Malus sieversii in Kazakhstan, followed by intensive introgressions from M. sylvestris. M. sieversii in Xinjiang of China turns out to be an “ancient” isolated ecotype not directly contributing to apple domestication. We have identified selective sweeps underlying quantitative trait loci/genes of important fruit quality traits including fruit texture and flavor, and provide evidences supporting a model of apple fruit size evolution comprising two major events with one occurring prior to domestication and the other during domestication. This study outlines the genetic basis of apple domestication and evolution, and provides valuable information for facilitating marker-assisted breeding and apple improvement.Human selection has reshaped crop genomes. Here we report an apple genome variation map generated through genome sequencing of 117 diverse accessions. A comprehensive model of apple speciation and domestication along the Silk Road is proposed based on evidence from diverse genomic analyses. Cultivated apples likely originate from  Apple is one of the most important fruit crops. Here, the authors perform deep genome resequencing of 117 diverse accessions and reveal comprehensive models of apple origin, speciation, domestication, and fruit size evolution as well as candidate genes associated with important agronomic traits. As a result, cultivated crops diverge from their wild progenitors morphologically and genetically in response to human selection. Recently, rapid advances in sequencing technologies have allowed researchers to sequence entire crop genomes efficiently and the resultant information can help elucidate how human-involved evolutionary processes have shaped modern crop genomes, as demonstrated in peach2, date palm3, tomato4, maize5, soybean6, and rice7. These studies provide valuable information for facilitating future crop improvement and thus securing global food production.Crop domestication is an artificial selection process, fostering co-dependence between human and crop plants. This process often includes an initial phase of unconscious selection by farmers for several thousand years, followed by a subsequent breeding phase of intentional selection of favorable traits by breedersMalus domestica Borkh.), one of the most widely produced and economically important fruit crops in temperate regions, has been domesticated from M. sieversii in the Tian Shan Mountains for 4000–10,000 years, and dispersed from Central Asia to West Europe along the Silk Road, allowing hybridization and introgression of wild crabapples from Siberia (M. baccata (L.) Borkh.), Caucasus , and Europe (M. sylvestris Mill.)8. It has been reported that M. sieversii is the primary progenitor and M. sylvestris is a major secondary contributor of cultivated apples9. Centuries of human exploitation and selection have produced thousands of apple cultivars with diverse fruit sizes and flavors10. In addition, apples are characterized with lengthy juvenile phases, self-incompatibility, and vegetative propagation11. These characteristics together with its unique domestication history indicate that the impact of domestication on the genomes of apples would be much different from that on the genomes of seed-propagated annual crops as well as self-compatible tree fruits such as peach. However, very limited information is available on how the apple genome has been reshaped by human selections and how small and sour wild ancestors have evolved into modern large and sweet apples. In this study, we performed deep genome sequencing of 117 diverse Malus accessions. The large amount of genomic variation resources has not only allowed us to gain new insights into apple evolution and domestication, but also provided invaluable information for accelerating the usage of wild species in apple breeding and improvement.Cultivated apple , 10 M. sylvestris, 29 M. sieversii, 9 M. robusta, 6 M. baccata, 4 M. asiatica, 4 M. hupehensis, and 20 in the remaining 17 wild species with one or two accessions per species  indicated that domesticated apples are significantly bigger, firmer, and sweeter than M. sieversii in Kazakhstan, while less acidulated and much bigger than M. sylvestris , we identified a final set of 7,218,060 single nucleotide polymorphisms (SNPs)  are the closest to pear, followed by Asian wild species M. baccata and M. hupehensis. Notably, the recent introgression from M. sylvestris into M. domestica has been so intensive that the cultivated apples now appear to be closer to European crabapple M. sylvestris than to their progenitor M. sieversii, which is consistent with a previous report9. A principal component analysis (PCA) illustrated a similar pattern to the phylogenetic tree in that M. domestica, M. sieversii, and M. sylvestris accessions formed closely related clusters that were clearly separated from the dispersed accessions of other wild species . The tree showed that accessions of ade Fig. . Wild spies Fig. .Fig. 1Po13 to estimate ancestry proportions for each accession  represent the best model for these 117 accessions . For K = 3, M. domestica and its wild relatives, M. sieversii and M. sylvestris, were clearly separated from other wild species, supporting the evolutionary history of domesticated apple14. With K from 4 to 5, two new subpopulations arose from the wild species other than M. sieversii and M. sylvestris, indicating their high diversity and further distance from domesticated apples. M. sieversii accessions from the two sides of the Tian Shan Mountains segregated into two different subpopulations reflecting their geographical distributions. M. sieversii accessions in Kazakhstan showed admixed ancestry possibly from hybridizations with wild apples such as M. orientalis along the Silk Road, and/or domesticated apples cultivated nearby, while Xinjiang accessions kept their homogeneous genetic background probably due to their geographical isolation that blocks interspecific hybridization. In addition to M. sieversii in Xinjiang, six other species in distinct habitats, such as M. ioensis and M. angustifolia in North America and M. baccata in East Asia, were also identified with homogeneous genetic background, which gave rise to other hybrid species and possess tremendous value in apple breeding practices  of M. domestica (2.20 × 10−3) was lower than that of M. sieversii in Kazakhstan (2.35 × 10−3), M. sylvestris (2.55 × 10−3), and other wild species (4.26 × 10−3), while M. sieversii in Xinjiang exhibited the lowest diversity level (1.30 × 10−3). Compared to other domesticated perennial crops, the nucleotide diversity in cultivated apple is higher than that of peach2 (1.5 × 10−3), but lower than that of cassava15 (2.6 × 10−3) and date palm3 (9.2 × 10−3). The similar level of nucleotide diversity between M. domestica and its progenitor M. sieversii in Kazakhstan indicated a very weak bottleneck, if any, during apple domestication, consistent with findings in previous studies9. Linkage disequilibrium (LD) analyses for each group further supported a very weak and nearly undetectable domestication bottleneck   and M. domestica vs. M. sylvestris for secondary introgression (Dom_Syl)  and 17.2 Mb (4.6%), and harbored 840 and 1089 genes, respectively, among which 246 (29.3%) and 336 (30.9%) showed differential expression during apple fruit development  Fig.  and 10. M. domestica and different wild species groups using the top 1% FST values  (Dw1 and Dw2)18, R genes and receptor kinase genes were identified to have non-synonymous SNPs highly divergent between rootstock and scion cultivars, which could facilitate the study of important apple rootstock traits, such as dwarfing, precocity, and disease resistance  were found to be co-located with selective sweeps  and two β-galactosidase genes (MDP0000921848 and MDP0000179821) in fw1 were under human selection. Interestingly, the β-galactosidase gene, MDP0000179821, was in selected regions of both Dom_SieK and Dom_Syl and showed differential expression during apple fruit development with highest expression at the active cell division stage  and a gene (MDP0000241347) homologous to rice GS3 that controls grain size24 were found in several other selected regions, and they all showed highest expression at the active cell division stage of fruit development . We detected a highly differentiated SNP only between domesticated and other wild apples in the precursor sequences of miRNA172g and miRNA172h, two miRNAs sharing the same precursor sequences . Therefore, besides the previously discovered miRNA172p25, we identified two additional miRNA172 genes (miRNA172g and miRNA172h) that might have contributed to the increase of fruit size during Malus speciation prior to domestication.ces Fig. . No diffNPs Fig. . Theref5, apple domestication was initiated from M. sieversii whose fruits are larger than all other wild apples8. As fruit size is one of the most desirable traits for crop domestication and improvement, apple domestication started with a great advantage and much lower evolutionary pressure than other crops. The finding of fruit weight QTLs, and miRNA172s, and their target genes from comparisons between small-fruited wild apples and large-fruited cultivars helped explain why a weak selection in a highly heterozygous perennial crop can still yield favorable large fruits. The mild increase in fruit size during several thousand years of domestication after speciation, partially contributed by QTLs fw1 and fw2, suggests that apple fruit size has great potential to be increased in future breeding practices, considering that the modern tomato fruit is approximately 100 times larger than its direct wild progenitor.Taken together, we propose a two-step evolution model for fruit size enlargement in apple to characterize its unique evolution process Fig. . Unlike 27. To decipher the genetic mechanism underlying the selection for firm apples, we mined selective sweeps for genes that were potentially involved in regulating fruit firmness. We found a region on chromosome 16 was under intensive human selection in the Dom_SieK contrast, harboring genes encoding key cell wall modifying enzymes including three polygalacturonases , and one glucan endo-1,3-beta-glucosidase (MDP0000295938) , and another sweep from the same chromosome harbored one pectate lyase (MDP0000301545), one glucan endo-1,3-beta-glucosidase (MDP0000206670), and one aldose 1-epimerase (MDP0000737131)  and two pectinesterase genes (MDP0000278119 and MDP0000278118)  Fig. . Further31) Fig. . Similar18) Fig. . A numbe28, we found that both fruit sugar content and acidity have been altered during apple domestication using historical phenotype data in the USDA-GRIN database  with substantially reduced nucleotide diversity in M. domestica compared to M. sylvestris, and another region about 750 bp downstream of Ma1 CDS when compared to M. sieversii in Kazakhstan , 33 by Institute of Pomology of Chinese Academy of Agricultural Sciences , and 14 were collected from the Tian Shan Mountains . Genomic DNA was extracted from young leaves using the DNeasy Plant Mini Kit . Paired-end Illumina genomic libraries were prepared and sequenced on an Illumina Hiseq 2500 platform following the manufacturer’s instructions .31. The cleaned unique reads were aligned to the apple reference genome12  using BWA32, and only uniquely mapped reads were retained. Following mapping, genotypes were assigned to each genomic position for each accession based on the alignment mpileup files generated by SAMtools33. SNPs and small indels in the population of 117 apple accessions were then identified if they were supported by at least two mapped reads, and had an allele frequency ≥0.3. Furthermore, SNP sites with the nearest SNPs less than 5 bp away, containing only heterozygous SNPs, or with more than 50% missing data were excluded.The Illumina paired-end reads from each apple accession were first processed to collapse duplicated read pairs into unique read pairs. Duplicated read pairs were defined as those having identical bases at positions of 15-90 in both left and right reads. The resulting unique reads were processed to remove adaptor and low-quality sequences using TrimmomaticM. domestica, Dom_01; M. sieversii, Sie_K_04, Sie_X_03 and Sie_X_09; M. sylvestris, Syl_01; and M. hupehensis, Hup_01).To validate the SNP calling, we randomly selected 958 homozygous SNP loci across the genome and PCR-amplified and Sanger-sequenced the corresponding genomic regions in six apple accessions (18. PCA was performed using program EIGENSOFT34. A neighbor-joining phylogenetic tree was constructed using MEGA635 in 1000 bootstrap replicates with