Linum usitatissimum L.) is a significant fibre and oilseed crop. Current flax molecular markers, including isozymes, RAPDs, AFLPs and SSRs are of limited use in the construction of high density linkage maps and for association mapping applications due to factors such as low reproducibility, intense labour requirements and/or limited numbers. We report here on the use of a reduced representation library strategy combined with next generation Illumina sequencing for rapid and large scale discovery of SNPs in eight flax genotypes. SNP discovery was performed through in silico analysis of the sequencing data against the whole genome shotgun sequence assembly of flax genotype CDC Bethune. Genotyping-by-sequencing of an F6-derived recombinant inbred line population provided validation of the SNPs.Flax . Depending on the relatedness of the genotypes and the number and length of the reads, between 78% and 93% of the reads mapped onto the CDC Bethune whole genome shotgun sequence assembly. A total of 55,465 SNPs were discovered with the largest number of SNPs belonging to the genotypes with the highest mapping coverage percentage. Approximately 84% of the SNPs discovered were identified in a single genotype, 13% were shared between any two genotypes and the remaining 3% in three or more. Nearly a quarter of the SNPs were found in genic regions. A total of 4,706 out of 4,863 SNPs discovered in Macbeth were validated using genotyping-by-sequencing of 96 FNext generation sequencing of reduced representation libraries was successfully implemented for genome-wide SNP discovery from flax. The genotyping-by-sequencing approach proved to be efficient for validation. The SNP resources generated in this work will assist in generating high density maps of flax and facilitate QTL discovery, marker-assisted selection, phylogenetic analyses, association mapping and anchoring of the whole genome shotgun sequence. Linum usitatissimum L.) is a self-pollinated annual species (2n = 2x = 30) belonging to the Linaceae family. It has been utilised by mankind for some 30,000 years  [http://publications.gc.ca/collections/collection_2011/statcan/22-007-X/22-007-2011002-eng.pdf).Flax (hic era) , was domFlax oil is highly sought after in the fabrication of biodegradable products such as paint, linoleum and varnish, while its oil-free meal is used as livestock feed. Recently, linseed has gained importance as nutraceutical primarily because of its α-linolenic acid (ALA) and lignan content. The ALA component of flax oil (omega-3 fatty acid) improves bone and cardio-vascular health  while liDrosophila melanogaster[Arabidopsis thaliana[Arabidopsis[To assess and capitalize upon the genetic variability in flax, genomic resources are needed. The flax genome assembled from short shotgun reads  as well anogaster, wheat [anogaster, eggplananogaster, rice [2anogaster-28, Arab thaliana,30, barl thaliana-33, waln thaliana, lupin [ thaliana, globe a thaliana, rapesee thaliana, perenni thaliana and maiz thaliana to name abidopsis but the abidopsis,42. The abidopsis. Its repabidopsis making iGenomic complexity can be reduced using restriction enzymes , high-CoTM[TM[SNP genotyping of one to several thousands of SNPs can be performed simultaneously using various chemistries such as Taqman® probes ,54, InvaTM, SNaPshoTM[TM, GoldenGTM[TM and InfiTM[TM. The higThe objective of the current study was to discover and validate SNPs in flax using a combined NGS of RRLs and GBS strategy with the updated annotation based genome-wide SNP discovery pipeline (AGSNP) ,61. The Flax genotypes CDC Bethune, Macbeth, SP2047 and UGG5-5 were selected because they are parents of mapping populations. Atlas, Double Low, G-1186/94 and Crepitam Tabor were chosen from a core collection of flax lines because they were genetically diverse according to our previous assessment based on several hundred SSR markers . The broAll sequencing was performed on the Illumina platform. Advances in sequencing technology through the duration of the project led to reads ranging in length from 50 to 100 bp, thus resulting in variation in the number of paired end tag (PET) reads and in sequence coverage among the eight genotypes  between MCP and sequence coverage was observed .The distribution of the mapping coverage percentage (MCP) and the mapped read depth (MRD) in bins of 0.5 Mbp over the entire length of the concatenated reference sequence is shown as heat maps in Figures -5 per nucleotide of the reference sequence  was obtained using BWA and SAMtools. The alignment file generated by BWA was used as input for SNP discovery using SAMtools . A totalhttp://www.phytozome.net/flax) for the reference sequence, we found that a quarter of the SNPs were present in genic regions , of which 4,515 (8%) were present in the coding regions  followed in decreasing order by Atlas, G-1186/94, Double Low, Macbeth, UGG5-5 and SP2047 Table . The SNPregions 1,367, of  followedClassification of SNPs based on base changes included 36,156 65.2%) transitions and 19,309 (34.8%) transversions with a transition to transversion ratio of 1.87  that did not show segregation constituted non-validated SNPs. SNP locations with reads from the RIL population that segregated for the SNPs previously identified between CDC Bethune and Macbeth constituted validated SNPs. A total of 4,706 SNPs (96.8%) were thus validated and 157 SNPs remained non-validated.The current study was undertaken to discover SNPs using flax genotypes that were parents of mapping populations and/or of diverse genetic backgrounds. The Illumina platform was chosen to sequence the RRLs because of its throughput, relatively low cost, indexing and PET capabilities.The mapping of sequence reads was performed with Bowtie and BWA using their default settings on the same Linux based servers. Wang and colleagues suggested using Bowtie or BWA for fast and efficient alignment of Illumina short reads . The curAegilops tauschii (genome size of 4.02 Gbp). Half a million SNPs with a validation rate of 85.9% were discovered [http://avena.pw.usda.gov/wheatD/agsnp.shtml.The AGSNP pipeline was initially designed for large-scale genome-wide SNP discovery in large and complex genomes using next generation sequences of two homozygous lines . This piscovered . In the MseI. To confirm the genome-wide distribution of the SNPs discovered in our study, we estimated the SNPs distribution in 0.5 Mbp bins and found that the SNPs were well distributed throughout the concatenated reference sequence assembly  of the SNPs could not be validated and were considered false-positive. The false-positive SNPs from non-repetitive regions could result from gene family or duplicate genes which can cause mis-mapping of reads. Validation failure could also be due to errors of the WGS sequence assembly or sequencing errors of the Macbeth reads. By using next generation sequencing, the current study discovered a significant number of flax SNPs with a high validation rate achieved through GBS, which was revealed to be an effective method for large scale SNP validation when used in conjunction with a segregating population. The RRL coupled with GBS approach has been effective in maize, a large genome species of 2.3 Gbp, and barley where SNP validation rates of 91% and 99% were achieved, respectively .vegetative to generative transition 1 (vgt1) in maize that are crucial for flowering [The current study estimated the rate of SNP discovery in flax to be 0.17 SNPs per Kbp across the eight genotypes sequenced. This is lower than potato (11.5 per Kbp) , maize  with a validation rate of 96.8% using GBS of a segregating population, proving this strategy to be powerful for validation purposes. These SNPs will be applied in genetic mapping, anchoring of genetic maps with WGS sequence assembly, marker-assisted selection, association mapping and phylogenetic analysis and, as such, they will constitute an important genomic resource for flax studies.TM 2047) is a yellow-seeded solin line characterized by low linolenic acid (LIN) content (2-4%) [Eight flax genotypes namely CDC Bethune, Macbeth, SP2047, UGG5-5, Atlas, Double Low, G-1186/94 and Crepitam Tabor were selected. CDC Bethune is a high yielding oilseed flax variety with intermediate oil content, oil quality, seed size and resistance to lodging, rust and fusarium wilt . Macbetht (2-4%)  whereas t (2-4%) . CrepitaMseI was selected because it yielded a large fraction of DNA smaller than the 500bp target size and it generated few high copy number bands in this region. A total of 20 μg of DNA of each genotype was restricted with MseI  according to the manufacturer’s instructions. The digested DNA was separated on a 1.6% agarose gel for 6 h at 100 volts and fragments in the 350-425 bp size range were excised. This size range was spanned by two high copy number bands that were not included in the excised fraction. Gel extraction of the DNA fraction was done with the QIAEX II gel extraction kit (Qiagen Sciences).The plants were grown in pots in a greenhouse with a 16 h light and 8 h dark cycle. DNA was extracted from 10 mg of lyophilised leaf tissue using the Qiagen DNeasy 96 plant kit  according to manufacturer’s instructions. A total of 8 4bp cutter restriction enzymes were evaluated for their ability to restrict flax genomic DNA. The enzyme RRL construction from the 350-425bp fraction and Illumina/Solexa sequencing  was perfhttp://www.phytozome.net/flax, NCBI genome project #68161) [The WGS sequence assembly of CDC Bethune  was calculated by dividing the total read length by the estimated size of the flax genome (~370 Mbp) ,44. Mapps, was considered to be an optimal cut-off value in the self-pollinating species Aegilops tauschii. We used the same criteria for flax, applying it to each genotype to remove potentially false-positive SNPs due to highly repetitive sequences or mis-mapping. s were estimated for each genotype based on the mapping results using a pipeline program in the AGSNP package. The SNP filtering criteria are listed in Table In the original AGSNP pipeline , the avehttp://www.phytozome.net/flax that was created using Augustus (version 2.5.5), a Hidden Markov Model-based gene finding program [To determine SNP location within genes, we used the gene prediction database available at  program  and Glim program .6-derived CDC Bethune/Macbeth RIL population. RRLs were constructed for each RIL and four RILs were indexed per lane of Illumina GAIIx. The 100bp PET reads were mapped onto the CDC Bethune reference sequence assembly using the modified AGSNP pipeline and the same parameters as described earlier for the eight genotypes  of CDC Bethune using Bowtie.Click here for fileTransition and transversion frequencies of the 55,465 SNPs.Click here for file


