The 60,000 selected SNPs, evenly spaced on the goat genome, were submitted for oligo manufacturing  and published in dbSNP along with flanking sequences and map position on goat assemblies (i.e. scaffolds and pseudo-chromosomes), sheep genome V2 and cattle UMD3.1 assembly. Ten breeds were then used to validate the SNP content and 52,295 loci could be successfully genotyped and used to generate a final cluster file. The combined strategy of using mainly whole genome Next Generation Sequencing and mapping on a contig genome assembly, complemented with Illumina design tools proved to be efficient in producing this GoatSNP50 chip. Advances in use of molecular markers are expected to accelerate goat genomic studies in coming years.The success of Genome Wide Association Studies in the discovery of sequence variation linked to complex traits in humans has increased interest in high throughput SNP genotyping assays in livestock species. Primary goals are QTL detection and genomic selection. The purpose here was design of a 50–60,000 SNP chip for goats. The success of a moderate density SNP assay depends on reliable bioinformatic SNP detection procedures, the technological success rate of the SNP design, even spacing of SNPs on the genome and selection of Minor Allele Frequencies (MAF) suitable to use in diverse breeds. Through the federation of three SNP discovery projects consolidated as the International Goat Genome Consortium, we have identified approximately twelve million high quality SNP variants in the goat genome stored in a database together with their biological and technical characteristics. These SNPs were identified within and between six breeds : Alpine, Boer, Creole, Katjang, Saanen and Savanna, comprising a total of 97 animals. Whole genome and Reduced Representation Library sequences were aligned on >10 kb scaffolds of the  Goat was one of the first domesticated animals, around 10,500 years ago in the Fertile Crescent  in 2010, whose aims were to consolidate research efforts at the international level. The Goat Genome reference sequence has been published http://goat.kiz.ac.cn/GGD/). A RH panel has also been developed Recently, the development of next generation sequencing (NGS) allowed de novo assembled goat genome were made available by BGI sequencing center (www.genomics.cn) to allow mapping of the flanking sequences of the identified SNPs. The assembly consisted of 285,379 scaffolds representing 2,665,022,236 bp. Since a Yunling female goat was sequenced to create the assembly, no representation of the Y chromosome was included in the sequence. As shown in Scaffolds of the SNP discovery projects were underway in three laboratories, before they were consolidated within the IGGC. This explains why the SNP discovery was achieved through two different pipelines, for milk and mixed breeds  in one project and meat breeds  in another project.For dairy and mixed breeds, sixteen animals  were sequenced using a HiSeq2000  and produced 80 to 220 million high quality (passing filter) 100 bp reads per animal. This resulted in a sequencing coverage of 13.5 to 26X depending on the breed, after filtration of the BAM files. Another set of 17 Dutch Saanen animals was used to construct a RRL library. Sequencing from this RRL library resulted in 120 million 76bp sequences. The sequence coverage, using those data, after BAM filtration, was 2.85X on average in the genomic regions that were covered with one read at least.SNP discovery was performed for each breed separately after which the results were combined. A minimum of two reads per allele was mandatory in the definition of a SNP not including the reference, with a minimum sequencing depth of six and a maximum depth of 100, to avoid repeated regions. These criteria have also been used with success in cattle For meat breeds, sequencing was performed on two pools of animals (20 Boer goats in pool 1 and 20 Savanna together with 24 Katjang goats in pool 2). Over 680 million sequences (106 Gbp) were obtained for each pool, resulting in an average coverage of 35X. After alignment to sheep and cattle reference genomes, SNP discovery was performed within each pool and resulted in 4,001,890 SNPs . A minimum of three reads per allele was used to define a SNP, not including the reference. Around 90% of the four million SNPs and their flanking sequences were successfully aligned with goat sequence scaffolds. The number of these discovery SNPs is reported in Additional SNPs were identified in expression data, from published ESTs and RNASeq projects (see acknowledgements section) and included in the candidates for final SNP selection. The detection procedure described in the methods section was followed by mapping on the goat scaffolds and resulted in a 7,008 SNP list.Regarding PRNP, casein and DGAT1 genes, already known as important for milk composition and disease resistance, regions orthologous to cattle were identified on goat scaffolds 881, 980 and 2304 respectively  and exteA total of 11,924,638 variants were tracked including 1,229,120 Indels and 10,695,518 discovery SNPs. For each variant, Illumina Assay Design Tool (ADT) score, Infinium type  and allele frequency was stored and used for final SNP selection.Illumina iSelect 60K designs can include both Infinium II SNPs , that require a single bead type, and Infinium I SNPs  that require two bead types for genotyping Since the average spacing of markers on a 50–60K SNP chip is about 60,000 bp, we disregarded SNPs mapping on contigs of fewer than 10,000 bp. To maximise the success rate of the beadpool, we also eliminated INDELs or SNPs that a) represented >2 alleles b) were within 20bp from another variant c) had an ADT score <0.8 or d) had an estimated