pear “Pyrus × bretschneideri” as the outgroup. Pear genomic sequence reads36 were downloaded from the NCBI SRA database (accession no. SRP016889) and aligned to the apple genome v1.0p for SNP calling. Population structure was investigated using STRUCTURE13. To determine the most likely number of ancestral kinships (K) in the apple population, STRUCTURE was run 20 times on 2000 randomly selected 4D SNPs for each K value from 3 to 19. The statistic “∆K”, which indicates the change in likelihood of different numbers of clusters, was calculated, and the cluster number with the highest ∆K value, which indicated the most likely number of clusters in the population, was obtained. We then used the whole set of 4D SNPs to calculate the cluster membership for each accession with 10,000 iterations. According to the phylogenetic and population structure analyses, nine accessions  were positioned into unexpected species groups and thus excluded from downstream population genetics analyses.We selected a total of 24,326 SNPs at fourfold degenerate sites (4D SNPs) with minor allele frequency (MAF) ≥5% and missing rate per site ≤10% for phylogenetic and population structure analyses. The rationale for choosing 4D SNPs is that they do not cause amino-acid changes and thus, should be under less selective pressure and more reliably reflect population structure and demographyπ) is often used as a measurement of the degree of the genotype variability within a population or species37. Values of π were calculated for 10-kb non-overlapping windows across the apple genome with the final set of 7,218,060 SNPs using the BioPerl module PopGen38.Nucleotide diversity  was performed using an R function cmdscale to transfer FST values into two dimensional values for plotting. To test the accuracy of this MDS visualization, the Spearman rank-sum correlation ρ was calculated between Euclidean distances between all pairs of values in the MDS plot and the corresponding FST values. SNPs with top 1% FST values were identified as highly divergent variations in comparisons between M. domestica and each of the four wild groups, including M. sieversii in Kazakhstan, M. sieversii in Xinjiang, M. sylvestris, and other wild species, as well as between rootstock and scion cultivars. To better reflect selective pressure on nucleotide variants, non-synonymous SNPs were further identified from the highly divergent SNPs with top 1% FST values, and the corresponding genes containing these non-synonymous SNPs were determined.Population differentiation is evaluated by the fixation index r2) of genotypes was calculated to measure LD level in each of the five groups using Haploview41 with the following parameters: -nogui -minMAF 0.10 -hwcutoff 0.001 -dprime. SNPs within each group were extracted for the analysis. SNPs within 500-kb sliding windows were used to estimate average r2 at various physical distance classes, and the LD decay was plotted as a function of the derived average r2 and physical distances. Tag SNPs were also identified using Haploview with parameters set as follows: -nogui -minMAF 0.05 -hwcutoff 0.001 -dprime -blockoutput GAB -minGeno 0.8 -pairwiseTagging.Correlation coefficient (M. domestica (Dom) are derived from those of M. sieversii in Kazakhstan (Sie_K) or M. sylvestris (Syl), the entire genome was scanned using SNPs with MAF ≥5% and missing rate per site ≤10%. First, allele frequency per site was computed in each species group and the allele with the frequency >0.6 was assigned as the major allele. For each site, we considered the major allele in Sie_K as the ancestral allele in Dom if these two groups share consistent major alleles; similarly, Syl was considered as the allelic ancestor for Dom when they had the same major alleles. For each non-overlapping 100-kb window, if the count of ancestral alleles as Sie_K was greater than two times of that as Syl and the total SNP count was larger than five, we regarded this region as being derived from Sie_K; similarly, if the ancestral-allele count ratio between Syl and Sie_K was larger than two and the window contained more than five SNPs, we considered this window as originating from Syl.To evaluate whether major alleles of 42, following previous studies44 with modifications. Briefly, selection screening was performed in two contrasts: M. domestica vs. M. sieversii in Kazakhstan for initial domestication (Dom_SieK) and M. domestica vs. M. sylvestris for introgressive domestication (Dom_Syl). Genetic distances between adjacent SNPs were calculated according to their physical distances in a widely used genetic map reported with the apple reference genome12. Parameters “-w1 0.0005 100 100 -p0 0.7” were adopted to run the program for each chromosome, allowing XP-CLR scores to be calculated for each 100-bp region using unphased SNPs with correlation level at 0.7 and no more than 100 SNPs per window (in size of 0.05 cM). The XP-CLR scores per 100 bp were averaged across non-overlapping 10-kb windows on each chromosome. Adjacent 10-kb windows with an average XP-CLR score no less than 80% of the genome-wide average XP-CLR score were joined as putative selective regions, which were further merged together if two regions were separated by only one low-score 10-kb window. The maximum of window-wise average XP-CLR scores in a merged region was assigned as the region-wise XP-CLR score. Merged regions with top 10% of region-wise XP-CLR scores were considered as candidate selective sweeps. To improve the prediction accuracy, only candidate selective sweeps having top 50% of π ratios between the ancestral and domesticated species were kept.Selective sweeps across the genome were identified using a XP-CLR method that models the allele frequency spectrum to search for signals of differentiation between two populations45.Genes in the candidate selective sweeps were grouped by their physical locations into each 1-Mb non-overlapping segments throughout the genome. Adjacent segments and segments separated by no more than 1-Mb were merged together, while segments containing no more than five genes were discarded. GO term enrichment analyses were conducted for genes in each merged segment along the genome using GO::TermFinderhttps://npgsweb.ars-grin.gov/gringlobal/cropdetail.aspx?type=descriptor&id=115). The phenotypic scores of fruit flavor, weight, width, length, flesh firmness, skin color, and soluble solid content were downloaded for 66 out of the 70 accessions from USDA-ARS that are included in the USDA-GRIN database. Phenotype scores recorded in multiple years for a particular accession were screened to exclude outliers (more than 1.5 times the interquartile range below the first quartile or above the third quartile) and then averaged across the years. Tukey’s HSD mean separation tests were conducted to analyze these phenotypes in pairwise comparisons between M. domestica, M. sieversii in Kazakhstan, and M. sylvestris.The USDA-GRIN database archives phenotypic observations for 105 different traits in 4123 apple accessions from 1995 to 2014 ; 3, medium red ; 4, red (without green or yellow base); 5, dark red. Same was done on 13 accessions collected from China  implemented in the FaST-LMM program46. Raw P values were adjusted for multiple testing using the Benjamini-Hochberg procedure47 and a significant association was based on a threshold of false discovery rate of 0.1 .Phenotypic data of skin color was used for the association study. To improve phenotyping of fruit skin color, we manually examined fruit pictures in the USDA-GRIN database and assigned a score of fruit skin color for each accession using the following scoring system: 0, 48 and sequenced on the Illumina HiSeq 2000 system using single-end 51-bp mode. Around 20–50 million reads were generated for each of the 15 libraries. Raw reads were first processed to remove adapter and low-quality sequences using Trimmomatic31. Reads shorter than 40 bp after trimming were discarded. The resulting reads were then aligned to the ribosomal RNA database49 using Bowtie50, allowing up to three mismatches. The aligned reads were discarded and the remaining reads were aligned to the apple genome (v1.0) using HISAT51. Raw counts for each apple gene were derived from the read alignments and normalized to reads per kilobase of exon model per million mapped reads (RPKM). Differential expression analyses were performed using edgeR52. Genes with adjusted P values less than 0.01, expression changes of at least twofold, and expression levels of at least 1 RPKM in at least one of the five stages were identified as differentially expressed genes during fruit development.Fruit samples were taken at five stages of fruit development from the apple cultivar “Greensleeves”: 18 days after bloom (DAB) (active cell division), 37 DAB (the end of cell division), 67 DAB (rapid cell expansion), 90 DAB (rapid cell expansion), and 132 DAB (ripening). Three biological replicates were collected for each stage. Fresh tissues were immediately frozen in liquid nitrogen and ground to fine powder. Total RNA was extracted using the QIAGEN RNeasy Plant Mini Kit  followed by treatment with RNase-free DNase I  according to the manufacturers’ protocols. Strand-specific Illumina RNA-Seq libraries were constructed following the protocol described in Zhong et al.ftp://bioinfo.bti.cornell.edu/pub/Apple_SNP/.Sequence reads of genome and transcriptome resequencing have been deposited into the NCBI sequence read archive under accessions SRP075497 and SRP102350, respectively. SNP data sets can be accessed at Supplementary InformationSupplementary Data 1Supplementary Data 2Supplementary Data 3Supplementary Data 4Supplementary Data 5Supplementary Data 6Supplementary Data 7Supplementary Data 8Supplementary Data 9Supplementary Data 10Supplementary Data 11Supplementary Data 12Supplementary Data 13


Picea glauca) is used to illustrate our purpose. In Quebec, one of the favored strategies to produce elite varieties is to use somatic embryogenesis (SE). In order to detect human errors both upstream and downstream of the white spruce production process, this project had two main objectives: (i) to develop methods that make it possible to trace the origin of plants produced, and (ii) to generate a unique genetic fingerprint that could be used to differentiate each embryogenic cell line and ensure its genetic monitoring. Such a system had to rely on a minimum number of low-cost DNA markers and be easy to use by non-specialists. An efficient marker selection process was operationalized by testing different classification methods on simulated datasets. These datasets were generated using in-house bioinformatics tools that simulated crosses involved in the breeding program for which genotypes from hundreds of SNP markers were already available. The rate of misidentification was estimated and various sources of mishandling or contamination were identified. The method can easily be applied to other production systems for which genomic resources are already available.Biological material is at the forefront of research programs, as well as application fields such as breeding, aquaculture, and reforestation. While sophisticated techniques are used to produce this material, all too often, there is no strict monitoring during the “production” process to ensure that the specific varieties are the expected ones. Confidence rather than evidence is often applied when the time comes to start a new experiment or to deploy selected varieties in the field. During the last decade, genomics research has led to the development of important resources, which have created opportunities for easily developing tools to assess the conformity of the material along the production chains. In this study, we present a simple methodology that enables the development of a traceability system which, is in fact a by-product of previous genomic projects. The plant production system in white spruce ( In agriculture and other industry sectors, traceability of the biological material has become mandatory to assess quality control and to trace material upstream and downstream of the value chain. Phytosanitary issues such as the mad cow disease crisis have certainly contributed to stress the importance of tracing living material , one of the most planted forest tree species in Canada, has become central to genomic research programs in the last decade (Arborea and SmartForest), and is the current focus of major genomic investments in advanced breeding programs in Canada (FastTRAC Project). As for many breeding programs, white spruce improvement relies on the production of offspring produced through controlled crosses