Arabidopsis thaliana can be a difficult and time-consuming process, specifically if the phenotype is subtle and scoring labour intensive. Here, we have re-sequenced the 120-Mb genome of a novel Arabidopsis clock mutant early bird (ebi-1) in Wassilewskija (Ws-2). We demonstrate the utility of sequencing a backcrossed line in limiting the number of SNPs considered. We identify a SNP in the gene AtNFXL-2 as the likely cause of the ebi-1 phenotype.Map based cloning in  Arabidopsis has a sequenced reference genome of 120 Mb from the Columbia (Col-0) accession  [Using current (TAIR 8) annotations  as a guiant:393] .ebi-1 and Ws-2, we used a simple PCR-based approach of CAPS and dCAPS analysis. PCR primers for CAPS/dCAPS analysis were designed using dCAPS finder 2.0 [ebi-1 and Ws-2, and the PCR products were digested and run on a 4% agarose gel and scored. The primers, restriction sites and product sizes are summarized in Additional file PRR7 and EBI were further validated by standard sequencing methods.To validate the SNPs between nder 2.0 . A stand® Green Supermix (Bio-Rad Laboratories). The efficiency of amplification was assessed relative to β-TUBULIN (βTUB) expression. The measurements were repeated at least two times with independent biological material. Expression levels were calculated relative to the reference gene using a comparative threshold cycle method [βTUB. Primers for βTUB have been published previously [EBI-specific primers were as follows: EBI-F, 5'-TGC GAG AAT ATG CTT AAT TGC-3'; EBI-R, 5'-CCA CAA CAT CAC AAG ACA AG-3'.Seedlings were grown under 12-h light/12-h dark cycles for 6 days. Seedlings were harvested directly into liquid nitrogen at 1 h after dawn and 1 h after dusk using a green safety light. The RNA was subsequently extracted using an RNeasy Plant Mini Kit . cDNA was synthesized from 1 μg of total RNA using the iScript™ cDNA synthesis kit . Real-time PCR was performed with a MyIQ™, ICycler or CFX96 Real-Time PCR Detection System , using iQ SYBRe method . The reseviously . The EBI2 mapping population was made between ebi-1 and Col-0. A set of approximately 20 individuals from this population, which had their ebi-1 phenotype confirmed in the F3, had recombination events in chromosome 5 and placed the ebi-1 mutation on the north arm of chromosome 5. This mapping population was increased and with two individuals we were further able to limit the mapping interval to between CIW18 and nga158.An FCAB2: Chlorophyll a/b-binding protein 2; CAPS: cleaved amplified polymorphic sequence; dCAPS: derived cleaved amplified polymorphic sequence; EBI: early bird mutant; EMS: ethyl methanesulfonate; LUC: luciferase; NASC: Nottingham Arabidopsis Stock Centre; SE: standard error; SNP: single nucleotide polymorphism; WT: wild type.ebi mutant was conceived by AH, MME and AJM and the SNP identification strategy by NH and AH, with AH responsible for overall co-ordination. SK and CA performed the SOLiD sequencing and LD performed the 454 sequencing. The characterization of ebi and alleles was performed by MJ, PG and MEE. The SNP validation was performed by LD. The bioinformatics was performed by KA with assistance from AH and NH, with all sequencing and sequence analysis overseen by NH. The paper was written by AH with assistance from NH and MEE. MEE was responsible for distribution of plant materials integral to the findings presented in this article and should be contacted directly. All authors read and approved the final manuscript.The screening and characterization of the Figure S1 - plant to plant variation in clock function is greater in Col-0 than in Ws-2. Seedlings were entrained under 12-h light/12-h dark cycles for 12 days, after which they were transferred to constant light where rhythms of leaf movement were assayed. Ws-2, filled squares; Col-0, empty squares. Period estimates for individual seedlings are plotted against their relative amplitude errors (R.A.E.).Click here for fileTable S1 - sequence tag counts available at various stages of the analysis, as reported by the different matching schema employed.Click here for fileTable S2 - SNP counts before and after filtering as reported by the various matching schema. a Unfiltered SNPs were all those reported by the Corona lite SNP detection pipeline. bFiltering involved retaining only those SNP loci where tag coverage exceeded 5× in both ebi-1 and Ws-2, the SOLiD score was 0.7 or greater, and SNPs were homozygous. (c) 'Schema screened' SNPs were those filtered SNPs reported by all five schema.Click here for fileTable S3 - dCAPS and CAPS marker design and use to validate SNP discovery. SNP marker denotes the chromosome position of the SNP based on the TAIR 8 Arabidopsis genome build. In the primer sequence the underlined base is the mismatched base in the primer sequence. ^Borderline SNP; aSNP in the clock gene PRR7; bSNP in At5g05660, EBI.Click here for fileTable S4 - EMS-induced SNPs on chromosome 5.Click here for fileTable S5 - EMS-induced SNPs on chromosome 1.Click here for fileebi-1Figure S2 - the presence or absence of EMS-induced mutations on chromosome 1 do not affect the phenotype of . Transgenic seedlings carrying the LUC reporter gene fused to the CAB2 promoter were entrained under 12-h light/12-h dark cycles for 7 days, after which luminescence was monitored in constant red light. WT, open squares; ebi-1, closed squares; ebi-1 with no EMS-induced SNP on chromosome 1, red triangles.Click here for fileTable S6 - analysis of temporal expression patterns of non-synonymous SNPs on chromosome 5 using Diurnal to fit temporal expression data to expression pattern models consistent with circadian regulation.Click here for filePRR7Figure S3 - identification of a SNP in . Top: A schematic representation of the PRR7 protein in Arabidopsis ecotype Columbia is shown in green. Gray boxes represent the two conserved region Receiver (REC) domain and CCT motif. The amino acids were aligned using the ClustalW program. Bottom: identical and similar amino acid residues are highlighted with black and gray backgrounds, respectively. The SNP leads to a change from arginine (R) to histidine (H) at position 329. The frame shows the residue in the Pseudo Response Regulator protein from Arabidopsis ecotype Columbia , Hordeum vulgare subsp. vulgare , Arabidopsis thaliana , Triticum aestivum , Oryza sativa Indica , Oryza sativa Indica , Lemna paucicostata , Lemna gibba , obtained from NCBI database, and Gossypium raimondii (TC272), Brassica napus (TC71410), Brassica napus (TC78134), Gossypium raimondii (TC82653), and Citrus clementina (TC8380) obtained from TGI databases.Click here for file


Meat quality traits, including pH, color, drip loss, moisture content, protein content and intramuscular fat content (IMF), marbling and firmness scores in the M. longissimus (LM) and M. semimembranosus (SM) muscles, were recorded on the two populations. In total, 127 chromosome-wide significant SNPs for these traits were identified. Among them, 11 SNPs reached genome-wise significance level, including 1 on SSC3 for pH, 1 on SSC3 and 3 on SSC15 for drip loss, 3 (unmapped) for color a*, and 2 for IMF each on SSC9 and SSCX. Except for 11 unmapped SNPs, 116 significant SNPs fell into 28 genomic regions of approximately 10 Mb or less. Most of these regions corresponded to previously reported QTL regions and spanned smaller intervals than before. The loci on SSC3 and SSC7 appeared to have pleiotropic effects on several related traits. Besides them, a few QTL signals were replicated between the two populations. Further, we identified thirteen new candidate genes for IMF, marbling and firmness, on the basis of their positions, functional annotations and reported expression patterns. The findings will contribute to further identification of the causal mutation underlying these QTLs and future marker-assisted selection in pigs.Thousands of QTLs for meat quality traits have been identified by linkage mapping studies, but most of them lack precise position or replication between populations, which hinder their application in pig breeding programs. To localize QTLs for meat quality traits to precise genomic regions, we performed a genome-wide association (GWA) study using the Illumina PorcineSNP60K Beadchip in two swine populations: 434 Sutai pigs and 933 F2 pigs from a White Duroc Meat quality is one of the most important economical traits in farm animals. It is decisive for the suitability of the meat for further processing and storage including retail display. The main attributes of interest are pH, color, firmness, water-holding capacity, fat content and composition, oxidative stability and uniformity RYR1, PRKAG3, IGF2) influencing meat quality have been applied in the pig industry, resulting in considerable improvement of meat quality in commercial pig herds Meat quality homogeneity is a major concern in the pig industry and market, but it is difficult to achieve by traditional selection because most meat quality traits exhibited low to moderate heritabilities http://www.animalgenome.org/cgi-bin/QTLdb/SS/), but only a handful of causative variants have been identified via QTL fine mapping analysis. During the past few years, the emergence of more cost-effective and high-throughput genotyping platforms, SNP arrays, have rendered association mapping an increasingly popular and powerful approach for QTL mapping in human, animal and plant In the past decades, quantitative trait loci (QTLs) in livestock have been detected mainly by using linkage mapping method with low-density microsatellite markers across the genome. Thus most QTLs generally span a large chromosomal region (comprising hundreds of genes), from which it is difficult to identify causative genes ×Erhualian F2 resource population White Duroc is a lean-type western pig line and Erhualian is a Chinese fat-type indigenous pig line. They show obvious differences in meat productivity and quality, and are therefore genetically distant from each other. We have previously conducted genetic linkage analyses to detect QTLs for meat quality traits using a White DurocAll procedures involving animals followed the guidelines for the care and use of experimental animals approved by the State Council of the People’s Republic of China. The ethics committee of Jiangxi Agricultural University specifically approved this study.A three-generation resource population and a Sutai pig population were involved in this study. The former one was created and managed from 2001 to 2006 as described by Ren et al. (2006) At 240±6 days of age, a total of 1030 F2 animals including 549 gilts and 481 barrows and a total of 436 Sutai pigs including 206 gilts and 230 barrows were slaughtered at a commercial abattoir. Meat quality measurements were performed on longissimus muscle (LM) between the 10th-rib and the first lumbar vertebra and semimembranosus muscle (SM) from left-side carcass, as described in detail at elsewhere P< = 10−5 (based on Chi-squared test). Samples were removed on low (<90%) call rate. After quality control, all samples passed the filter and a final set of 39,414 SNPs and 44,532 SNPs was selected for GWA in the F2 and Sutai populations, respectively. The distribution of SNP markers after filtering and marker density on each chromosome are shown in http://dx.doi.org/10.5061/dryad.7 kn7r).Genomic DNA was isolated from ear