selection procedure is needed based on the expected number of SNPs influencing the variability of a given trait, e.g., based on heritability estimates.Recently, Wang et al.  applied


We explored SNP-SNP interactions on survival using the likelihood-ratio test comparing multivariate Cox’ regression models of SNP pairs without and with an interaction term. We found two interacting pairs associating with prognosis: patients simultaneously homozygous for the rare alleles of rs5996080 and rs7973914 had worse survival , and patients carrying at least one rare allele for rs17243893 and rs57890595 had better survival . Based on in silico functional analyses and literature, we speculate that the rs5996080 and rs7973914 loci may affect the BAFFR and TNFR1/TNFR3 receptors and breast cancer survival, possibly by disturbing both the canonical and non-canonical NF-κB pathways or their dynamics, whereas, rs17243893-rs57890595 interaction on survival may be mediated through TRAF2-TRAIL-R4 interplay. These results warrant further validation and functional analyses.In breast cancer, constitutive activation of NF-κB has been reported, however, the impact of genetic variation of the pathway on patient prognosis has been little studied. Furthermore, a combination of genetic variants, rather than single polymorphisms, may affect disease prognosis. Here, in an extensive dataset ( NQO1 gene, the expression and function of which is closely connected to the NF-κB network . A . A 23]. survival . To discinteraction 6.98, 95% CI = 3.3-14.4, P = 1.42E-07, Table Plikelihood-ratio-corrected = 0.003)  and rs7973914  was found to be associated with patient survival: patients carrying the homozygous rare allele for both SNPs  had worse overall survival compared to carriers of at least one common allele  (Table Plikelihood-ratio-corrected = 0.005)  and was not confined to any specific subset of patients  and rs57890595  to be associated with patient survival: Patients simultaneously carrying at least one rare allele for both variants  had better overall survival compared to the patients with common homozygous genotypes . rs5996080 is in strong linkage disequilibrium (r2 = 1) with 15 SNPs located in BAFFR. The other SNP in the pair, rs7973914, is located on chromosome 12, 8kb upstream of the NF-κB pathway gene TNFR3 ) and 27kb upstream of TNFR1 . rs7973914 resides in a short haplotype block with very few SNPs.Of the recessive SNP pair, rs5996080 resides in chromosome 22, in the flanking region (31.5 kb downstream) of the NF-κB pathway gene TRAF2 (TNF receptor associated factor 2). The other SNP in the pair, rs57890595, is located in the chromosome 8, and lies in the intronic region of NF-κB pathway gene TRAIL-R4 . Other nearby genes include TNFRSF10A and TNFRSF10C.Of the dominant model SNP pair, rs17243893 is located in chromosome 9, within the intron of NF-κB pathway gene 2 > 0.2), we utilized the ENCODE-based functional annotations at the Haploreg and RegulomeDB databases in order to determine whether they are located within genomic regulatory elements in human mammary epithelial cells (HMEC).To investigate the possible functional role of the interacting SNPs or other SNPs in the surrounding LD regions (r2 and/or D’ >0.8). The Haploreg annotation indicated that in HMECs, the majority of rs5996080 proxies are located in regions with enhancer histone marks. We identified 10, 22, and 6 SNPs mapping to regions with promoter histone marks, enhancer histone marks, and DNase hypersensitivity sites, respectively. Of the SNPs mapping to DNase hypersensitivity sites, four were overlapping with regions with promoter histone marks, and three were overlapping with regions with enhancer histone marks (2 = 0.86 with rs5996080) alters a putative transcription factor binding motif of NF-κB. The rs5996080 SNP itself changes the putative motif binding of ERα-a. As for rs7973914, four SNPs in the LD region map to regulatory regions with enhancer histone marks in HMEC. Of these, three SNPs are in strong LD with rs7973914 (r2 and/or D’ >0.8)  .in silico analysis was limited by the short/undefined haplotypes, as indicated above. Haploreg and RegulomeDB annotation maps two SNPs in the rs17243893 locus  in HMEC to regions with enhancer histone mark and DNase hypersensitivity sites. . The interacting SNPs themselves were not genotyped in either of the data sets, but are represented through LD.To further evaluate whether the interacting pair SNPs might have a functional effect or be related to other functional SNPs that affect the expression level of their corresponding genes in NF-κB activating pathway, we performed eQTL analysis using two publicly available data sets consisting of genotype and gene expression data: TCGA and METABRIC. To this end, we analyzed all available SNPs in the LD region (r2 = 1) consistently associated with higher expression of BAFFR in both the TCGA tumor (P = 0.049) and METABRIC data sets (P = 0.003). We also detected one rs5996080 proxy  which associated with the expression of TNFR1 (P = 0.049), and several SNPs in the locus that correlated positively with the expression of TNFR3 . There were only four proxies of rs7973914 available in the TCGA/METABRIC data, and none of them associated significantly with the expression level of either TNFR1/3 or BAFFR.For the recessive model SNP pair, an rs5996080 proxy , and one  in the normal tissue