among many elite trees. The scheme of production for white spruce elite varieties to be adopted by Quebec nurseries consists of the large-scale production of white spruce seedlings (up to 2 million/year) through somatic embryogenesis (in vitro culture/tissue culture) in combination with rooted cuttings. This is seen as the most efficient means to increase genetic gains (up to 35%), but also to decrease rotation age (by up to 10 years); in other words, more volume in less time . In white spruce, the seedling production strategy is no different from other systems and consists of a multi-stage procedure. First, breeding activities, which are very labor-intensive because they are achieved within a narrow time frame, are subdivided in to different sub-activities: seed orchard establishment with grafted materials, pollen collection, controlled crosses, cone collection, seed extraction, and seed storage. These multiple steps are then followed by somatic embryogenesis (SE), which also consists of four main stages: initiation, growth (including cryopreservation), maturation of the somatic embryos, and germination  advanced breeding population managed by the Ministère des Forêts, de la Faune et des Parcs (MFFP) of the province of Quebec, which consists of a subset of 73 (superior) elite trees that were selected by the MFFP for seedling production using somatic embryogenesis (SE).The development and testing of the traceability system were conducted using a large part of the white spruce . While several SNP genotyping technologies and platforms exist, the Sequenom® iPLEX® Gold Technology (hereafter referred to only as Sequenom®) was used because it makes it possible to build SNP arrays with a modest number of markers that are affordable for end-users (nurseries), and also because commercial genotyping platforms using this technology are available and make obtaining genotypic information easy for end-users. In addition, this technology is less restrictive in terms of quality and quantity of template DNA. Based on this, our objective was to select the 40 most informative SNPs among the subset of 458 candidate SNPs, that corresponded to the capacity of no more than a single Sequenom® genotyping array  close to 0.5 . The third procedure relied on the Random Forest classification algorithm , for a total of 15 simulated progeny datasets.The capacity of each discrimination procedure described above to correctly assign progeny to their cross of origin was assessed using a second ensemble of simulated progeny datasets  FAP, which uses an exclusion-based analysis for parental assignment Taggart, ; and (2)The discrimination procedure presenting the best assignment performance using the simulated progeny datasets and altered simulated progeny datasets was then used to choose the 40 most informative SNPs and build the Sequenom® SNP array. Also, SNPs were selected from as many distinct genes as possible in order to minimize linkage between markers using data from the gene catalog developed for white spruce  were submitted to the program to create 15 altered datasets presenting different percentages of missing data , genotyping errors  and a combination of the two  located at the St-Modeste forest nursery and at the MFFP scientific headquarters in Québec.Before DNA extraction, the samples from embryogenic cell lines stocked in cryogeny had to be cleaned by removing the cryoprotectant DMSO. Embryogenic tissue samples stored in cryopreservation were thawed in 37°C water for 2 min. DMSO was removed by centrifuging for 6–10 min at 5,250 rpm and discarding the supernatant. Cells were cleaned by re-suspending the pellet in 500–750 μL of PBS 1x buffer, transferred to eight-strip-tubes, and centrifuged at 5,250 rpm for 10 min. The supernatant was removed as described above and the pellet was stored at −20°C until DNA extraction.DNA was extracted from fresh needles and embryogenic tissue using the Nucleospin 96 Plant II kit  following the manufacturer's protocol for vacuum processing with the following modifications: (a) cell lysis using buffers PL2 and PL3 (PL2 was heated for 2 h at 65°C instead of 30 min), and (b) elution with an in-house Tris-Cl 0.01 mM pH 8.0 buffer.Figure 4). Allele frequencies and summary statistics for all SNPs were calculated using Cervus v.3.0.7 . Moreover, multilocus fingerprints for each individual (embryogenic tissues and trees) were generated by the concatenation of all single genotypes for each individual from each cell line.All samples  were genotyped using the 40-SNP Sequenom® array developed in this project. Genotyping was performed at the Génome Québec Innovation Centre  using their internal protocols. In total, after removing individuals presenting more than 15% of missing data, genotypes were obtained for 2,845 trees and 1,501 tissue samples, for a total of 4,346 samples representing 1,517 cell lines , and Fst . The procedure based on Fst was the least efficient (0.47% error rate using FAP and 0.50% using Papa). Interestingly, only six SNPs were shared among the 40 most informative ones by the three procedures tested. Following these comparison results, the set of 40 most informative SNPs used to develop the Sequenom® genotyping array was identified with the Random Forest procedure, and additional most informative SNPs to satisfy the probe design of the Sequenom genotyping assay were also drawn from this procedure (see below).The selection of the most informative markers was done using simulated progeny datasets , large differences were noticed . Manual verification of those trees was done by inferring their genotypes using the genotypes of their progeny; it led to the conclusion that their genotypes obtained from Sequenom® were the correct ones and that the differences observed were likely caused by genotyping the wrong trees as a result of laboratory misidentification or mislabelling in the previous large-scale genotyping project . When testing simulated progeny datasets presenting genotyping errors, Papa was more robust than FAP. For instance, when a percentage of 0.5% genotyping errors was introduced over the whole dataset, Papa resulted in a percentage of wrong assignments that varied between 0 and 0.8% (average of 0.1%), while it varied between 0.3 and 8.7% (with an average of 5.1%) using FAP. Conversely, when the occurrence of missing data was tested alone, the use of the FAP software resulted in the highest robustness, with a maximum level of false assignments not exceeding 0.01%. The use of Papa showed that crosses that had one of the two parents in common were, not surprisingly, more afflicted by wrong assignments (data not shown). Globally, the effect of missing data appeared negligible for FAP. In fact, we observed about the same overall performance for this analysis when we tested the effect of genotyping errors only or the combined effects of genotyping errors and missing data.In the absence of missing data or genotyping errors, the genotypes obtained for the simulated progeny datasets using the 36 successful SNPs of the Sequenom® array showed no errors when using the FAP assignment method  could be unambiguously assigned to their putative cross of origin. For most of them (with the exception of 25 lines), more than one individual (tree) was analyzed. For 84 of the 112 problematic lines detected , with the exception of 11 pairs of cell lines that shared an identical fingerprint.The purpose of this work was to show how it is possible to design a reliable in-house traceability system using a modest number of informative SNPs, and how it can be used to detect several sources of misidentification in a multi-step plant production system. The criteria followed for ease of use were: (i) the design step (marker selection) should be easy to understand and follow by non-specialists  and; (ii) the traceability procedure should not be complex to use and interpret by end-users . Indeed, with only 36 informative SNPs, we showed, using both simulated and real genotype datasets, that the SNPs selected could be used to test the origin of the trees in a large-scale production system.SNP markers are well-suited for traceability purposes. Although they are less informative than microsatellites , which represent the first markers to be used in identity control systems or in forensics  to an observed Fst = 0.370 ; however this difference between the Fst indices obtained with simulated and real data could have been influenced by the number of progeny, which was equal between crosses (n = 100) for the simulated progeny datasets and heterogeneous (between 8 and 34 per cross) for the real data. The advantage of Random Forest over the MAF method may appear weak, but should not be considered negligible because other factors may interfere as we observed in our experiment, i.e., the genotyping technology switch that diminished the effective number of markers available and the genotyping technology itself for which a 100% success rate cannot reasonably be expected.The production of simulated progeny datasets through the use of existing parental genotypic information, and their subsequent use in apa), although, and as previously demonstrated  was less sensitive to the presence of missing data than the likelihood method  in seed orchards has also proven them to often be mislabelled , and the family ranking for growth and wood quality traits was quite affected  trees compared with unimproved stock  and family heritability (H2F)  distinct multilocus haplotypes, we can suppose that we can retrieve the uniqueness of the improved trees in the years following planting. This feature can be useful in monitoring which lines better survive and reproduce in the wild. Moreover, such an in-field traceability system can also be used to measure genetic diversity in planted sites, which is an interesting feature given that forest biodiversity is a major concern for population geneticists, foresters, and forest certification organizations especially in the context of climate change  coming from the use of a traceability system in white spruce.In this project, three central objectives were achieved: (i) we established an analytical pipeline to design an efficient genotyping tool enabling the traceability of biological materials; (ii) we verified the presence of errors upstream of the multiplication process, i.e., pedigree problems related to breeding activities; and (iii) we generated unique fingerprints for every cell line for traceability purposes during the tree production and deployment process. We showed that a small number of informative SNPs could be used to discriminate efficiently among a large number of full-sib families and track down the illegitimate progeny, and could be used to follow up cell lines and their individuals at different steps of the production and deployment process. Hence, this work highlights the fact that once genomic data associated with the foundation germplasm involved is available, it is quite simple to create simulated datasets representing the variability associated with the putative offspring, and then to identify the most informative markers using a discrimination procedure such as the Random Forest algorithm.As in the white spruce example presented here, many living production systems use germplasm under the form of elite parents or stock to generate progeny that will form the basis of the exploitation system . Therefore, a pipeline as shown herein for identifying informative SNPs and then using them for parentage assignment and individual fingerprinting could be useful in those systems to enable traceability at various scales, from research to production.Not only should traceability be used in closed production systems such as the one presented herein, but tracing the origin could also be applied to fulfill similar demands in alternative markets. Today, consumers value new aspects in their buying choices, such as the country of origin and/or the sustainable aspect related to product fabrication (United Nations Global Compact and BSR., JG, LT, JM, JB, and NI contributed to the project design; JG, CL, and NI took part in the experimental work; JG and PL performed the statistical and bioinformatic analyses; JG, LT, CL, AR, JB, and NI were involved in both data acquisition and interpretation; JG wrote the manuscript. All the authors contribute in drafting and reviewing the article. All the authors approved the final version of this text.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. The reviewer OKH and handling Editor declared their shared affiliation, and the handling Editor states that the process met the standards of a fair and objective review.