clip or spleen tissues using a routine phenol/chloroform extraction method, and DNA concentration was diluted to 50 ng/ul. The quality and concentration of genomic DNA fulfilled the requirements for the Illumina Infinium SNP genotyping platform. Genotyping of 62,163 SNPs on the Illumina Porcine 60 K SNP Beadchip was carried out at the Illumina-certified service provider, Beijing Emei Tongde Technology Development Co. Ltd (EMTD). Genotypic data is available on all F2 and Sutai offsprings phenotyped, as well as their parents and/or grandparents. Quality control was carried out using PLINK v1.07 y is the vector of phenotypes of all genotyped and phenotyped F2 or Sutai piglets; μ is the overall mean; b is the vector of fixed effects including sex and batch effects; w is the vector of slaughter weight of individuals considered as covariate; c is the vector of SNP effects with Erhualian allele substitute to White Duroc allele; a is the vector of random additive genetic effects with α∼N, where G is the genomic relationship matrix calculated from the corrected pedigree and σα2 is the polygenetic additive variance); k is the regression coefficient of slaughter weight and e is the vector of residual errors with e∼N, where I is the identity matrix and σe2 is the residual variance. X, S and Z are incidence matrices for b, w and c respectively. The herd-year-season effect was contained in the batch effect.The association analyses were conducted using GenABEL in the R software P-value was divided by the number of tests performed P<0.05/N and have chromosome-wide significance at P<1/N, where N is the number of SNPs tested in the analyses. The genome-wide and chromosome-wide significant thresholds were 1.27e-6 (0.05/39414) and 2.54e-5 (1/39414) respectively for the F2 population, and were 1.12e-6 (0.05/44532) and 2.25e-5 (1/44532) respectively for the Sutai population.The genome-wide significance threshold was determined by the Bonferroni method, in which the conventional 10P values for each SNP calculated from their observed association statistics (χ2 statistics) were ranked in order from smallest to largest on the y-axis and plotted against the distribution that would be expected under null hypothesis of no association on x-axis. Deviations from the diagonal identity line suggest that either the assumed distribution is incorrect or that the sample contains values arising in some other manner, as by a true association The influence of population stratification was assessed by examining the distribution of test statistics generated from the thousands of association tests and assessing their deviation from the null distribution (that expected under the null hypothesis of no SNP associated with the trait) in a quantile-quantile (Q-Q) plot Haplotype or linkage disequilibrium (LD) block analyses were performed for the chromosomal regions with multiple significant SNPs clustered around the peak SNP. The LD blocks were determined using Haploview version 4.2 software with default settings Population stratification for GWAS can lead to false positive results P-values of the tested SNPs for all meat quality traits are shown in P-value in the tables) with different traits: 1 for pH, 4 for meat color, 4 for drip loss and 2 for IMF.Both genome-wide significant SNPs and chromosome-wide significant SNPs for the pH, meat color, drip loss, chemical compositions, marbling and firmness are presented in Five and seven SNPs significantly associated with pH traits were identified in the F2 and Sutai pigs respectively . All theWe identified 8 and 16 significant SNPs associated with meat color in the F2 population and the Sutai population respectively . No commP = 6.74E-08), ALGA0086324 (P = 7.64E-07) and ALGA0110636 (P = 1.03E-06) on SSC15 were in a haplotype block spanning 178 kb , and 25 in the Sutai pigs with the top SNP ALGA0053636 on SSC9 (P = 1.12E-06). Neither common loci for IMF nor significant SNPs associated with marbling of LM were found in the two populations. Only one SNP MARC0090739 on SSC13 showed a significant association with marbling of SM in the F2 population.Forty-four SNPs were significantly associated with these traits: 7 for moisture content of LM, 2 for protein content of LM, 32 for IMF of LM, 1 for marbling of SM and 2 for firmness of LM . In the ×Minzhu F2 population. This article reported that most significant SNPs (except for unmapped SNPs) for these traits were located within a 10.70 Mb region (51.37–61.07 Mb) on SSC12. In this region, we also identified a chromosome-wide significant SNP ALGA0067119 at 58.08 Mb for IMF of LM. The favorable allele (G) that increases IMF derived from Erhualian . Most of the SNP effects overlapped with previously reported QTL regions on SSC2, 3, 4 and 13 for pH Using the GWA analyses of 933 F2 individuals and 434 Sutai piglets, we herein identified 127 SNPs significantly associated with the 25 pork quality traits. Of these SNPs, 116 were located in 28 genomic regions of approximately 10 Mb or less, while others cannot be mapped to the current pig genome assembly  between the two traits, it suggests the existence of a pleiotropic QTL simultaneously regulating meat color and drip loss.Additionally, a 3.01 Mb region on SSC3 (from 14.40 Mb to 17.41 Mb) was found to be associated with both pH values  and drip loss. Because the development of drip loss is largely governed by the rate and extent of postmortem pH decline SW2456 and S1426 (48–58 cM and 42–103 Mb) on SSCX in the F2 population P-value of 2.74E-05 and SNP H3GA0051863 at 104.42 Mb with P-value 1.65E-04, approaching significance level), because IMF and marbling score are highly correlated .Previously, a genome-wide significant QTL for IMF was mapped to a region flanked by microsatellite makers There are some differences in QTL findings between the present GWA study and the previously published genetic linkage studies using the same F2 population. Several 1% genome-wide significant QTLs that were reported in our previous papers Repeated detection of a QTL among populations is a way to validate the QTL. Interestingly, the GWA analyses of the Sutai population revealed some genome-wide significant SNPs for IMF and color a* on SSC9 and 11 respectively. They situate within the above-mentioned QTL regions detected in the F2 population. This result thus provided evidence that those genome-wide significant QTLs found in the F2 population are unlikely to be artifacts of linkage analyses.In addition, several association signals, e.g. those for pH values on SSC3 and for color parameters on SSC7 were repeatly identified in the two populations, validating the existence of these loci. However, many association signals appeared in only one population. This maybe resulted from the differences in environmental background  and genetic background  between the two populations.ADSL (adenylosuccinate lyase) gene that was found to be possibly associated with drip loss and pH 45 min of LM in Pietrain pigs We noticed that the significant SNPs for pH, meat color, drip loss, moisture content and protein content are rarely situated within or near known genes affecting these traits. Only one SNP DRGA0005419 for drip loss on SSC5 was located 436 kb upstream of the FOS gene is closest to the significant SNP ALGA0043983 on SSC7. This gene encodes a leucine zipper protein that has been implicated as a regulator of signal transduction, cell proliferation and differentiation (e.g. myogenesis) FOS gene could be regarded as a prime candidate gene for the QTL. Within the QTL region on SSC12, the MYH1, MYH2 and MYH3 genes that belong to the myosin heavy chain gene family (MYH) have been proposed as candidate genes by Luo et al. (2012), who also identified this QTL in their GWA study. On SSCX, a genome-wide significant SNP MARC0090296 for IMF is located at 46.12 Mb, within the SLC9A7 (Solute carrier family 9 member 7) gene. A promising gene, RGN , is located 438 kb away from this SNP. Regucalcin plays a multifunctional role as a regulatory protein in intracellular signaling processes in many cell types and is related to lipid metabolism In contrast, according to gene biological functions in lipid metabolism, adipocyte and/or muscle development, we identified some candidate genes for IMF, marbling and firmness within 500 kb upstream/downstream of the peak SNPs. In the F2 population, four GWA QTLs for IMF were detected on SSC7, SSC12 and SSCX. The ATG14 (autophagy related 14 homolog) gene that plays an important role in hepatic lipid metabolism BMPR1B , because the ligands of this repceptor is BMPs that can induce commitment of C3H10T1/2 pluripotent stem cells into adipocytes STEAP4 gene encoding metalloreductase, which is associated with obesity and insulin-resistance in human In the Sutai population, we found four QTLs for IMF on SSC1, SSC8 and SSC9. A candidate gene for the QTL on SSC1 is the UBASH3A gene, which has a role in immune function and was observed to be differentially methylated in peripheral blood leukocytes between lean and obese adolescents LEM2 gene (at 34.64 Mb) and the HMGA1 gene (at 34.98 Mb). The LEM2  gene is involved in nuclear structure organization and its mutations cause muscular dystrophies and other disorders HMGA1 gene encodes high mobility group AT-hook 1 protein that may play critical role in adipogenesis The peak SNP MARC0090739 for marbling score is located only 55 kb from the In summary, this GWA study identified 11 genome-wise significant SNPs and 116 chromosome-wide significant SNPs associated with 25 meat quality traits. Our results narrow down the previously detected QTL intervals, and reveal 7 new QTL positions. At least two QTL regions on SSC3 and SSC7 were found to affect multiple traits and are common to the two populations. However, many QTLs are not conserved across the two populations, reflecting the genetic heterogeneity of these QTLs and the complexity of the genetic basis of pork quality. For some traits including pH values, drip loss and firmness, it is the first time that they are included in a GWA analysis. In the QTL regions, some candidate genes stand out because of their functional annotations, positions and reported expression variation in related tissues. The current findings will contribute to further identification of the causal mutation underlying these QTLs and future improvement of meat quality in pig breeding programmes.Figure S1Manhattan plot of genome-wide association analysis for meat quality traits of longissimus muscle (LM) and semimembranosus muscle (SM) from a White Duroc×Erhualian F2 population and a Sutai (ST) population. (A) pH phenotypes; (B) meat color phenotypes; (C) drip loss phenotypes; (D) chemical compositions , marbling and firmness scores.(RAR)Click here for additional data file.Figure S2Qiantile-quantile plot of SNPs after quality control in genome-wide association analysis for each meat quality trait.(TIFF)Click here for additional data file.Table S1Distribution of SNPs after quality control and average distances on each chromosome.(DOC)Click here for additional data file.