data (n = 85), that correlated with the expression of TRAIL-R4 (P = 0.004). Another SNP in the region rs57890595  associated with the expression level of TRAF2 (P = 0.0006) in the METABRIC data.For the dominant model SNP pair, rs17243893 and rs57890595, we did not find any significant correlation between the expression of the few rs17243893 proxies and TRAF2, nor with TRAIL-R4. We identified one SNP rs57890595 in the TCGA tumor data  in these loci are in general TNF receptors , and rs7973914 is in the intron of SCNN1A . SREBF2 gene is a lipogenesis transcription factor which has been shown to be up-regulated in a breast cancer cell line (HCC1143) compared to normal mammary epithelial cells (MCF10A) [SCNN1A, a read-through fusion transcript of SCNN1A-TNFR1 has been recently identified in breast cancer cell lines as well as in primary breast cancer tumors, and was not detected in normal tissues [2 = 1) associates with decreased expression of SREBF2 and SCNN1A  which might also contribute to the observed interactive survival effect. BAFFR, the candidate NF-κB gene in the locus with significant eQTL association with rs5996080, is one of the best known receptors involved in the non-canonical NF-κB pathway and is known to be the physiological signal that promotes the processing of RelB/P100 to ReLB/P52 resulting in the activation of NF-κB2 [BAFFR and TNFR1/TNFR3. This is in line with the poor breast cancer survival observed in this study, due to the BAFFR-mediated activation of the non-canonical pathway contributing to cell survival [2 = 0.86) alters a binding site for the NF-κB transcription factor itself, as well as a putative ERα binding site. In light of this, it would have been interesting to investigate the interaction in subgroups defined by ER status, but such an analysis was unfortunately not feasible due to the limited number of cases in the interacting genotype category. Taken together, we speculate that the combination of the rare homozygous alleles for rs5996080 and rs7973914, or the causative variants in LD with them, might simultaneously compromise the BAFFR and TNFR1/TNFR3 receptors’ function  . SREBF2 (MCF10A)  which is(MCF10A) , 27. For tissues . Interesf NF-κB2 . Althougf NF-κB2 . TNFR1 ssurvival , 32 and survival . Indeed,survival , 35. AddUnder the dominant model, we identified an interaction between rs17243893 and rs57890595: compared to the patients with wild type homozygous genotypes, patients carrying at least one rare allele for both variants have a better 10-year breast cancer survival. Interestingly, the genotype combination associating with improved survival also tended to associate with negative axillary lymph node status and, although not to a statistically significant degree, less frequent distant metastasis at diagnosis.TRAIL-R4. Evidence suggests that NF-κB activation by TRAIL-R4 takes place through a TRAF2-NIK-IKK cascade. This implicates an interplay between TRAF2 and TRAIL-R4 that ultimately promotes cell survival [fos expression and consequently the AP-1 activity in pancreatic adenocarcinoma cell lines (MDApanc-28) [TRAIL-R4 and TRAF2. Nevertheless, these two genes remain plausible candidates for the prognostic interactive effect: TRAF2 /TRAIL-R4-mediated NF-κB activation results in cell survival which would be consistent with the poor prognosis observed in this study under the hypothesis that the rs17243893-rs57890595 (or the causative variants in LD with them) interaction potentiates or otherwise dysregulates this signaling cascade.rs17243893 is an intronic SNP located in TRAF2, an oncogene which activates NF-κB in epithelial cancers including breast cancer, by activating the NIK-IKK complex  . UnfortuIn summary, in analyses of 917 SNPs in 75 genes of the NF-κB activating pathway, we identified two SNP-SNP interactions to be associated with survival of the breast cancer patients. We have searched for regulatory elements in these loci and examined their influence on gene expression, and we propose a biological rationale for the observed effects. Yet, one must remain aware of potential data interpretation biases introduced by pathway-based SNP selection. For example, long-distance regulatory effects may extend well beyond the local LD block, and the observed prognostic interactions may therefore be caused by genes other than the primary candidate genes or their neighbors. However, we did not observe significant association between the SNPs in LD with the interacting SNP pairs and the expression level of genes elsewhere in the genome in TCGA and METABRIC dataset consistently. A weakness of our study is that large-scale analyses are potentially at the risk of inflated type I error. As even seemingly significant results can easily be false positives in an underpowered analysis, to address this, we chose to use power calculations to define robust HR thresholds for this study . NeverthThe prognosis of complex diseases such as breast cancer is a dynamic process which is influenced by a large number of clinicopathological factors, and is likely also affected by a combination of genetic variants. Compared to an agnostic genome wide analysis, the pathway-oriented approach tested here may increase the chance of obtaining biologically meaningful results with statistical significance regarding SNP-SNP and gene-gene interactions in a prognostic and/or predictive context, although