Streptococcus mutans (S. mutans) is the principal pathogen involved in the formation of dental caries. Other systemic diseases have also been associated with specific S. mutans serotypes . Silver nanoparticles (SNP) have been demonstrated to have good antibacterial effects against S. mutans; therefore, limited studies have evaluated the antimicrobial activity of biofunctionalized SNP on S. mutans serotypes. The purpose of this work was to prepare and characterize coated SNP using two different organic components and to evaluate the antimicrobial activity of SNP in clinical isolates of S. mutans strains and serotypes; (2) Methods: SNP with bovine serum albumin (BSA) or chitosan (CS) coatings were prepared and the physical, chemical and microbiological properties of SNP were evaluated; (3) Results: Both types of coated SNP showed antimicrobial activity against S. mutans bacteria and serotypes. Better inhibition was associated with smaller particles and BSA coatings; however, no significant differences were found between the different serotypes, indicating a similar sensitivity to the coated SNP; (4) Conclusion: This study concludes that BSA and CS coated SNP had good antimicrobial activity against S. mutans strains and the four serotypes, and this study suggest the widespread use of SNP as an antimicrobial agent for the inhibition of S. mutans bacteria.(1) Background:  Streptococcus mutans (S. mutans) has been considered the principal oral pathogen involved in the development of dental caries ), Shibata et al., 2003 ; serotype e, size 517 bp ; serotype f, size 316 bp ) and Hoshino et al., 2004 ). Electrophoresis assay was used to analyze the PCR products through 2% of agarose gels including a Tris-acetate-EDTA buffer and a 100-bp DNA ladder  as molecular size marker. An ethidium bromide solution (0.5 μg/mL) was used to stain each agarose gel. Images of each gel were obtained under UV illumination  identifying and classifying to the Streptococcus mutans bacteria and its four serotypes .Thirty-four clinical isolates stocks of l., 2003   were cultured in brain-heart infusion broth (BHI) for 18 h at 37 °C. The distribution of serotypes obtained was c = 20, e = 6, f = 1, and k = 7. A microdilution plate assay was used and SNP sample was mixed with BHI medium containing 2% sucrose, and inoculated with 6 × 105 CFU/mL of each clinical isolate strains. After 24 h at 37 °C of incubation, minimum inhibitory concentrations (MIC) were determined based on turbidity . To confirm the MIC, all wells were washed with PBS (phosphate buffered solution), fixed in 0.1% glutaraldehyde for 5 min and stained with 0.5% crystal violet. The MIC was considered the minimum necessary concentration to promote antimicrobial activity in the last well that presented coloration. Purple coloration indicated the presence of S. mutans bacteria . Samples were considered significantly different when p < 0.05.All data were expressed as the mean ± standard deviation. Significant differences between SNP samples and S. mutans bacteria. Higher antimicrobial activity was associated with smaller SNP and the presence of BSA coating. All samples of serotypes c, e, f, and k had similar sensitivity levels to the three different sizes and coatings of SNP; however, serotypes c and e were considered the most resistant, although this difference was not statistically significant. This work indicates that BSA and CS coated SNP have strong antimicrobial effects against S. mutans bacteria including the serotypes c, e, f, and k, suggesting the potential for biomedical uses. According to our knowledge, this is the first study to determine the antimicrobial activity of three different sizes and shapes of BSA and CS coated SNP against clinically isolated stocks of S. mutans bacteria across four different serotypes.The BSA and CS coated SNP with different sizes and shapes were successfully prepared and characterized. All of the treatments had antibacterial activity against clinical stocks of


Alternative splicing (AS) plays important roles in gene expression and proteome diversity. Single nucleotide polymorphism (SNP) and insertion/deletion (InDel) are abundant polymorphisms and co-dominant inheritance markers, which have been widely used in germplasm identification, genetic mapping and marker-assisted selection in plants. So far, however, little information is available on utilization of AS events and development of SNP and InDel markers from transcriptome in radish.In this study, three radish transcriptome datasets were collected and aligned to the reference radish genome. A total of 56,530 AS events were identified from three radish genotypes with intron retention (IR) being the most frequent AS type, which accounted for 59.4% of the total expressed genes in radish. In all, 22,412 SNPs and 9436 InDels were identified with an average frequency of 1 SNP/17.9 kb and 1 InDel/42.5 kb, respectively. A total of 43,680 potential SSRs were identified in 31,604 assembled unigenes with a density of 1 SSR/2.5 kb. The ratio of SNPs with nonsynonymous/synonymous mutations was 1.05:1. Moreover, 35 SNPs and 200 InDels were randomly selected and validated by Sanger sequencing, 83.9% of the SNPs and 70% of the InDels exhibited polymorphism among these three genotypes. In addition, the 15 SNPs and 125 InDels were found to be unevenly distributed on 9 linkage groups. Furthermore, 40 informative InDel markers were successfully used for the genetic diversity analysis on 32 radish accessions.These results would not only provide new insights into transcriptome complexity and AS regulation, but also furnish large amount of molecular marker resources for germplasm identification, genetic mapping and further genetic improvement of radish in breeding programs.The online version of this article (doi:10.1186/s12864-017-3874-4) contains supplementary material, which is available to authorized users. Arabidopsis , marker names are shown on the left of each linkage group. (DOCX 1013 kb)Radish genotypes used for genetic diversity analysis in this study. Additional file 2:The information of detected alternative splicing (AS) events in radish. (XLSX 5752 kb)Additional file 3:The differential alternative splicing events between three genotypes (XLSX 740 kb)Additional file 4:List of novel transcripts detected in the genotypes ‘NAU-YH’, ‘NAU-LB’ and ‘NAU-RG’. (XLSX 523 kb)Additional file 5:Putative single nucleotide polymorphisms (SNPs) in the genotypes ‘NAU-YH’, ‘NAU-LB’ and ‘NAU-RG’. (XLSX 1184 kb)Additional file 6:Putative insertion/deletion (InDel) in the genotypes ‘NAU-YH’, ‘NAU-LB’ and ‘NAU-RG’. (XLSX 517 kb)Additional file 7:The detailed information of putative SSR markers identified in radish. (XLSX 2112 kb)Additional file 8:Primer sequences used for SNPs validation between ‘NAU-YH’, ‘NAU-LB’ and ‘NAU-RG’ by Sanger sequencing. (XLSX 54 kb)


Sodium nitroprusside (SNP) has been previously shown to extend the vase life of various cut flowers; however, its positive effect on extending vase life of carnations has not been well documented. Moreover, the role of SNP in the mechanisms underlying determination of vase life of cut carnations has also not been well addressed.−1 SNP had the longest vase life and maximum relative fresh weight (RFW). This was achieved through significant suppression of ethylene production via downregulation of ethylene biosynthesis and petal senescence-related genes, and through an increase in the scavenging mechanism of reactive oxygen species (ROS) by antioxidant activity during flower vase life. In addition, the positive efficacy of SNP could also be confirmed using 1-aminocyclopropane-1-carboxylic acid (ACC) and different cultivars, resulting in similar trends for both experiments.SNP increased vase life of Tico Viola carnations along with their relative fresh weight (RFW). Among the treatments, the flowers treated with 10 mg LTaken together, these results suggest that SNP plays a crucial role in multiple modes of action that are associated with the longevity of cut carnation flowers.The online version of this article (10.1186/s12870-017-1097-0) contains supplementary material, which is available to authorized users. Dianthus caryophyllus L.) are one of the most important ornamental plants, and are widely used as a cut flower and a bedding plant in gardens . SNP stock solution was prepared following the manufacturer’s instructions. Five cut stems, which had approximately the same fresh weight, were placed into the vases filled with different concentrations of SNP for 24 h. To avoid photodegradation of SNP (releasing nitrosyl ligand and cyanide ion), the vases were wrapped with aluminum foil during the SNP treatment. Next, the treated stems were thoroughly washed under tap water and replaced into vases containing 250 mL distilled water. The vases were then maintained in a growth chamber at a light intensity of 20 μmol−2 s−1 for 12 h, at 23 °C and 60–70% relative humidity. There were three vases (15 flowers) per treatment and the experiment was conducted three times.Glass bottles (500 mL volume) were used as vases for this experiment; they were filled with 250 mL distilled water containing different concentrations of sodium nitroprusside (SNP) (Enzo Life Sciences) [0 (control), 1, 5, 10, 15, or 20 mg LDcACO1 and DcACS1) and petal senescence-related gene , and antioxidant activity measurement using traits such as 1, 1-diphenyl-2-picrylhydrazyl (DPPH), 2, 2′-azino-bis-3-ethylbenzthiazoline-6-sulphonic acid (ABTS), total polyphenol, and total flavonoid content.The initial fresh weight of all flowers in each vase was recorded, after which five flowers were selected to evaluate the relative fresh weight and vase life throughout the experiment. The remaining 10 flowers were used for the estimation of ethylene production, transcriptional analysis of ethylene biosynthesis genes  with ethylene precursor ACC 1 mg L−1 (Sigma Aldrich), either alone or in combination with 10 mg L−1 SNP. The vases were then maintained in the same growth chamber that was used in the above experiment, and the physiological and molecular analyses from the above experiment were performed again for this experiment. There were three vases (15 flowers) per treatment and the experiment was conducted three times.In the above experiment, 10 mg L−1 concentration was found to improve the postharvest life of the carnation Tico Viola; however, we were interested in determining whether SNP would have the same effects on other cultivars, such as Venus, Tico Tico, and Shino Lily. Hence, these other cultivars were obtained from the same flower production farm where Tico Viola was grown. Their samples were prepared in