Genome-wide association studies (GWAS) identify disease-associations for single-nucleotide-polymorphisms (SNPs) from scattered genomic-locations. However, SNPs frequently reside on several different SNP-haplotypes, only some of which may be disease-associated. This circumstance lowers the observed odds-ratio for disease-association.Here we develop a method to identify the two SNP-haplotypes, which combine to produce each person’s SNP-genotype over specified chromosomal segments. Two multiple sclerosis (MS)-associated genetic regions were modeled; DRB1 (a Class II molecule of the major histocompatibility complex) and MMEL1 (an endopeptidase that degrades both neuropeptides and β-amyloid). For each locus, we considered sets of eleven adjacent SNPs, surrounding the putative disease-associated gene and spanning ∼200 kb of DNA. The SNP-information was converted into an ordered-set of eleven-numbers (subject-vectors) based on whether a person had zero, one, or two copies of particular SNP-variant at each sequential SNP-location. SNP-strings were defined as those ordered-combinations of eleven-numbers (0 or 1), representing a haplotype, two of which combined to form the observed subject-vector. Subject-vectors were resolved using probabilistic methods. In both regions, only a small number of SNP-strings were present. We compared our method to the SHAPEIT-2 phasing-algorithm. When the SNP-information spanning 200 kb was used, SHAPEIT-2 was inaccurate. When the SHAPEIT-2 window was increased to 2,000 kb, the concordance between the two methods, in both of these eleven-SNP regions, was over 99%, suggesting that, in these regions, both methods were quite accurate. Nevertheless, correspondence was not uniformly high over the entire DNA-span but, rather, was characterized by alternating peaks and valleys of concordance. Moreover, in the valleys of poor-correspondence, SHAPEIT-2 was also inconsistent with itself, suggesting that the SNP-string method is more accurate across the entire region.Accurate haplotype identification will enhance the detection of genetic-associations. The SNP-string method provides a simple means to accomplish this and can be extended to cover larger genomic regions, thereby improving a GWAS’s power, even for those published previously. Multiple sclerosis (MS) has a complex etiological basis, which involves both the genetic makeup of an individual and their environmental experiences The largest of these was a multicenter, multi-country GWAS involving tens of thousands of cases and controls As a result, several haplotype-based approaches have been explored and are thought to improve the statistical power for detecting genetic associations, especially for rare causal alleles Nevertheless, the simplicity of the approach is notable and it seems likely that refinements in the method might overcome many of these concerns. It is the purpose of this paper to explore the utility of using an alternative analysis method to define the haplotypes that are present at a particular genetic locus within the population and to test the ability of such specific haplotypes to detect disease-associations.The study cohort has been described in detail previously The genotyping and quality control methods utilized for the analysis of this cohort have been previously described in detail Although several of the MS-related genetic loci were screened preliminarily (including an intergenic region), two loci were selected for detailed analysis, both of which had been previously associated with MS in a large GWAS To simplify program development, only eleven SNPs were used from each genomic region. The choice of eleven SNPs was arbitrary. Preliminary exploration demonstrated that the method could define haplotypes using anywhere between 3 and 24 SNPs although, in theory, there is no upper limit to the method as long as there are a sufficient number of homozygotes and single-site heterozygotes in the population. Nevertheless, for the purpose of this study, the eleven SNPs were chosen because they flanked both the most significantly associated SNP and the putative gene of interest . Each SNth subject in the database had the DRB1 subject-vector of (20001111110), which indicated that he possessed 2 copies of (n1), 0 copies of (n2), 0 copies of (n3), and so forth. Because, essentially, all SNPs in the genome are binary, these subject-vectors are composed of two haplotypes, which either do or don’t have a particular SNP variant at each of the SNP locations. For the purpose of the present analysis, SNP-strings were vectors, defined as those specific ordered sets of eleven binary numbers (0 or 1), representing the two haplotypes (over the entire cluster span), which combined (added) to produce each observed subject-vector.The DRB1 cluster spans a length of 160.7 kilobases (kb) of DNA and the MMEL1 cluster spans 228.6 kb . Of noteth subject in the database had a DRB1 subject-vector of (02202200000), which indicated that she possessed two copies of the (01101100000) SNP-string. The second method was to identify all individuals who were single SNP-heterozygotes . These individuals must have identical SNP-strings except for the single location where one SNP-string had a 0 and the other had a 1. For example, the 147th person in the database had a DRB1 subject-vector of (20000022221), which could only arise from the combination of the SNP-strings (10000011110) and (10000011111). In this manner, a list of most common SNP-strings in the population was compiled  have produced the observed subject-vector. For each person, one of three outcomes was possible. First, it might be the case that there was only one (unique) combination of two unambiguous “identified” SNP-strings, which could give rise to the observed subject-vector . Second,Because all such “novel” SNP-strings are (presumably) low in frequency compared to the unambiguous “identified” SNP-strings, it is very likely that, when they occur, they will be heterozygous with one of the unambiguous “identified” SNP-strings. In many cases, two or more “novel” SNP-strings could, potentially, have combined with an unambiguous “identified” SNP-string to produce the same observed subject-vector. In these cases, the “novel” SNP-string, which actually underlies the observed subject-vector was said to be in an “indeterminate” (conflicted) combination . In thesAdditional rounds of the same procedure were repeated until no further SNP-strings could be added to the “identified” SNP-string list or until more than 4 iterations were performed. Generally, however, this process was complete after 2 or 3 cycles. After screening ∼25,000 subject-vectors using this procedure, only one individual had a genotype (subject-vector) which could not be decomposed into a pair of haplotypes, in which at least one of the pair was an “identified” SNP-string. This single individual, therefore, must either have had a heterozygous state with two unidentified “novel” SNP-strings or an error was made in their genotyping.th person in the database had a DRB1 subject-vector of (11111011110). This could have arisen from the combination of either the (a7–a8) or the (a13–a20) SNP-strings. The probability of the latter combination (determined from the non-conflicted identifications in the whole population), however, involves two very rare SNP-strings compared to the former . Following the development of this “complete” list, the decomposition was repeated. Again, if a particular SNP-string was never observed in a non-conflicted combination, the estimated frequency was taken half the smallest possible frequency. However, for every SNP-string that was observed in a non-conflicted combination, its estimated frequency was taken as that, which had actually been observed. Using this “complete” list, 100% of the subject-vectors could be resolved into haplotypes. Following this “complete” analysis, those “novel” SNP-strings, which accounted for more than 1% of the non-conflicted identifications, were added to the final “identified” SNP-string list. Also, using this “complete” list, the decomposition was redone iteratively, eliminating those haplotypes that were never selected , until all remaining haplotypes on the “complete” list had a non-zero final frequency and 100% of the subject-vectors could be decomposed into their constituent haplotypes .Following, this penultimate decomposition, we reassessed those individuals who carried haplotypes that were found in fewer than 9 individuals. For these individuals, we created a new “possible” haplotype list, consisting of all of those “legitimate” alternative haplotypes, which could have combined with an “identified” haplotype to form the observed subject-vector and, thus, could have substituted for the very rare haplotype, which had been selected by the above procedure. The number nine was chosen because, for haplotypes having this expected number of observations (or more), there is greater than a 99.9% chance (Poisson distribution) that, at least, one example would have been found in the data. Also, the inclusion of a greater number of haplotypes on the “possible” list only serves to make the inadvertent exclusion of a rare (but present) haplotype less likely. The new list of “possible” haplotypes was added to the final “complete” list and, once again, the decomposition was redone iteratively, eliminating those haplotypes that were never selected, until all remaining haplotypes on the final “complete” list had a non-zero final frequency and 100% of the subject-vectors were decomposed. For both the DRB1 and MMEL1 loci, this last iterative decomposition made no difference to the final “complete” SNP-string list. The final SNP-string frequencies  were calWe compared our method of phasing to the commonly-used SHAPEIT-2 method, which has been validated on several large datasets For the purposes of our analysis, we compared the haplotype predictions using the two phasing methods. The actual haplotype frequencies in the population were estimated in three manners. The first , was to Subjects were then grouped into homozygous carriers, heterozygous carriers and non-carriers of a particular SNP-string and tested for differences in the SNP-string distribution between patients and controls. ORs were calculated separately from homozygous and heterozygous frequencies (with non-carrier frequencies as reference) and the significance of any distribution shift assessed by a Chi Square test with 1 degree of freedom. These were compared to similar calculations for the SNPs considered individually and, in the case of the DRB1 locus, to the same calculations for the actual distribution of the DRB1*1501 allele. Because the European and American data were acquired independently and in different geographic regions but were otherwise similar, these two data sets were used separately to assess the replicability of any findings. Only the American data contained both the SNP-status and the DRB1*1501 status so that only this data could be used to correlate SNP-haplotypes with a known susceptibility genotype.The results of the SNP-string analysis are presented separately for the DRB1 and the MMEL1 loci.For the DRB1 locus 18 SNP-strings were “identified” unambiguously in the population and another 3 were “identified” by the secondary analysis . One furOf the 1857 participants in this study, 54 had missing data in the DRB1 region and, therefore, their subject-vectors could not be constructed. Of the remaining 1803 subjects, 652 (36%) had unambiguous SNP-string identifications. Also, using the final “complete” SNP-string list for the decomposition, 1,084 of the DRB1 SNP-strings identifications were non-conflicted, whereas 719 were conflicted. Of the conflicted identifications, however, only selected SNP-strings were involved in the conflict. Thus, for example, the (a2) SNP-string was involved in only 256 of the conflicts. All subject-vectors could be explained either as a combination of two of the 22 “identified” SNP-strings or as a combination of a “novel” SNP-string with one of the 22 “identified” SNP-strings. In no circumstance, was there a need to postulate two “novel” SNP-strings to explain any subject-vector. Thus, for the 3,606 haplotypes present in the study population, the set of “identified” SNP-strings set see  was suffIn addition, the SNP-strings found at the DRB1 locus seemed to be related evolutionarily. Thus, these SNP-strings could be divided into four apparent “families” , which a2), as it must be for the cases to be in HWE The status of the major susceptibility allele (DRB1*1501) was