it also necessarily introduces a degree of bias based on the initial hypothesis. These findings can then contribute to functional analyses elucidating the underlying mechanisms of the interplay between genes involved in the NF-κB pathway, and their influence on breast cancer progression and survival.Investigation has been conducted in accordance with the ethical standards and according to the Declaration of Helsinki and according to national and international guide. All participants in these studies had provided written consent for the research. All participating studies were approved by the respective ethical review boards and ethics committees: ABCFS ), ABCS  Commissie Medische Ethiek and Protocol Toetsingscommissie van het Nederlands Kanker Instituut/Antoni van Leeuwenhoek Ziekenhuis), BBCC (Friedrich-Alexander-Universitat Erlangen-Nurnberg Medizinische Fakultat Ethik-Commission), BSUCH (Medizinische Fakultat Heidelberg Ethikkommission), CGPS (Kobenhavns Amt den Videnskabsetiske Komite), ESTHER(Ruprecht-Karls-Universitat Medizinische Fakultat Heidelberg Ethikkommission), HEBCS ), KARBAC , KBCP (Pohjois-Savon Sairraanhoitopiirin Kuntayhtyma Tutkimuseettinen Toimikunta), kConFab/AOCS ), LMBC (Commissie Medische Ethiek van de Universitaire Ziekenhuizen Kuleuven), MARIE (Ruprecht-Karls-Universitat Medizinische Fakultat Heidelberg Ethikkommission), MCBCS (Mayo Clinic IRB), MCCS (The Cancer Council Victoria Human Research Ethics Committee), MEC , OBCS , OFBCR , ORIGO ), PBCS  IRB), pKARMA ), RBCS (Medische Ethische Toetsings Commissie Erasmus Medisch Centrum), SASBAC ), SBCS (South Sheffield Research Ethics Committee), SEARCH (Multi Centre Research Ethics Committee (MREC)).We used primary data from the studies participating in BCAC . All stuwww.genome.jp/kegg/)  . These 7http://www.r-project.org/). P-values for association of the SNPs with tumor characteristics were calculated using Pearson's chi-squared tests. Power analyses for the survival study were performed using the powerSurvEpi package in R. We excluded SNPs with a minor allele frequency (MAF) <1%. The 10-year overall survival was calculated from the time of diagnosis to the date of death due to breast cancer or other reasons (median follow up time 5.6 years), or to the date of the last follow up. To allow for the inclusion of prevalent cases, time at risk was left censored using date of study entry. Survival analyses included Log-rank tests for assessing the statistical significance of differences between Kaplan-Meier curves for survival, and Cox’ regression models for estimating the hazard ratios (HR)s. All Cox’ models were adjusted for study. All p values reported are from two-sided tests. We performed two-way SNP interaction analysis on a total number of 917 SNPs for recessive  and dominant  models of inheritance. To determine the association of SNP-SNP interaction with patients’ survival we used the likelihood ratio test to compare multivariate Cox’ regression models of pairs of SNPs without and with an interaction term (SNP1+SNP2 vs. SNP1+SNP2+(SNP1*SNP2). For each pair of SNPs the best model was selected based on the likelihood ratio test p value. To adjust for multiple testing error we applied the Benjamini-Hochberg post hoc method, which is also known to be robust against moderate dependency between SNPs, for example linkage disequilibrium [p value< 0.01 after correction were selected. Secondly, for the selected set of interaction pairs to be considered significant, we applied a threshold for the HR based on the power analysis for each model. With a sample size of 30,431 with 3375 events and the average MAF of 23.4%, in the dominant and recessive models we had 80% power to detect interaction terms with HR > = 1.4 (or 1/1.4 = 0.6) and HR of > = 6.2 (or 1/6.2 = 0.16), respectively. In addition, for the SNP pairs with significant interaction survival effects, subgroup analyses were performed to investigate whether the survival effect was differentially affected by tumor/patients characteristics and/or treatment. However, the analyses were limited to the subgroups with 5 or more death events .We performed the statistical analyses using R environment for statistical computing version 2.15.2 . The raw genotype data was processed with Affymetrix Genotyping Console Software following the best practices of Affymetrix SNP 6.0 analysis workflow provided by the program provider. The workflow included a quality control step with Contrast QC, a metric that captures the ability of an experiment to resolve SNP signals into three genotype clusters, applying the sample quality threshold of < 0.4  and genotype calling using Birdseed v2 including the genotypes with call rate of ≥95%. After the QC process 1328 samples with both genotype and expression data from tumorous breast tissue remained in the analysis. The normalization of mRNA expression data (Illumina HT-12 v3 platform) was performed by quantile normalization utilizing single target distribution, described in detail elsewhere [In order to analyze the correlation between the loci of interest and the gene expression we utilized the data from The Cancer Genome Atlas (TCGA) and METABRIC project , 45. Frolsewhere . The cislsewhere  using li2 using SNAP [Linkage disequilibrium and the haplotype blocks in the SNP regions were examined based on ring SNAP . To annoing SNAP  and Reguing SNAP . ENCODE ing SNAP .