the same manner as done for Tico Viola, and 5 cut stems were placed into the same vase containing 250 mL distilled water (control) with 10 mg L−1 SNP. The vases were then maintained in the same growth chambers that were used in the above experiment. In addition, the same physiological and molecular analyses from the above experiments were performed in this experiment. There were three vases (15 flowers) per treatment for each cultivar and the experiment was conducted three times.SNP at 10 mg LPostharvest life of each flower was determined when more than one-third of its petals showed in-rolling, browning, or loss of ornamental value. The fresh weight of each cut stem was measured daily, and the relative fresh weight (RFW) was calculated using the formula: RFW (%) = (FWt/FW0) × 100; where FWt is the fresh weight of the stem (g) at day  and FW0 is the initial fresh weight of stem (g) at day 1 . Five flFor ethylene measurements, petals (5 g) from each treatment were weighed and sampled after different periods . They were placed in a 50-mL glass tube and enclosed with a rubber septum for 16 h at 20 °C. An aliquot of the accumulated gas (1 mL) was withdrawn using a 1-mL syringe through the septum and analyzed for ethylene using a gas chromatograph . Three syringes (three replicates) were used for each treatment.20 primer using a reverse transcription kit . Transcript levels of ethylene production-related genes (DcACO1 and DcACS1), and petal senescence-related gene  were measured using a StepOnePlus Real-Time PCR system  [DcACT) was used as the internal control. The primers and PCR conditions for the detected genes are listed in . The cDNA was synthesized from 1 μg of the total RNA with an oligo dTam, USA) . To confPetals were collected from the flowers on day 9 after treatment, when most of the control flowers showed a loss in their ornamental value, and were frozen for analysis of antioxidant activity.For DPPH and ABTS activity, 5 g of frozen petals were used and the analyses were performed following the methods of Kim et al. . For theP < 0.05 or 0.01.Data were analyzed in SPSS version 11.09  and are presented as means of three replicates. The significance differences among the means were analyzed at −1 SNP declined. Specifically, at 10 mg L−1, SNP significantly extended the vase life by approximately 6 days, and there was an increase of 2.6, 3.8, and 1.6 days at SNP concentrations of 1, 5, and 15 mg L−1, respectively, compared with the control. Thus, 10 mg L−1 was considered the optimal SNP concentration and used for further experiments.The vase life of cut carnation flowers (Tico Viola) responded differently to SNP, and the effects were dose-dependent  cut flowers, the ethylene production rate was relatively low at day 3; however, it began to periodically increase and tended to reach a peak by day 9  on days 9 and 8 after the treatment, followed by ethylene production in SNP treatments at 15 and 1 mg L−1, respectively. In case of other SNP treatments (5 and 10 mg L−1), the ethylene production rate increased markedly on day 9 and further increments were noticed until day 12 (data not shown); however, the ethylene production rate in 10 mg L−1 SNP treatment seemed to increase until day 15, when the flowers finally showed petal in-rolling. Overall, the ethylene production rate was likely to be associated with the symptoms of flower senescence such as petal in-rolling and wilting.Generally, the vase life of cut flowers was associated with their ethylene production during the vase life period. For the control and SNP-treated , and decreased thereafter  genes were determined on day 9. The expression profiles of the ethylene biosynthesis genes are presented in Fig. −1 SNP treatment, followed by other SNP treatments , whereas the lowest expression levels were noted in the 10 mg L−1 SNP treatment. These findings support the conclusion that ethylene production was strongly associated with ethylene-related gene expression.To understand the relationship between ethylene production and the expression level of ethylene biosynthesis or receptor genes, the transcript levels of the ethylene biosynthesis . Additionally, the antioxidant activity profiles  for both the control and the SNP treatments were also similar to those of ROS-scavenging activity . When ACC was added to the vase solution (distilled water), flowers showed obvious signs of senescence  by day 7  was not significantly different Fig. . HoweverDcACO1 and DcACS1 genes were significantly higher in ACC treatment than in controls, whose transcript levels were, in turn, higher than those in the ACC + SNP treatment  expressed in flowers treated with ACC + SNP was also higher than in both controls and flowers treated with ACC, whereas the transcript levels were higher in controls than in ACC treatment  and antioxidant activity  in the flowers treated with ACC, to examine whether the activity was reduced earlier than in the controls. Results shown in Fig. −1) in different carnation cultivars, i.e., Venus, Tico Tico, and Shino Lily. As shown in Fig. −1 SNP was used, the vase life of all cultivars improved. The vase life was 15 days for Venus and 12 days for Tico Tico and Shino Lily, resulting in an improvement of 4 days for the cultivars Tico Tico and Shino Lily, and 6 days for Venus, compared with the respective controls  were also significantly higher in controls than in SNP treatments for all the cultivars  were also higher in the SNP treatments than in the controls  significantly promoted the vase life further. However, concentrations higher than 10 mg L−1 appeared to reduce the vase life, resulting in a negative effect on vase life length at 20 mg L−1 SNP. Dwivedi et al. [Here, we found that SNP concentrations markedly affected the vase life of cut carnation flowers; SNP at 1 mg Li et al.  and Zengi et al.  also repi et al.  and Liaoi et al.  also rep−1 SNP treatment was associated with low ethylene production, because the level detected for SNP 10 mg L−1 was the lowest; such suppression of ethylene production by SNP was also observed in previous studies [−1 SNP treatment could be due to the absence of SNP that could suppress ethylene production (in the case of controls) or the presence of high SNP concentration that could be toxic to the flowers (in the 20 mg L−1 SNP treatment), resulting in the shortest vase life among the treatments. Moreover, it was found that a long vase life was associated with RFW as well. Although the maximum RFW was seen at day 3 for all treatments including the control, the reduction of RFW was achieved only in 10 mg L−1 SNP treatment on days 6 and 9, compared with the other treatments. The positive effect of SNP on RFW has been shown in roses and carnations [Previous studies have reported that the onset of petal senescence (in-rolling) coincided with the peak of ethylene production in carnations , 23. Acc studies , 24. Higrnations , 15. Seyrnations  revealedrnations  also reprnations .DcACS1 and DcACO1, and the expression patterns of the ethylene biosynthesis genes DcACS1 and DcACO1 mirrored the ethylene production rate, because the transcript levels of the detected genes were observed to be the lowest in 10 mg L−1 SNP treatment, followed by other treatments , whereas those in control and 20 mg L−1 SNP treatment were the highest. Satoh and Waki [DcACS1 were highly expressed in carnation flowers with a normal vase life. Tanase et al. [DcACS1 and DcACO1 in carnation flowers were high on days 5 and 6 after treatment, with an increase in the ethylene production. In addition, they further claimed that the differences in long vase life among different carnation cultivars were also associated with the transcript levels of the ethylene biosynthesis genes. Recently, Ichimura and Niki [Many studies have reported that NO could suppress ethylene production during the postharvest life of various cut flowers by inhibiting ethylene biosynthesis components such as ACC and ACO activity , 24, 26.and Waki  claimed e et al.  also claDcCPI) plays an important role in the regulation of carnation petal senescence by inhibiting the cysteine proteinase gene (DcCPase), which leads to the decomposition of cell components and cell death during petal senescence [DcCPI responded differently to the treatments; its transcript level was found to be the highest in 10 mg L−1 SNP treatment, followed by the other treatments (5 > 15 > 1 mg L−1), whereas the lowest transcript levels were noted in the control and 20 mg L−1 SNP. These results mirrored the vase life of the cut flower. In addition, this study also supported the finding by Tanase et al. [DcCPI acts as a suppressor of petal senescence in two carnation cultivars, the long-life cultivar MR and the ultra-long-life cultivar 532–6.Cysteine proteinase inhibitor gene  has been shown to shorten the vase life of flowers and to interrupt the positive effect of the SNP, which could be explained by the higher expression level of the ethylene biosynthesis genes, and senescence-related gene transcript levels. Moreover, the antioxidant activity detected in the ACC treatment distinctly decreased compared with the control, the activity of which was lower than that of the ACC + SNP treatment. Taken together, the data presented here provide experimental evidence of the close association between ACC and petal senescence in cut flowers; in addition, the results indicated a positive role of SNP against the ethylene precursor (ACC) in extending the vase life of cut carnation flowers.−1) on the vase life of different cultivars was also performed, using the same physiological parameters and molecular approaches, as used for Tico Viola. Here, we found that the application of SNP could increase the vase life of all the different cultivars tested via suppression of ethylene production and its related genes as well as the petal-senescence gene, and by maintaining antioxidant activity during flower development. The differences of results in response to SNP would be genotype-dependent. Therefore, the substantial confirmation of the roles of SNP presented here strongly suggests that SNP can be exploited as a novel agent for the improvement of vase life of different cut flowers.Confirmation of the effects of SNP treatment , an NO donor, could extend the vase life of the carnation Tico Viola by significantly suppressing ethylene production and maintaining antioxidant activity, compared with the control. Moreover, it could downregulate the expression of ethylene production-related genes and petal-senescence gene. SNP at 10 mg L