known for the US-population. The OR of disease for having one-copy of this allele was 3.12 (p<0.0001) whereas the OR for having two-copies was 9.24 . This isOnly SNPs  were positively correlated with DRB1*1501 status . All othBy contrast, the SNP-string analysis provided a much closer correspondence to both MS status and DRB1*1501 status . The OR In addition, from Closer examination of the 12 individuals who accounted for the non-perfect correlation of (a2) status with DRB1*1501 status revealed that only five (a2) SNP-string carriers  did not also carry the 1501 allele. In addition, one individual (an a2–a21 heterozygote) was homozygous for the 1501 allele, which implies that the (a21) SNP-string can also carry this allele. However, the (a21) SNP-string is very different from the (a2) SNP-string . The a2. It is, For the MMEL1 locus 16 SNP-strings were identified unambiguously in the population and another 8 by the secondary analysis . No addiOf the 1,857 participants in this study, 10 had missing data in the MMEL1 region and, therefore, the MMEL1 subject-vectors could not be constructed. Of the remaining 1,847 participants, 536 (29%) had unambiguous SNP-string identifications. Also, using the final “complete” SNP-string list for the decomposition, 845 of the SNP-string identifications were non-conflicted whereas 1,002 were conflicted. Again, only selected SNP-strings were involved in each conflict. For example, the (a4) SNP-string was involved in only 271 of the conflicts. All subject-vectors could be explained either as a combination of two of the 24 “identified” SNP-strings or as a combination of a “novel” SNP-string with one of the 24 “identified” SNP-strings. In no circumstance, was there a need to postulate two “novel” SNP-strings to explain any subject-vector. Thus, for the 3,694 haplotypes present in the study population, the set of “identified” SNP-strings set see  was suffAs was the case for the DRB1 locus, the MMEL1 locus also seemed to consist of related families of SNP-strings although, in this region, the families are more interconnected and, thus, the distinction of one family from another was less clear-cut . NeverthThe genetic association for each of the 11 SNPs in the MMEL1 region are presented in Running SHAPEIT-2 on the same 11-SNP data used by the SNP-string method and excluding subject-vectors that had missing data, the two methods were only 61% concordant in the DRB1 region and 75% concordant in the MMEL1 region. When the two methods predicted different haplotype combinations to explain a particular subject-vector, in general, the combinations chosen by SHAPEIT-2 had substantially lower likelihoods compared to those predicted by the SNP-string method. Indeed, this was even the case when estimating the haplotype frequencies based the SHAPEIT-2 predictions. In addition, to account for all of the subject-vectors, SHAPEIT-2 predicted the presence of many more “novel” haplotypes than the SNP-string method, despite the fact that the smaller set of haplotypes was sufficient to explain 100% of the subject-vectors. Thus, in the DRB1 region the SNP-string method invoked only 32 haplotypes, compared to 62 haplotypes using SHAPEIT-2, in order to explain 100% of the genotype (subject-vector) data. Similarly, in the MMEL1 region, SNP-string method invoked only 41 haplotypes, whereas SHAPEIT-2 invoked 57 haplotypes. In both regions, the additional haplotypes identified by SHAPEIT-2 were never unambiguously present in the subject-vector data. Finally, although a highly significant association of the (a2) haplotype with HLA DRB1*1501 status (r = 0.760) was found using the SHAPEIT-2 method, the strength of this association was significantly less (p<0.0001) compared to that found using the SNP-string method (r = 0.981).By contrast, when the length of the SNP data included in the SHAPEIT-2 analysis was increased ten fold  the results of the 2 methods were almost identical. Thus, the two methods were concordant in 99.4% of individuals in the DRB1 region and 99.1% of individuals in the MMEL1 region. Many of the discrepancies occurred because the one method included haplotypes, which were not on the other method’s list. Also, compared to the SNP-string method, SHAPEIT-2 predicted an additional 2 haplotypes in the DRB1 region and an additional 3 haplotypes in the MMEL1 region. In most of these case there is no way to determine which method is more accurate. In the DRB1 region, for two such individuals, however, there was additional information. In both individuals, the SNP-string method predicted the (a2) allele whereas SHAPEIT-2 did not. Only one of these individuals carried the DRB1*1501 allele. Consequently, provided these individuals were typed correctly, and assuming that DRB1 status is a good surrogate for (a2) status, each method was correct only once. For the remaining discrepancies in the DRB1 region, the two methods chose combinations from the same subset of SNP-strings so that probabilistic comparisons were possible. Using the haplotype frequencies estimated from the SHAPEIT-2 output, the SNP-string method predicted combinations were more probable than the SHAPEIT-2 predictions in every case. In these circumstances, the ratios of the two probabilities ranged from 1.05 to 619 in favor of the SNP-string method.Nevertheless, such high agreement between the two methods was not uniform throughout the 2,000 kb span of DNA but, rather, was characterized by peaks and valleys of agreement alternating throughout the span. For example, as noted above, in the MMEL1 region, when the 11-SNP sequence of subject vectors was started at SNP-position 119 (rs2234167) the agreement between the two methods was 99.1%. By contrast, when the 11-SNP sequence was begun at SNP-position 100 (rs1129333) the agreement between the two methods fell to 66.6%. Moreover, in this region there was, again, a marked disparity between the two methods in the number of haplotypes needed to explain 100% of the subject-vectors. Thus, in this region, the SNP-string method predicted only 120 haplotypes, whereas SHAPEIT-2 predicted the presence of 173 haplotypes . Because the SNP-string method is based upon only the local 11-tuple subject-vectors, the outcome of this method depends only upon the (known) identity of the subject-vectors in the population. By contrast, because SHAPEIT-2 seemed to perform poorly when the input was limited to the 11-tuple subject-vectors of interest, it also seemed possible that SHAPEIT-2 might perform less well depending upon where (in the genome) the haplotype analysis was begun. Consequently, we ran the SHAPEIT-2 program starting at two different genomic locations. The first SHAPEIT-2 analysis began at SNP-position 1 (rs2031709); the second began at position 25 (rs6603813). The concordance of the predictions from these two SHAPEIT-2 analyses were then compared for each 11-tuple across the region . As can Currently, many groups throughout the world conduct GWAS studies to identify genomic regions that are associated with complex human diseases There is a high degree of confidence in the identity of those predicted haplotype combinations, which include “identified” SNP-strings. Thus, the large majority of these “identified” SNP-strings are present unambiguously and, for the others, their presence is implicated repeatedly in many different individuals. The degree confidence in the identity of those combinations that include “novel” SNP-strings is more tentative because, in most cases, these are present in only a few individuals, they are never identified uniquely, and, for the purposes of their selection, they are “assigned” a uniform (arbitrarily low) frequency. Nevertheless, because the vast majority of persons (>98%) have a combination, which includes 2 “identified” SNP-strings, the phasing method proposed here identifies, with a high degree of confidence (for the vast majority of the population), the 2 SNP-haplotypes, which make up a person’s genotype. Thus, the SNP-string method mitigates many of the potential problems discussed earlier. First, in regions spanning approximately 200 kb of DNA, only a limited number of SNP-strings (SNP-haplotypes) seem to exist within the case-control populations from Europe and the US ; Table 3Although the analysis presented here represents only two loci, the same pattern pertained to every locus screened preliminarily (including MS-associated intergenic genomic regions). Naturally, the “identified” SNP-strings, likely represent sub-families of related haplotypes, which have not been separately defined. For example, only 22 SNP-strings were “identified” at the DRB1 locus , whereasSecond, this SNP-string phasing method seems to overcome many of the objections to Clark’s original algorithm Although the SNP-string method does not directly assume that the population is in HWE, it does resolve halpotype conflicts based on the observed frequencies of the different non-conflicted SNP-strings in the population. Therefore, the method does imply the random combination of SNP-strings. Nevertheless, at least in the case of the DRB1 locus, the susceptibility allele is known to be at HWE Third, the identification of SNP-strings could be expanded to provide haplotype information over larger genomic segments. For example, suppose that the “identified” SNP-string combinations from two adjacent 11-tuple SNP-string combinations were known. Thus, suppose that the first combination was (01101100000) plus (01011010000); and the second was (01010000000) plus (10000011111). Suppose, further, that the combination of the overlapping 11-tuple segment, beginning at position 6 of the first 11-tuple, was (10000001010) plus (01000010000). In this case, the only possible 22 SNP-string haplotype configuration is (0110110000010000001010) plus (0101101000010000011111). Using similar (but expanded) logic, such an approach can be extended to provide haplotype information over increasingly large segments of the genome. Naturally, there is a need to optimize the number of SNPs and the length of DNA to be included in each SNP-string analysis and, no doubt, the method will require other refinements. Nevertheless, the haplotype information yielded by such a method would be reproducible and largely accurate over protracted regions of DNA. Naturally, in the future, next generation sequencing techniques  might replace some (or many) of the current phasing methods. However, even if these methods becomes readily available and can overcome their own phasing issues, specific methods might fail to identify disease-associations with intergenic regions, some of which have already been found in the GWAS published by the International Consortium Fourth, and most importantly, this method permits considerably more powerful tests of genetic association. The advantage of this method over the simple SNP-analysis is underscored by two examples. First within this dataset, there was no SNP-association of the MMEL1 locus with the MS disease, even for the SNP that was identified in the much larger GWAS as being highly MS-associated


ISX, SLCO6A1, TMEM183A) as well as confirming many previously identified susceptibility genes in CD GWAS . Notably, 37 of the 59 chromosomal locations indicated for CD-association by a meta-analysis of CD GWAS, involving over 22,000 cases and 29,000 controls, were represented in the 195 genes, as well as some chromosomal locations previously indicated only in linkage studies, but not in GWAS. We repeated the analysis with two smaller GWASs from the Database of Genotype and Phenotype (dbGaP): in spite of differences of populations and study power across the three datasets, we observed some consistencies across the three datasets. Notable examples included TMEM183A and SLCO6A1 which exhibited strong evidence consistently in our WTCCC and both of the dbGaP SNP-SNP interaction analyses. Examining these specific forms of SNP interactions could identify additional genetic associations from GWAS. R codes, data examples, and a ReadMe file are available for download from our website: http://www.ualberta.ca/~yyasui/homepage.html.In genome-wide association studies (GWAS), the association between each single nucleotide polymorphism (SNP) and a phenotype is assessed statistically. To further explore genetic associations in GWAS, we considered two specific forms of biologically plausible SNP-SNP interactions, ‘SNP intersection’ and ‘SNP union,’ and analyzed the Crohn's Disease (CD) GWAS data of the Wellcome Trust Case Control Consortium for these interactions using a limited form of logic regression. We found strong evidence of CD-association for 195 genes, identifying novel susceptibility genes (e.g.,  If individual SNPs (or the regions tagged by them) are independently critical in the CD-risk-altering biological functions, this approach would be effective. This may be the case for the association between rs11209026 of the IL23R gene and CD risk, where the marginal association is quantified as an estimated odds ratio of 2.661. The other SNPs that are statistically significantly associated with CD risk, however, show very weak associations with estimated odds ratios typically in the range of less than 1.5. In addition, the sum of such marginal associations is far from describing the estimated degree of genetic contributions to the risk of CD jointly affect the CD-risk. Specifically, the following two forms of SNP-SNP interactions may be motivated biologically. One is the SNP intersection form using the set theory terminology, in which all of the SNPs in a set must take their respective high-risk genotypes for CD risk to be elevated, where one, or a subset, of the set is insufficient to influence CD-risk-altering biological functions. That is, an increase in the CD-risk requires all SNPs  to take their respective high-risk genotypes. This form of interaction is similar to a set of sequential mutations that must accumulate before a cell transforms in the multistage carcinogenesis theory. The other form of SNP-SNP interaction that is biologically plausible is motivated by the notion of genetic heterogeneity. Specifically, CD risk may be elevated through multiple independent ways, each of which may be a SNP-intersection or an individual SNP  or (SNP-C) each taking its respective high-risk genotype. This form of SNP-SNP interaction is referred to as a SNP union, also derived from the set theory terminology. Under a single or combination of SNP intersections and SNP unions, assessing the independent marginal effect of each individual SNP without considering these interaction forms will either fail to discover, or observe only weak, association between the individual SNP and the phenotype of interest.Analysis of genome-wide association studies (GWAS) often focuses on identifying individual single nucleotide polymorphisms (SNPs) that modify the risk of a phenotype, assuming the underlying association of an individual SNP without considering the involvement of any other SNPs. GWASs of Crohn's Disease (CD) have also focused largely on finding such with selected candidate genesTo incorporate these specific forms of SNP-SNP interactions in GWAS data analysis, we propose using logic regression to search for sets of SNPs that are jointly associated with the phenotype of interest in the form of a single SNP intersection or union, or in combinations of thereof c, where “∧”, “∨”, and “c” represents intersection (AND), union (OR), and complement (NOT), and X's are indicators of SNP genotypes. The logic regression model takes the form:0, β1,… βp are the parameters, and L1, L2, …, Lp are Boolean combinations of genotype indicators of SNPs within a gene, also called logic trees. The logic trees are selected adaptively, using a Simulated Annealing algorithm, and based on deviance as the model fit measure 2≥0.8). In each logic regression fit, we allowed a maximum of two Boolean combinations (Ls) of at most five indicators of SNP genotypes in total. Note that these constraints are necessary in GWAS because logic regression must search a large number of potential combinations, and therefore comes with a high computational cost. To correct for the inherent instability of the performance measure when searching a large space, we refit the logic regression 20 times, starting the algorithm with 20 different initial values: this process was applied to the original dataset as well as 20 datasets obtained by permutations of the case- control labels. Of the 20 results produced by the 20 starting values, we selected the best fit, measured by deviance.Our logic-regression-based gene-level SNP-SNP-interaction analysis of GWAS data can be summarized as follows. Combinations of SNP intersections and unions can be expressed mathematically as Boolean combinations, such as (X1 ∧ X2) ∨ X312. Specifically, suppose we have N genes to be investigated, of which 10 genes are assumed to be truly associated with CD risk. The prior odds for CD-risk association for any gene is therefore 10/(N-10). To make the posterior odds of CD-risk association for a gene to 10 , a likelihood ratio for the association over no association  has to be (N-10). Based on the number of genes we examined in the WTCCC dataset , the WTCCC framework above specifies a BF of 4.12 as the threshold, above which there is strong evidence of association between the gene and CD risk. The P-value for each gene is calculated as the proportion of all permuted BF values of all genes larger than the gene's observed BF. This p-value calculation properly takes the multiple testing into account.Running logic regression for each gene in the original dataset, as well as their 20 case-control-label permuted datasets, yields an approximate Bayes Factor (BF) for each gene. The BF is approximated by the corresponding Likelihood Ratio in this case , in the base-10 logarithm , where the denominator is the median of 19 (log10) maximum likelihoods from the 19 permuted datasets . An important feature of this approximate BF is that the denominator standardizes for the higher potential for genes with larger numbers of SNPs to overfit. We follow the Wellcome Trust Case Control Consortium (WTCCC) 's framework of using BF as the measure of evidence of the observed association between each gene and CD riskWe checked if our BF-based hypothesis testing has a proper size  by using a simulation study. We randomly chose 200 genes from Chromosome 1 . We simulated a total of 50 null hypothesis datasets by shuffling case-control labels randomly and imposing an equal number of cases and controls in each dataset. We ran the logic-regression-based SNP-SNP interaction analysis and estimated p-value for each of the 200 genes in each of the 50 null datasets. The 10,000 p-values roughly followed a uniform distribution (data not shown), indicating that our testing procedure has a proper size and proper control over the false positive rate.We applied the logic-regression-based SNP-SNP interaction analysis method to the WTCCC's GWAS data comparing 2,005 CD cases to the 1,502 members of the British 1958 birth control cohort (58C) plus the 1,500 controls of the UK Blood Service sample: these used Affymetrix GeneChip Human Mapping 500K Array Sets http://www.openbioinformatics.org/gengen/tutorial_calculate_gsea.html#_Toc210887414).The genotype calls of the WTCCC were generated by the Chiamo calling algorithm. Following the WTCCC's recommendations, we only considered genotype calls with confidence score >0.9, and treated the rest of the calls as missing genotypes. SNPs with SNP call rates less than 95% were removed. We also removed SNPs based on their minor allele frequencies: the default minor allele frequency cutoff in the GenABEL R package was used (2.5/N where N is the number of subjects), resulting in cutoffs of 0.05% for the WTCCC database and 0.3% for the Jewish and non-Jewish dbGaP databases. We used a cutoff of 0.2 for the Hardy-Weinberg Equilibrium (HWE) test's false discovery rates, based on controls. SNP-gene mapping files were retrieved from the OpenBioinformatics website  of the 59 chromosomal locations, that were previously identified by a meta-analysis of single-SNP studies that involved over 22,000 cases and 29,000 controls IBD1 (Chr 16q12), two genes in IBD2 (Chr 12q13), six genes in IBD3 , eight genes in IBD5 (Chr 5q31-33), two genes in IBD6 (Chr 19p13), and one gene in IBD7 (Chr 1p36), well-established regions of chromosomes for CD risk: no gene in IBD4 (Chr 14q11-12) was included, however. In addition, there were a number of chromosome regions that did not show strong or moderate evidence of association in the single-SNP analysis of WTCCC, but had three or more genes appearing among the 195 genes, namely, 1q32, 2q14, 8p12, 10q22, 10q26, 11p14, and 18q22. These are indicated by green highlighting in the tables. Furthermore, there are clusters corresponding to certain families of genes in the 195 genes. For example, genes associated with phosphoprotein phosphatase activity  showed strong evidence of association with CD risk, of which only PTPN2 had been previously indicated.There were 195 genes with strong evidence of association between the gene and CD risk in the logic-regression gene-level SNP-SNP-interaction analysis of the WTCCC GWAS data, 40 of which are listed in ISX) was the gene most strongly associated with CD risk in our WTCCC logic-regression-based analysis and represents a new CD susceptibility gene. Homeobox genes encode DNA-binding proteins, of which many are thought to be involved in early embryonic development. ISX is a transcription factor that regulates gene expression in the intestine ISX is shown in Intestine Specific Homeobox , genes of organic cation transporters, with CD risk SLCO6A1, three genes  showed consistently strong evidence across the three datasets.We note that using the WTCCC dataset for discovery and the dbGaP non-Jewish and Jewish datasets for replication is untenable, because of the observed population differences  and the Our results illustrate the power of the logic-regression-based GWAS analysis in identifying specific forms of SNP-SNP interactions associated with a phenotype and explaining a greater extent of CD genetics. We found strong evidence of CD-Association with 195 genes including both previously identified loci through the single-SNP analysis, in addition to newly identified susceptibility genes.In this paper, we reduced the computational demand of logic regression by limiting the search to SNP combinations within the same gene, and also by fixing the size of SNP combinations in the search. These strategies have a definite disadvantage: the search will not be comprehensive and true underlying SNP-SNP interactions that are more complex than the limited size under consideration will not be discovered. In view of the current practice of assessing the marginal effects of individual SNPs one at a time, however, we submit that the limited form of logic regression proposed here provides a clear advance over, and an alternative to, the individual-SNP analysis. It can search for more biologically-plausible forms of SNP effects (combination of SNP intersections and/or unions) with greater degrees of association indicated by appreciably larger values of odds ratios, although the search remains approximate due to the limited size.marginal single-SNP analysis approach of GWAS. Some of the genes  identified by our approach were previously identified only in linkage studies and not by GWAS: this also attests to the utility of the proposed approach.Despite the limitation of our approach by the small fixed size of logic regression models, the successful discovery of CD susceptibility genes demonstrates the potential utility of the logic-regression-based SNP-SNP interaction analysis of GWAS in providing additional insights to the False positive discoveries by GWAS in which a large number of SNPs are examined for association with a disease are a major concern. Any discoveries including those reported here have to be validated rigorously in further investigations for exclusion of false positive from population stratification and genotyping errors. The candidate gene approach is also a valid alternative to the data-driven approach of GWAS, whether driven by a functional or biological hypothesis or possibly following the potential discoveries of GWAS. The application of logic regression is less computationally involved in candidate-gene studies, compared to GWAS.Proper phenotyping is a key for increasing the chance to identify susceptibility genes specific for a clinical phenotype of interest. A recent paper on Crohn's disease Increasing attention has been paid recently to pathway-based analysis of GWAS Table S1One hundred and ninety five genes with the strongest evidence for association with Crohn's Disease risk, with chromosomal locations, numbers of SNPs, approximate p-values, and Bayes factors.(DOC)Click here for additional data file.Figure S1Principal components from the three datasets: WTCCC, Non-Jewish and Jewish subjects are represented by black circles, green pluses, and blue triangles, respectively.(PDF)Click here for additional data file.