The understanding of the biological determinism of meat ultimate pH, which is strongly related to muscle glycogen content, is a key point for the control of muscle integrity and meat quality in poultry. In the present study, we took advantage of a unique model of two broiler lines divergently selected for the ultimate pH of the pectoralis major muscle (PM-pHu) in order to decipher the genetic control of this trait. Two complementary approaches were used: detection of selection signatures generated during the first five generations and genome-wide association study for PM-pHu and Sartorius muscle pHu (SART-pHu) at the sixth generation of selection.p-value below 1e-6), 4 were detected as soon as the first generation (G1) and were recovered at each of the four following ones (G2-G5). Another four corresponded to a later onset of selection as they were detected only at G5. In total, 33 SNPs, located in 24 QTL regions, were significantly associated with PM-pHu. For SART-pHu, we detected 18 SNPs located in 10 different regions. These results confirmed a polygenic determinism for these traits and highlighted two major QTL: one for PM-pHu on GGA1 (with a Bayes Factor (BF) of 300) and one for SART-pHu on GGA4 (with a BF of 257). Although selection signatures were enriched in QTL for PM-pHu, several QTL with strong effect haven’t yet responded to selection, suggesting that the divergence between lines might be further increased.Sixty-three genomic regions showed significant signatures of positive selection. Out of the 10 most significant regions  contains supplementary material, which is available to authorized users. In chicken as in pigs, muscle ultimate pH (pHu) is a major factor of variation of both meat quality and processing ability. Normal values of pHu of broiler breast meat are approximately 5.8 to 5.9. The further the pHu deviates from this value, the more quality defects occur. Meat with pHu values greater than 6.1 is classified as dark, firm, and dry (DFD)-like meat while meat with pHu values less than 5.7 is classified as acid meat which is often referred to as pale, soft, and exudative (PSE)-like meat in broiler . The neaIt is now well established that chicken breast meat ultimate pH has a moderate to high heritability, as shown by the heritability values of 0.3 to 0.5 found in different genetic lines –12. Howep-value < 0.0001). This means that 61% of the breast meat in the pHu- line could be classified as acid or PSE-like (PM-pHu < 5.7) and 63% of breast meat in the pHu + line as DFD (PM-pHu > 6.1). Significant changes  were also observed in the thigh since the pHu of the Sartorius muscle was on average 6.20 and 6.53 in the pHu- and pHu + lines, respectively. As expected, by comparison to the PM muscle of the pHu + line, the PM muscle of the pHu– line was characterized by a higher Glycolytic potential [In the present study, we took advantage of a unique resource population constituted from two lines divergently selected for pHu, to identify the genomic regions underlying the genetic variability of this trait and to search for candidate genes. As described in Alnahhas et al. , the twootential . In ordeotential .Fig. 1Php-value measuring how likely it is to reach the level of genetic differentiation observed at this locus, under a pure drift model with the inbreeding values estimated previously. Third, based on these p-values, they detect outlier loci using a standard statistical procedure controlling the false discovery rate (FDR). Using this approach, even strong levels of genetic differentiation may be considered non-significant, depending on the level of genetic drift estimated genome-wide. For the second step , genetic differentiation at each locus is evaluated based on single SNP allele frequencies for FLK, and on local haplotype frequencies for hapFLK. Haplotype data is expected to provide higher detection power, especially with medium density chip data [We looked for genomic signatures of positive selection in the two lines using the FLK  and hapFhip data . More deIn our analysis, the estimated inbreeding coefficient F from generation 0 to generation 5 was 0.08 in the pHu- line and 0.068 in the pHu + line. In a Wright-Fisher model with N diploïd individuals, F is equal to (1-(1–1/2 N)^g), where g is the number of generations. Based on this formula with g = 5, these estimations correspond to an effective population size of 30 animals in the pHu- line and 35 animals in the phu + line, which is in good agreement with the number of reproducers used at every generation of the experiment see the .p-values below 1e-14. Applying FLK to the different generations at a FDR of 5% . By steering the catalytic subunit (PP1c) to glycogen, it promotes dephosphorylation of glycogen synthase (GS) and glycogen phosphorylase (GP) and thereby glycogen synthesis [PPP1R3A exhibit a 90% reduction in muscle glycogen [PPP1R3A was significantly over-expressed in the pHu- line [SLC37A4, involved in human GSD was present in the top list of the regions of interest  characterized by the accumulation of glycogen in some organs and tissues [RHOC and LOC107052650) were part of a set of 20 biomarkers evidenced as pertinent predictors of the variation of PM-pHu between the two lines [VTI1B, SLC2A1 and CAV3 which were within signatures of selection or QTL regions detected at a lower level of significance in the current study . SLC2A1, also called GLUT1, is coding for Glucose transporter-1, which is considered to play a key role in maintaining basal glucose transport in most chicken cell types as in mammals [GAA (located within the QTL PM-14a) coding for lysosomal alpha-glucosidase which is essential for the degradation of glycogen to glucose in lysosomes and responsible for GSD type II or Pompe disease. It is also the case for PHKA1 located ~ 20 kb away from the most significant SNP (BF = 257) we identified for SART-pHu on GGA4 (QTL SART-4a). The PHKA1 gene encodes the muscle alpha regulatory subunit of phosphorylase kinase which catalyzes phosphorylation of glycogen phosphorylase and thus promotes glycogen degradation. Mutations in PHKA1 cause GSD type VIII which is usually a mild myopathy with slight elevation of plasma creatine kinase concentration and muscle glycogen content.The study of the two divergent lines allowed a better understanding of the genetic architecture of chicken meat ultimate pH and revealed several outstanding regions with highly significant signatures of selection and/or direct association with PM-pHu and SART-pHu phenotypes. In order to identify putative candidate genes, these regions were examined in light of the results of the transcriptomic analysis performed on samples from the pectoralis major muscle of the pHu + and pHu- lines  and of tynthesis . Knockouglycogen . A few yglycogen . PPP1R3AHu- line  which isHu- line . Another tissues . As indiwo lines . This se mammals . Further mammals  were preThe divergent selection conducted on PM-pHu allowed the creation of a unique resource population. This population, as shown in the present study, is highly useful to understand the genetic control of not only the selection criterion but more generally that of muscle glycogen storage and metabolism in chicken. Sixty-three genomic regions showed significant signatures of positive selection and were enriched in QTL for PM-pHu. Still, several QTL with strong effect have not yet responded to selection, suggesting that the divergence between lines might be further increased. A few regions of major interest, with significant selection signatures all along the selection process and/or highly associated with PM-pHu or SART-pHu phenotypes, were evidenced for the first time in chicken. They suggested several candidate genes, directly involved in glycogen synthesis and degradation or in the balance between these two systems, which necessitate further investigation in chicken.This study was conducted on birds originating from two lines divergently selected for PM-pHu according to a breeding scheme described in Alnahhas et al. . In thisn = 558) and sires used for the scan of positive selection (n = 288) were genotyped by the Labogena Laboratory  using the Illumina chicken SNP 60 K Beadchip containing 57,636 SNPs. After filtering SNPs for their minor allele frequency (higher than 0.05) and their call rate (higher than 0.95), 40,590 SNPs located on 28 autosomes were retained for QTL detection on a total of 558 birds which had all a call rate higher than 0.95. When applying the same criteria, a total of 42,026 SNPs and 288 birds were considered for the detection of regions under selection.Birds used for QTL mapping (https://forge-dga.jouy.inra.fr/projects/hapflk). These methods consider genotype data from multiple populations and detect regions where genetic differentiation between these populations is not consistent with a neutral evolution model. First, based on the matrix of observed genome-wide pairwise Reynold’s distances between populations, they estimate a population tree, using a Neighbour-Joining algorithm, and summarize this tree by a kinship matrix quantifying the amount of drift accumulated in each population since their divergence from a common ancestral population. Then, they scan the genome for loci where genetic differentiation between populations is significantly larger than expected under this pure drift model. Similar to the classical Fst, the statistic measuring genetic differentiation for one allele is a sum of the terms (pi – p)^2, where pi is the frequency of this allele in population i and p the estimated frequency of this allele in the ancestral population. The difference with Fst is that the terms of this sum (i.e. the populations) are weighted using the entries of the kinship matrix estimated previously, in order to account for differences of population size and for shared ancestry between populations. In the case of FLK, the statistic is computed for one of the two alleles of each SNP (the choice is arbitrary and has no influence on the results). In the case of hapFLK, local haplotypes are estimated (see below) and genetic differentiation is summed over all these haplotypes.We looked for genomic signatures of positive selection using the FLK  and hapFWe applied these two tests for all generations of the selection experiment. At each generation (G1 to G5), 3 populations were compared: the pHu + line, the pHu- line, and the G0 population. G0 was defined as outgroup when building the population tree and computing the kinship matrix (option –outgroup). Data from G0 were also used when computing genetic differentiation at each locus (option –keep-outgroup). As a result, estimated allele frequencies in the ancestral population were always very close to the allele frequencies observed in G0 (p0), so the allele frequency differences computed within FLK (the terms (pi-p)^2 mentioned above) were almost equal to the allele frequency differences (pi-p0)^2 observed along each selected line.p-values at each SNP were computed from this estimated distribution. To identify selected regions, we used a q-value threshold of 0.05, therefore controlling false discovery rate at the 5% level [HapFLK makes use of the local clustering approach of Scheet and Stephens  to estim5% level .p-values were directly obtained from the hapFLK software . As described above, we used an FDR threshold of 5% to identify significant SNPs. In contrast to hapFLK values, which are relatively smooth along the genome, FLK values, as most single SNP statistics, are highly variable along the genome. Consequently, defining significant regions is more difficult in this case. However, in our analysis, only few SNPs had significant FLK values (1 in G4 and 57 in G5) and several of them were located in regions already detected by hapFLK. Based on significant FLK values that did not overlap with a hapFLK region, we defined 10 new regions under selection including either one significant SNP or several significant SNPs distant by less than 1 Mb.For FLK analyses, For all regions detected under selection using FLK or hapFLK, we performed another hapFLK analysis combining animals from all generations, which allowed studying the evolution of haplotype cluster frequencies along generations . For each region, we estimated haplotype clusters using 3 EM runs, and plotted cluster frequencies for each of these runs using the R script hapflk-clusterplot.R, which is provided on hapFLK website. These 3 runs always led to very similar interpretations concerning the selection history in the region, so we only presented one of them.p-value at each SNP ^2 was greater than (pf-p0)^2. Two simulation strategies were implemented. In the first strategy, we considered a standard Wright-Fisher model with N haploïd individuals: if one allele has x copies out of N individuals at generation g, the number of copies at generation (g + 1) is a binomial variable with parameters . In the second strategy, we accounted for the number of reproducing males Nm(g) and females Nf (g) at each generation g: if one allele has xm copies among the 2*Nm(g) male alleles and xf copies among the 2*Nf(g) female alleles at generation g, the number of copies among the males at generation (g + 1) is Xmm + Xmf, where Xmm is a binomial variable with parameters (Nm(g + 1), xm/2*Nm(g)) and Xmf is a binomial variable with parameters (Nm(g + 1), xf/2*Nf(g)). Similarly, the number of copies among the females at generation (g + 1) is Xfm + Xff, where Xfm is a binomial variable with parameters (Nf(g + 1), xm/2*Nm(g)) and Xff is a binomial variable with parameters (Nf(g + 1), xf/2*Nf(g)). For each simulation strategy, a first set of simulations was performed using parameters mimicking the pHu- line, and a second set was performed using parameters mimicking the pHu + line. For strategy 1, the number of haploïd individuals was the haploïd effective population size estimated by FLK. For strategy 2, the number of male and female reproducers at every generation was the same as in the divergent selection experiment. The final yj is the phenotype for an individual j corrected for the fixed effects of sex and hatch, μ the overall mean, M the number of markers analysed, xij the genotype score  of SNP i for individual j, ai the additive effect of SNP i, and ej the random residual for individual j with ej ∼ N. I is an identity matrix and σ2e is the residual variance. All unknown parameters were assigned prior distributions and sampled with a Monte Carlo Markov chain (MCMC) using Gibbs sampling. The MCMC was run for 400,000 iterations, with a burn-in of 80,000 iterations and thin interval of 400. The Bayes Cπ analysis allows introducing in the model all the SNPs at the same time but makes the assumption that only a small proportion of them has a significant effect on the trait. Thus, the prior parameter used for ai is a mixture distribution with ai ∼ N  if the SNP is in the model (with a probability π) and ai = 0 if the SNP is not in the model (with a probability 1-π). σ2a is the common marker effect variance and the hyper parameter (1-π) the prior probability that the effect of marker i is 0. For π the prior distribution was set at a Beta distribution with parameters α = 0.5 × 104 and β = 99.5 × 104, meaning that π was almost fixed at 0.005. Variances σ2a and σ2e were assigned inverted chi-squared distributions with v = 4.2 degrees of freedom and a scale parameter 2a or σ2e. The statistics used to detect significant SNP was the Bayes Factor (BF), which corresponds to the increase from prior to posterior probabilities of the SNP being “in” the model [QTL detection on birds from generation 6 was performed by the multi-marker Bayes Cπ analysis  implemenhe model . In thishe model . We defiBy including all the SNPs simultaneously in the model while assuming that only a proportion of those (π) has a significant effect, Bayes Cπ ensures that strong BF can only be reached at SNPs where the association between genotypes and traits clearly exceeds that expected from population structure and cannot result from drift alone. BF statistics were also calculated with a model including the fixed effect of the line to test whether the SNPs contributing to the variability of PM-pHu or SART-pHu in the whole population constituted from the two divergent lines also contributed to the intra-line variability.Computation of linkage disequilibriumhttps://www.cog-genomics.org/plink2). This computation was performed using genotyped animals from generation 0. For a given category of regions  obtained along the genome when applying FLK to generation 1 (yellow), 2 (green), 3 (blue), 4 (red) or 5 (black) of the selection experiment. Horizontal dotted lines indicate the 5% FDR threshold for each of these generations. Vertical black lines correspond to a change of chromosome, and the numbers below these lines provide the length of the finishing chromosome. (TIFF 1599 kb)Additional file 2:Figure S2. Evolution of haplotype cluster frequencies in region hapFLK-1c . A: each panel corresponds to a population, from G0 (top) to G5plus (bottom). For one given genomic position (on the x-axis), each color band corresponds to one haplotype cluster, and the height of this band gives the cluster frequency. The selection scenario described in the text is based on cluster frequencies at the position of the strongest hapFLK signal, which is indicated by the vertical black line. B: Evolution of the dark red, red and green cluster frequencies along generations. As discussed in the text, these clusters are the ones showing the strongest evidence of selection at this locus. (PNG 1614 kb)Additional file 3:Figure S3. Evolution of haplotype cluster frequencies in region hapFLK-2c . A: each panel corresponds to a population, from G0 (top) to G5plus (bottom). For one given genomic position (on the x-axis), each color band corresponds to one haplotype cluster, and the height of this band gives the cluster frequency. The selection scenario described in the text is based on cluster frequencies at the position of the strongest hapFLK signal, which is indicated by the vertical black line. B: Evolution of the light blue and light green cluster frequencies along generations. As discussed in the text, these clusters are the ones showing the strongest evidence of selection at this locus. (PNG 1608 kb)Additional file 4:Figure S4. Manhattan plot of the BF factor testing the association between SNP and ultimate pH of the pectoralis major (breast) muscle (PM-pHu). Bayes Factor (BF) comprised between 20  and 150  indicates strong evidence of QTL and BF higher than 150 indicates very strong evidence of QTL. (PDF 198 kb)Additional file 5:Figure S5. Manhattan plot of the BF factor testing the association between SNP and ultimate pH of the sartorius (thigh) muscle (SART-pHu). Bayes Factor (BF) comprised between 20  and 150  indicates strong evidence of QTL and BF higher than 150 indicates very strong evidence of QTL. (PDF 198 kb)Additional file 6:Table S1. Comparison of p-values  for different tests rejecting neutral evolution, for the most significant SNP of the 10 regions listed in Table Additional file 7:Table S2. Differential expressed genes within the most significant signatures of selection and QTL regions*. (DOCX 16 kb)


It remains challenging to predict regulatory variants in particular tissues or cell types due to highly context-specific gene regulation. By connecting large-scale epigenomic profiles to expression quantitative trait loci (eQTLs) in a wide range of human tissues/cell types, we identify critical chromatin features that predict variant regulatory potential. We present cepip, a joint likelihood framework, for estimating a variant’s regulatory probability in a context-dependent manner. Our method exhibits significant GWAS signal enrichment and is superior to existing cell type-specific methods. Furthermore, using phenotypically relevant epigenomes to weight the GWAS single-nucleotide polymorphisms, we improve the statistical power of the gene-based association test.The online version of this article (doi:10.1186/s13059-017-1177-3) contains supplementary material, which is available to authorized users. P values between 1 × 10−5 and 5 × 10−8), localize to non-coding genomic regions with many gene regulatory signals [Complex traits are usually affected by a large number of genetic factors, jointly contributing to the susceptibility of the disease and the phenotype , 2. Rece signals , suggest signals . This is signals . TherefoGenes are regulated in a highly context-specific manner. Both genetic and epigenetic gene regulations are tissue/cell type-specific and depend on chromatin states and interactions , 15. Stuhttp://jjwanglab.org/cepip or https://github.com/mulin0424/cepip under the GNU General Public License v3.We previously developed an ensemble model to integrate predicted scores from CADD , FunSeq cis-eQTLs from 11 gene expression studies on seven tissues/cell types [To illustrate distinct regulatory effects of functional variants in different tissues/cell types, we used a uniformly processed dataset of fine-mapped ll types . The finFor each of the 11 eQTL studies, we trained the logit model to estimate the probability that the 36 chromatin features were associated with eQTLs , which resulted in 11 models. To identify the most informative feature set, we used a backward stepwise regression based on the Akaike Information Criterion (AIC) [These highly reoccurring chromatin signals included some