High-throughput re-sequencing, new genotyping technologies and the availability of reference genomes allow the extensive characterization of Single Nucleotide Polymorphisms (SNPs) and insertion/deletion events (indels) in many plant species. The rapidly increasing amount of re-sequencing and genotyping data generated by large-scale genetic diversity projects requires the development of integrated bioinformatics tools able to efficiently manage, analyze, and combine these genetic data with genome structure and external data.In this context, we developed SNiPlay, a flexible, user-friendly and integrative web-based tool dedicated to polymorphism discovery and analysis. It integrates:1) a pipeline, freely accessible through the internet, combining existing softwares with new tools to detect SNPs and to compute different types of statistical indices and graphical layouts for SNP data. From standard sequence alignments, genotyping data or Sanger sequencing traces given as input, SNiPlay detects SNPs and indels events and outputs submission files for the design of Illumina's SNP chips. Subsequently, it sends sequences and genotyping data into a series of modules in charge of various processes: physical mapping to a reference genome, annotation , SNP frequency determination in user-defined groups, haplotype reconstruction and network, linkage disequilibrium evaluation, and diversity analysis .Furthermore, the pipeline allows the use of external data  to define groups and compare statistical indices.2) a database storing polymorphisms, genotyping data and grapevine sequences released by public and private projects. It allows the user to retrieve SNPs using various filters , to compare SNP patterns between populations, and to export genotyping data or sequences in various formats.Our experiments on grapevine genetic projects showed that SNiPlay allows geneticists to rapidly obtain advanced results in several key research areas of plant genetic diversity. Both the management and treatment of large amounts of SNP data are rendered considerably easier for end-users through automation and integration. Current developments are taking into account new advances in high-throughput technologies.http://sniplay.cirad.fr/.SNiPlay is available at:  The combination of high-throughput re-sequencing and genotyping technologies with the availability of reference genome sequences allows Single Nucleotide Polymorphisms (SNPs) and insertion/deletion (indels) to be extensively characterized in many plant species -3, incluTwo large projects have recently been launched: one to produce a SNP diversity map of the entire grapevine genome and the other to estimate linkage disequilibrium between SNPs in four genomic regions. These grapevine projects are based on the present ability to obtain high-throughput sequences and subsequent SNP genotypes using the recently available complete genomic sequence.In this context, our objective was to conceive an original web-based tool, called SNiPlay, dedicated to polymorphism discovery and analysis in genetic diversity studies. It is an integrated pipeline freely accessible through the internet that combines and transparently chains existing softwares to compute various statistical indices and generate graphical layouts of SNP data. Given standard multiple sequence alignments, Illumina chip genotyping data or Sanger sequencing traces as input, SNiPlay detects SNPs and indels events. In a second round, it sends sequences and genotyping data into an integrative pipeline executing a series of modules in charge of various types of post-processing of the collected polymorphisms and associated genotyping data.In addition, a database has also been designed and developed to store grapevine polymorphisms, genotyping data and sequences produced by nationally-funded public projects.To our knowledge, there is to date no freely available web-based tool able to integrate SNP management with as many analytical tools.Previous software packages have been implemented to automatically discover SNP markers, but most of them are not available through a web server and must be installed on a local computer. Arlequin  is a sofSNiPlay aims to fulfil this need as a web application assisting biologists in extracting and analyzing polymorphism data in a simple and robust way, while offering "one-stop-shopping" among leading methods corresponding to well-accepted standards.The production by the pipeline of well-formatted output data enables their subsequent deeper analysis using external specialized tools.Since it computes standard FASTA alignments, the pipeline is generic enough to manage sequencing data obtained from traditional Sanger technology as well as those from New Generation Sequencing (NGS) techniques.SNiPlay combines two major components in a common web interface: a set of pipelined programs and a relational database.The pipeline consists of Perl modules wrapping various existing programs, except for SNP detection from alignments, which is computed by a home-made module. The SNiPlay database system uses MySQL. The web interface is implemented in Perl CGI scripts running on an Apache web server. The interactivity is allowed by JavaScript and Ajax technologies.The automated process consists of seven main modules Figure . The comThis process represents the core of the system, and all subsequent analyses are based on it. Users can start from various entry points using different types of input files and corresponding procedures.1. When input is provided as standard FASTA alignments, SNiPlay uses a home-made Perl module to detect SNPs and insertion/deletion events and to extract allelic information for each polymorphic position. The detection consists of a simple parsing of the alignment in order to determine positions, i.e. columns, exhibiting variation, either a base variation or a heterozygous IUPAC code for SNP, or the presence of gaps for indel. In a second time, alleles are assigned to genotypes for each variant position. SNiPlay provides both lengths and sequences for indels.2. When a genotyping file is loaded, a parser extracts allelic data, but the subsequent mapping, annotation and diversity analysis steps are disabled because sequence information is lacking.3. When the input consists of trace files, the first step makes use of the Polymorfind  program,Whatever the entry point, the next step consists in reporting the statistical information for each polymorphic position, namely, variation, number of alleles, frequency of the most and least frequent alleles, number of readable accessions, and number of homozygous vs. heterozygous accessions for each polymorphism. These SNP statistics are summarized into Excel files available for download. Additionally, this module generates a formatted SNP file for the specific design of Illumina's SNP chips.Allelic files are generated in various formats so that users can load genotyping data and run locally some widely used external programs dedicated to haplotype reconstruction, association mapping, diversity and phylogenetic analyses and population genetic structure analysis. Indels are binary coded in allelic file so that they can be interpreted as SNP and processed by the phasing programs.Finally, a FASTA alignment and a consensus sequence showing which nucleotides are most abundant at each position are generated for each set of sequences. This consensus sequence offers a good representation of the initial dataset and will be used subsequently for mapping, functional annotation, and diversity analysis.An automatic BLAST-based  mapping The first best hit is used to localize the amplicon within the genome and a table displays the number of hits to reveal the presence of duplicate genes or pseudo-genes.The genome-associated GFF3 annotation  is then A simple BLAST is performed against the SWISS-PROT and TrEMBL protein databases to further annotate the genomic region and to assign a putative function to the gene. If the mapping reveals that the region corresponds to a predicted gene, the putative protein is then used for BLASTP; otherwise, a BLASTX is launched using the consensus nucleotide sequence as query.A haplotype is the sequence of SNPs in one of the two homologous chromosomes of a specific region, and it cannot be directly established from Sanger sequencing techniques, which are unable to assign each allele to a specific copy within a pair of homologous chromosomes. Haplotype reconstruction consists in inferring the most probable allele combination on each homologous chromosome from genotyping data. Moreover, this method allows missing data to be inferred. The pipeline implements different programs to perform this task, with parameters set at default values: Gevalt  using th2, D') are defined as the non-random association of alleles between two loci, and their significance can be calculated from the reconstructed haplotypes. HaploView then produces a plot of LD and haplotype blocks along each region/amplicon downloadable as a PNG image.Two classical measures of linkage disequilibrium (r2S), relatedness between individuals (r2K) or both (r2SK) are calculated from genotypic data by a newly adjusted algorithm implemented as an R module [2S, r2K or r2SK measures are provided in addition to the classical r2 measure defined for unphased genotypes using the method of Rogers et al. [Alternatively, three new linkage disequilibrium measures correcting for biases due to population structure  Collections of trace files (ABI sequence electrophoregrams). In this case, SNPs and indels are automatically identified using the Polymorfind program.ii) Alignments in FASTA format . SNiPlay also accepts alignment outputs from the Staden  or Genaliii) Genotyping files in the ".ped" format, which is the widely used format for linkage pedigree data required for Gevalt.Furthermore, alignments or genotyping files can be provided either as a single file or as a zipped collection to analyze several genes in the same run. In order to enable the program to extract gene/amplicon and accession names, users are requested to specify under which form the sequence identifiers are provided.Electrophoregrams should be provided in a single archive containing all the trace files. If several amplicons are included, each will then be automatically detected using the file nomenclature.Each pipeline step can be set up by choosing among the programs and settings available.An option is available to first filter out missing data within an alignment. This process removes columns over a certain threshold of missing data (threshold specified by the user), therefore generally eliminating sequence ends with poor trace quality.In a second tab, users can select subgroups of accessions to be analyzed among those extracted from the input Figure . SNP dishttp://sniplay.cirad.fr.SNiPlay may be accessed at SNP pipeline section constitutes a freely accessible, user-friendly, customizable pipeline with which users can select the analysis steps to be performed, alternative programs being available at some steps. All pipeline results are accessible through a tabulated menu facilitating navigation, and examples are provided to familiarize users with the correct input and expected results .At the time of writing, it includes polymorphisms and genotyping data produced by three public grapevine projects:Fleshless project [fleshless berry" locus.• The  project  aims at Vitis vinifera (including 28 cultivars and 7 wild accessions) and 12 other Vitis species. A detailed analysis of these data will be presented in a separate paper.• A grapevine project used a genome-wide SNP discovery approach based on Sanger re-sequencing of 900 amplicons from 47 accessions: 35 • A third project concerns polymorphisms in genomic regions involved in berry size determinism.SNiPlay allows one to easily explore data and retrieve SNPs and indels using various filters  to compare SNP patterns between populations and to export genotyping data or sequences in different formats.After having selected a project, the user has to opt for genotypes on the left side and genomic regions or genes on the right side and adjust the filters; the system then dynamically extracts SNPs for this selection and calculates corresponding frequencies and consensus sequences Figure . If the For a SNP with a given position in the genome, the database can collect and combine genotyping data obtained in distinct experiments, from sequencing and/or Illumina VeraCode projects, and helps to manage conflicting data. These positions are highlighted in the results table in green if no difference appears between different experiments or in yellow if differences are detected. In this latter case, the conflict is replaced by missing data when exporting allelic files or calculating SNP frequencies Figure .With respect to this concept, the interface can accommodate different "merging levels", from global synthesis (high merging level) to the experimental level (low merging level) using a specific program. The sample can be composed of varieties, accessions or DNA samples, while the sequence can be a gene, an amplicon or a submission batch (as provided by each reader of a given electrophoregram). Depending on the merging level, users can decide to focus on a specific experiment (which will not imply any merging) or force the system to combine allelic data between experiments.Request results can finally be exported as different files: genotyping file, alignment file or submission file for Illumina VeraCode technology. SNPs with accompanying files can also be exported for submission to GnpSNP, the INRA SNP repository .Vitis projects for a total of 1,111 amplicons within known genes. The "project overview" option summarizes the data for each project. Today, the database contains 39,163 polymorphisms corresponding to 36,905 distinct positions in the grapevine genome, spanning all the chromosomes and including 2601 indels. Other information that can easily be obtained from the database is the number of mutations in coding vs. non-coding sequences and the number of synonymous vs. non-synonymous substitutions, as well as the average SNP density along the genome.The SNiPlay