well-established chromatin marks such as enhancer marks H3K4me1), active gene bodies (H3K36me3), active promoters, and enhancers or transcribed regions (DHS and H3K79me2). Additionally, H3K9me3 Hit/Intensity and H3K4me3 Intensity were selected features, although H3K9me3 showed inconsistent contributions among different tissue/cell type-specific models. Another repressive mark H3K27me3 Hit was also a selected feature in the random allele frequency-matched sampling models, indicating some eQTLs could modulate gene silencing. Other frequent features, such as H3K36me3 Intensity and H3K79me2 Centrality in the random allele frequency-matched sampling models, could be used to distinguish two variants located within the same ChIP-seq peak . To investigate whether additional histone modification features could be selected in the absence of DHS-related features, we retrained our models without DHS-related features. This resulted in H3K27ac Hit appearing in more than half of the models, which implies that it is a critical chromatin feature. In addition, occurrences of H3K4me2 Intensity and H3K4me3 Intensity increased in all cell type-specific models   Fig. . This shr = 0.197, Additional file cis-eQTL datasets of relevant tissues/cells  and a rheumatoid arthritis (RA) eQTL dataset [To further enhance the predictive performance, we combined our context-dependent model with a previously reported composite model , which c dataset .cis-eQTLs, performing better than fitCons and deltaSVM   as the percentage of top-ranked variants increased by using all variants from the 1000 Genomes Project. Our combined model can detect more eQTLs in the three independent eQTL datasets than fitCons and deltaSVM among the 10% top-ranked variants. For example, our model and fitCons achieved over 20% coverage in the Geuvadis eQTLs for the 10% top-ranked variants, whereas deltaSVM only achieved 10% coverage Fig. . Comparely) Fig. . Our comly) Fig. .P value    to demonP values calculated from association studies in a Caucasian population. Thus, it is more challenging to pinpoint true functional SNPs in the Caucasian population with strict LD compared with the African population with its relatively unconfined LD , we removed all known RA-associated SNPs and still found similar patterns  located in a genomic region enriched with Mo-CD14+ specific chromatin features  [P values as potential novel candidates for further validation  . We alsoP values < 1 × 10–5) by using the effective numbers of independent markers [To test whether the context-dependent prioritization was affected by LD, we recalculated the ratio of SNPs with sub-genome-wide associations  or by further lowering of the originally significant P values to more significant levels  in the larger sample size study , Stranger_LCL (STL), Harvard_cerebellum (HCE), Harvard_prefrontal_cortex (HPC), Harvard_visual_cortex (HVC), GenCord_fibroblast (GCF), GenCord_LCL (GCL), GenCord_tcell (GCT), UChicago_liver (CLI), Merck_liver (MLI), and Myers_brain (MBR). We downloaded the eQTL SNPs of the most highly associated cis-linked SNP within an LD block. To acquire reliable fine-mapped eQTL SNPs, we applied a strict cutoff of 10% false discovery rate (FDR) for each tissue/cell type. These eQTL SNPs were treated as the candidate regulatory variants. We constructed corresponding background SNPs using two strategies: (1) random allele frequency-matched sampling (less than 0.05 deviations to each eQTL SNP using 1000 Genomes project EUR population) of SNPs around the gene’s nearest TSS genome-wide (within 10 kb); and (2) a strict matching scheme with (i) no overlap with any SNPs within high LD of each fine-mapped eQTL SNP (r2 > 0.8 in 1000 Genomes project EUR population), (ii) matched allele frequency, (iii) matched TSS distance to each eQTL SNP (up/downstream 1000 bp deviations), and (iv) matched GC content to each eQTL SNP locus (up/downstream 50 bp DNA sequence).We used the uniformly processed n et al. . A multihttp://www.gtexportal.org/), which contains Matrix eQTL [We downloaded significant SNP-gene associations from GTEx V4 from the GTEx portal , including primary tissues, diverse blood cells, and embryonic stem cells, as well as ENCODE cell lines. For each tissue/cell line, we acquired narrow peaks using DNase-seq and ChIP-seq for 11 histone modification marks, including H2AFZ, H3K27ac, H3K27me3, H3K36me3, H3K4me1, H3K4me2, H3K4me3, H3K79me2, H3K9ac, H3K9me3, and H4K20me1 from the uniformly reprocessed consolidated epigenome, which can capture relatively equalized signal strength across tissues/cell types. We used imputed peak calls for missing epigenomes [We incorporated epigenomic data of 127 human tissues/cell lines from Roadmap Epigenomics Mapping repositories (igenomes .The 11 eQTL tissues/cell lines and 13 GTEx eQTL tissues were mapped onto the 127 tissues/cell types from Roadmap Epigenomics Project (including 16 ENCODE cell lines) according to their best matched cell type or tissue origin. For eQTL SNPs in each tissue/cell line, we defined three types of feature for the DNase I hypersensitive site and the 11 histone marks using the narrow peak calling results: (1) the hit of peak (Hit) feature; (2) the peak intensity (Intensity) feature; and (3) the distance to summit  feature. Overall, we extracted 36 cell type-specific genomic and epigenomic features from all eQTL SNPs and corresponding background SNPs.We trained logit model for eQTL SNPs and control SNPs from each of the 11 eQTL studies and calculated the regulatory probability of each investigated SNP. We termed this probability the “regulatory potential” given by:X (36 features) from the logistic regression of eQTL SNPs of a specific tissue/cell type and corresponding control SNPs. To identify the most informative features, we performed a model selection using backward stepwise selection based on the AIC [where α is the intercept and β is the vector of coefficients of  the AIC . We selehttp://www.broadinstitute.org/pubs/finemapping/), which contained 8741 fine-mapped SNPs that passed the PICS probability cutoff (>0.0275). We extracted our selected chromatin features for each candidate regulatory SNPs in the 127 epigenomes. Next, we applied our generalized context-dependent model to predict the regulatory potential for each fine-mapped SNP k in the corresponding disease category i using each of the consolidated epigenomes of 127 tissues/cell types j, defined as Pk,i,j. For each disease and cell/tissue type combination, we calculated the mean prediction score for all causal SNPs that were associated with the disease, defined as the meani,j(P). We normalized the meani,j(P) across diseases/traits (centered by 1) and used hierarchical clustering to group the tissues/cell types according to the normalized value. We estimated the most relevant tissue/cell type for each of the 38 immune and non-immune diseases/traits by selecting the largest normalized meani,j(P) among the 127 tissues/cell types.We retrieved candidate causal SNPs from a GWAS fine-mapping study of 38 immune and non-immune diseases . The Mann–Whitney We used the ensemble model  and our S is the observed set of prediction scores and the probability that a causal or a neutral SNP obtains a score si could be calculated through the empirical distribution of training dataset. The flat prior probability π = 0.5 for the causal probability of each variant.where S) and the chromatin features of one defined cell (X) for an observed SNP, one can estimate the posterior probability of the SNP being a regulatory variant. We termed this joint likelihood the “combined probability” as:Given a set of functional prediction scores (Since test scores and chromatin signatures are two independent measurements:where http://www.ebi.ac.uk/Tools/geuvadis-das/). The cis-eQTLs from a meta-analysis study of whole blood samples in ~5300 individuals were downloaded from the Blood eQTL Browser (http://www.genenetwork.nl/bloodeqtlbrowser/) [cis-eQTLs with FDR < 5% was retrieved from a published paper [Geuvadis eQTLs of lymphoblastoid cells for 373 European individuals were downloaded from the EMBL-EBI Geuvadis Data Browser (rowser/) . A datased paper . For eacThe RA SNP summary statistics from two GWASs by Okada et al.  and by SP values in each sampling and compared the combined statistics among all samples. To reduce the computational burden, we removed SNPs with GWAS P values > 0.001. The Q-Q plot of the top 5% SNPs was constructed to compare the two non-cancer blood cell lines (Mo-CD14+ and GM12878) with other cell lines against the permutated GWAS signals.The GWAS signal enrichment analysis was performed using RA-associated SNPs from Okada et al. . SNPs inP values < 5 × 10–8 from the meta-analysis dataset [Known RA-associated SNPs were defined according to SNPs reported in GWAS Catalog  and SNPs dataset . These SP values < 1 × 10–5) in the top 1% SNPs. For each set, we calculated the effective number (Me) of independent markers chromosome by chromosome. The ratio for each chromosome was obtained as:We estimated the effective number of independent markers in each SNP set using The Genetic Type I error calculator (GEC) , which in SNPs, ith sorted SNP by its P value isFor each RA GWAS SNP from Okada et al. and Stahl et al., we first calculated the context-dependent combined probability using epigenomes of the 13 ENCODE cell lines. The combined probability of SNPs was rescaled to keep the ratio of highest value to the lowest one as ten for each cell line, so that a “wrong” weight would not substantially affect the power . The resme(j) is the effective number of independent P values among the top j SNPs, me(0) = 0, and c is defined such that the weights sum to me:where P value was then given by:The weighted gene-based P value decreased from a non-significant level using NW_SNP to a significant level using W_SNP (significant threshold of 1 × 10–5); or (2) an already detected gene with a P value above a significant level using NW_SNP was further decreased by over 0.8-fold when using W_SNP. A gene detected as significant using NW_SNP but becoming less significant when using W_SNP was defined as a worsened gene.For each cell type, we performed the gene-based association test on both the weighted SNPs (W_SNP) and non-weighted SNPs (NW_SNP), respectively. Next, we compared the W_SNP with NW_SNP for each context. For each gene, we defined an improved gene as: (1) a gene where the http://software.broadinstitute.org/gsea) to evaluate the pathway enrichment. The immune system-related gene sets were retrieved from Reactome [Genes from each context-specific prioritization were inputted into the GSEA analysis tool (Reactome .