database currently contains data pertaining to 4 This section also provides graphical outputs comparing the average heterozygosity of accessions, the frequency of alternative nucleotide substitution, and the distribution of indel sizes Figure .The "group comparison" option allows the user to compare summary data among given sub-populations, for example, among different taxa.Adaptation or domestication promotes phenotypic evolution. At genomic level, these changes correspond to genetic variations among targeted loci. Tajima's D index is used to evaluate these modifications and to distinguish between a DNA sequence fitting the neutral theory and one evolving under selection. A negative Tajima's D signifies an excess of low frequency polymorphisms, indicating population size expansion and/or positive selection. A positive Tajima's D signifies a deficit of low frequency polymorphisms, indicating a decrease in population size and/or balancing selection. At the single-locus level, it is impossible to distinguish between demographic effects and selection at this locus. However, analysis of the distribution of Tajima's D for a collection of genes as offered by SNiPlay allows genes targeted by selection to be identified   around the causal genetic variants responsible for the variation of a trait within an association panel directly determines (i) the ability to identify the genomic region carrying these causal genetic variants given the distribution of genotyped markers spanning the whole genome and (ii) the precision with which causal polymorphisms can be identified, i.e., the minimum physical size of the genomic region carrying the causal variants that are in complete LD with neighbouring ones. Then linkage disequilibrium analysis is generally needed (i) as a prerequisite to whole-genome scanning association studies to determine the number and distribution of markers needed to identify genomic regions involved in phenotypic variation and (ii) a 2, D') and their associated p-values to be calculated from haplotypes inferred from different phasing programs and visualizes them using Haploview  to define a set of well-distributed SNPs to be used as a reference for other studies (Bacilieri pers. comm.), such as the estimation of kinship for the correction of LD measures, and ii) to allow the specific design of Illumina arrays for other projects focusing on association mapping, for example.In the pipeline section, an optional tabulated file can be provided by the user with external information for each accession such as its wild or cultivated status, its geographical origin, its taxon, population stratification, phenotypic characteristics, or any other information linked to the accessions. This information is taken into account at different levels of the analysis to compare groups or populations:• Venn diagrams are displayed to report the number of SNPs shared between the different groups Figure  as well • A comparative diversity map can be generated to show a colour distribution of Tajima's D values along chromosomes for each group Figure . SNiPlay• The haplotype network integrates coloured pies indicating the distribution of subgroups within each haplotype Figure .Thanks to the well-formatted outputs offered by SNiPlay, users are able to load "ready-to-use" allelic files and locally invoke well-known external programs for specific purposes: PHASE , TASSEL Input limitations depend on the entry point in the pipeline. The server currently accepts archives containing a maximum of 4,000 alignment files for a single run, themselves limited to 200 sequences/accessions. In the case of ABI sequence electrophoregrams, loading is restricted to archives of 2,000 ABI trace files. Allelic files are also limited to a maximum of 500 SNPs and 500 accessions. These limitations could be relaxed in the future, should the application migrate onto more powerful servers.SNiPlay is a flexible and user-friendly web application that rapidly explores SNP data using a wide range of analyses.Our experiments within the grapevine genetics projects showed that SNiPlay allows geneticists to efficiently manage and analyze the large amounts of SNP data generated by high-throughput technologies and, therefore, to rapidly obtain advanced results in several key areas of plant genetic diversity research.The major advantage of SNiPlay is that it saves considerable time by automatically detecting SNPs and indels from sequencing data and by injecting allelic data into a reliable multi-analysis system.It presents detailed and well-organized information so that scientists can avoid routine data processing work and focus on the biological aspects of their own projects. For instance, submitting multiple datasets makes it easy to quickly identify and localize signals of positive selection with the help of diversity maps.Drosophila melanogaster. Recently, AutoSNPdb [Brassica species.Currently, besides the International HapMap project  in HumanutoSNPdb  has beenWith more than 1,400,000 SNP alleles at 39,163 sites, SNiPlay hosts and provides the grapevine community with a sizeable resource of polymorphisms and other genetic data, and could become the international reference as a grapevine SNP datasource. It can thus provide significant global statistical information on SNPs and indels across the grapevine genome such as density, proportion of synonymous SNPs, and heterozygosity.de novo transcriptome sequencing in various Coffea species.SNiPlay is fully functional and currently used by multiple laboratories. Initially conceived to meet grapevine research needs, it was successfully used with grapevine Sanger sequencing data for SNP discovery and genome-wide diversity analysis and with Illumina VeraCode genotyping data for LD fine study in regions of interest. However, SNiPlay provides a generic system for SNP management designed to be applicable to sequence or allelic data of any diploid species. Indeed, the system presently manages allelic data on rice, and it is also being used for In addition, SNiPlay is independent of the sequencing technology since it accepts standard FASTA alignments. Short reads from NGS deep sequencing experiments  can make use of SNiPlay, if the assembly have been preliminarily pre-processed and formatted into FASTA alignment files including IUPAC symbols at heterozygous positions. SNiPlay, being a web application, cannot afford such a task online. However, among the further developments planned for coming years, an additional and independent module will be incorporated to pre-process the assembly of short reads from NGS technologies, by detecting SNPs using depth and quality criteria and converting assemblies into FASTA alignments. As a rule, efforts will be constantly undertaken to take into account future technologies.The second major development planned is the addition of a new component for association studies based on the TASSEL software and/or the GenABEL library .The modular architecture of the SNiPlay pipeline renders this system easily extensible, which will facilitate the addition of new features and programs according to user feedback. For instance, we plan to integrate another phasing program such as FastPhase  and to iMore generally, we intend to integrate in the future new modules able to manage other types of analyses such as kinship, population structure, and phylogeny.Finally, because SNiPlay is being accepted by the local community working on diversity projects as the tool for SNP management and analysis, it will naturally benefit from continuous maintenance and improvements. It is expected to evolve towards an open-source distribution package including both pipeline and database so that it can be locally and independently installable in multiple labs and adapted for small user communities working on different plant species.• Project name: SNiPlayhttp://sniplay.cirad.fr• Project home page: • Operating system: web access• Programming language: PERL• Any restrictions to use by non-academics: noneADe wrote the application and coordinated the development. SN and LLC helped to design the software architecture. RB, ADo and JPP helped to define appropriate methods and user needs. MR and PT provided advice and guidance throughout the project.All the authors read and approved the final manuscript.


Ascaridia galli in the brown layer line . Therefore, IFNG seems to be a promising candidate gene for further studies on helminth resistance in the chicken.Single nucleotide polymorphisms (SNPs) in the genes for interleukin-4, -13 and interferon-gamma, and 21 additional SNPs which previously had been significantly associated with immune traits in the chicken, were genotyped in white and brown layer hens and analyzed for their association with helminth burden following natural infections. A nucleotide substitution located upstream of the promoter of the interferon-gamma gene was significantly associated with the log transformed number of  In the European Community, animal welfare issues and changes in consumer demands have resulted in a ban of conventional cages for laying hens from 2012 on (Council Directive 1999/74). This has resulted in an increased importance of floor husbandry systems and consequently in a renewed relevance of helminthoses  and restIL4), interleukin-13 (IL13) and interferon-gamma (IFNG) genes were not included.Although immunity in birds is not as well understood as in mammals, it has been shown that as in mammals , helmintIL4, IL13 and IFNG genes and of 21 additional SNPs significantly associated with immune traits in white and brown commercial layer lines and to analyze their association with worm numbers resulting from a natural helminth infection in order to identify gene regions as promising candidates for further studies on parasite resistance in chickens.The aim of the present study was to determine genotypes of SNPs in the Ascaridia galli, Heterakis gallinarum, Capillaria spp. and tapeworms of 197 Lohmann Brown (LB) and 246 Lohmann Selected Leghorn (LSL) hens and pedigree data (sires) were available from a recent study conducted by Kaufmann et al. . Genotyping of two IFNG and three IL4 SNPs was done by PCR restriction-fragment-length-polymorphism (RFLP) analysis. For this purpose forward and reverse primers for IFNG  and for IL4  were used to amplify DNA fragments that were digested with appropriate restriction enzymes  were genotyped with matrix-assisted laser desorption/ionization time of flight mass spectrometry (MALDI-TOF MS) by Eurofins Medigenomix GmbH, Martinsried, Germany, using the Sequenom Massarray iPLEX Gold System . Twenty-one SNPs were chosen from the study of Biscarini et al.  to get approximately normally distributed data, as done before by Kaufmann et al. . AssociaBLB1 region of the major histocompatibility complex (MHC), was fixed in both lines. SNP 12 showed a minor allele frequency < 0.05 in LB and was monomorphic in LSL. Therefore SNPs 12 and 26 were not included in association analyses in any of the two lines.Allele frequencies of the 27 genotyped SNPs are shown in Figure IL4 were genotyped by amplifying a single PCR product and digested with a single enzyme, enabling the demonstration of a total of 3 haplotypes  were not significantly different between LB and LSL  were significantly associated with one of the traits analyzed; whereas 7 SNPs showed a tendency towards significance for association with one or more of the traits , whereas genotype CT occurred in a lower frequency (17%). The average A. galli worm number was 6.4 ± 1.0 in LB hens with the genotype CC, whereas it was 11.7 ± 2.2 in hens with the genotype CT. As 10 of the 19 LB sires had only progeny with the CC genotype, the association analysis for SNP 1 regarding the number of A. galli in LB was repeated only with hens (n = 90) from the 9 other sires, resulting in a P-value of 0.011.For SNP 1, showing significant association with the log transformed worm number of A. galli worm number- accompanied by higher heritabilities for worm numbers and at the same time significantly higher worm numbers, compared to LSL [CC and CT of SNP 1, a nucleotide substitution we previously identified in the IFNG 5'-flanking region of New Hampshire and White Leghorn, were significantly associated with the number of A. galli worms in LB. In sheep, where nematode resistance has been a breeding goal much longer than in poultry, IFNG variants and markers located in the same chromosomal region as IFNG have already been associated with nematode resistance [IFNG SNP analyzed here is located outside and upstream of the gene promoter [A. galli worm numbers as in LB, it is more likely that its association with the A. galli number in LB is due to a linkage with a causal SNP in IFNG than influencing the worm number itself. Repeating the association analyses with other hens and with additional neighboring SNPs will be necessary to confirm the genetic influence of IFNG on susceptibility to A. galli in chickens that is supposed here. However, additional IFNG SNPs will be mainly located in non-coding gene regions, since the chicken IFNG is known for its high degree of sequence conservation especially in coding regions [The higher number of monomorphic SNPs in the white layer line  compared to the brown  was in accordance with other studies ,16 and csistance -20. Howepromoter . Togethe regions ,22.IFNG, some of them may be worth studying in further experiments, especially variants of IL13 as SNP 25 and other adjacent SNPs.The existence of only weak linkage to a causal SNP may also be the reason that some SNPs only tended to be associated with one or more of the traits and in only one of the two lines. Therefore, additionally to SNPs in The authors declare that they have no competing interests.GL conceived the study, designed and carried out the molecular genetic and association analyses and drafted the manuscript. MG conceived and designed the infection study, acquired funding and contributed to the interpretation of data. FK acquired, analyzed and interpreted the parasitological data. GE conceived the study, participated in its design and helped draft the manuscript. All authors read and approved the final manuscript.