MAF <0.2 in breeds of interest. This list of 537,145 SNP was ranked using increasing category number  and decreasing MAF. Class 1 SNPs were detected in ESTs and all included. The following ten classes corresponded to a decreasing number of breeds in which the SNP is observed. The first selection of 60,000 SNPs was done with spacing along the genome using an in-house algorithm (available on request). More than 97% of the SNPs were heterozygous in at least three breeds. To capture some remaining large genomic regions without SNPs, 1,000 SNPs of this first list were replaced by an alternative SNP, presenting either a higher number category or a <0.2 MAF or an <0.8 ADT score. Finally, as shown in Estimated MAFs of the selected SNP ranged from 0.05 to 0.5 with 50% of the selected SNP with a >0.420 MAF, as shown in To check the spacing procedure on a closely related assembled genome, the selected SNP flanking sequences were aligned on cattle UMD3.1 assembly. A total of 59,001 SNP flanking sequences were aligned on the cattle genome. As shown in http://www.ncbi.nlm.nih.gov/projects/SNP/snp_viewBatch.cgi?sbid=1057128 and File S1). The submission also included 60bp flanking sequences on each side of the variant site, estimated MAF value, Illumina_score, Illumina_state (successful or not) and position on goat scaffolds, CHIR_1.0, Sheep_OARV2 and Bovine_UMD3.1. Unique dbSNP Submitter SNP (ss) accession numbers were assigned for each variant, including those on the goatSNP50 chip, and they are listed in File S2. The corresponding Reference SNP (rs) numbers were assigned for dbSNP Build 136 release in December, 2012. As the goat reference genome evolves, it is possible a small fraction of rs numbers may be updated in future dbSNP release if evidence suggests a different assignment.For the 60,000 selected SNPs, the variants were submitted to dbSNP under the handle IGGC with batch ID “CAPRI-BATCH1” (www.goatgenome.org).The file containing this information is also downloadable from the IGGC website . This beadpoool was tested with 288 animals. 281 animals were successfully genotyped on 52,295 loci (87.15% of the submitted SNPs and 98% of the synthesized SNPs) that had a minimum average call rate of 99.83% . These gThe results of MAF >5% are indicated in The GoatSNP50 chip described in this paper has been validated as a useful tool for a variety of goat breeds, with >78% SNPs segregating in seven breeds, including Angora and Skopelos, that had not been used for SNP discovery. This chip has already been successfully used for genetic diversity studies on Boer, Cashmere and Rangelang goats via the quality checking process by Illumina. This results in a random failure that is unrelated to the sequence and design of the probes themselves.Technology failures during probe synthesis resulted in 11% loss of SNPs that were submitted to design resulting in 89% conversion of the pre-manufacturing content which was slightly below expected conversion of working assays based upon the weighted average ADT design score of.95 leading to the expectation that 95% of the assays would convert to working assays. The reason for the failure of a subset of the probes to appear in the final post-manufacturing content is independent of the likelihood of a sequence to convert to a working design due to the random nature of the assembly of beads into wells and the minimum redundancy threshold in the Infinium manufacturing process for each probe. If a probe was manufactured successfully, but the number of beads representing that probe were not represented in a minimum redundancy of over 5x , then it was removed In addition, because we did not have goat genome input into the ADT tool, we were not able to do a check on probe design based upon expected repetitive, low diversity or otherwise known problematic regions of the genome. As a result, any scores that come from the design as “species: Other” will be slightly inflated compared to whether we had the genome check integrated into the design process.50–60K SNP chips are primarily used for finding association between markers and phenotypes. To achieve this well, both a uniform distribution of the SNPs on the genome and the complete coverage of the genome are required. A large excess of SNPs is required to be able to evenly space the SNPs on the genome. Groenen and co-authors The combined strategy of using mainly whole genome NGS and mapping on a contig genome assembly, coupled with Illumina designing tools proved to be efficient when creating the GoatSNP50 chip. We hope that with this tool goat genomics studies can now advance rapidly.DNA samples from this study came from three Institutes in France, Netherlands and Malaysia. Neither sperm collection nor blood sampling was performed specifically for this study. Sperm collection was performed on bucks by Capgenes, which obtained the authorization from DGAL  FR CC 860. Sperm collection was made by Artificial Insemination stations, and we used extra doses from this collection. Blood samples were taken from commercial farms. Animals did not belong to any experimental design but were sampled by veterinarians and/or under Veterinarian supervision for routine veterinary care; extra samples were requested when blood sampling occurred.High quality genomic DNA was extracted from sperm of seven Alpine, three Creole and six French Saanen bucks. Briefly, the content of three straws from the same animal was pelleted  after dilution in 500 µL of 10mM TrisHCl, 10mM EDTA, 150mM NaCl pH = 7.5 buffer and rinsed in the same buffer. Disulphide bonds of protamines were reduced with DTT and then proteins digested through the addition of 200 µL of 10mM Tris-HCl (pH 7.5), EDTA 10mM, NaCl 100mM, SDS, 2%, 0.1M DTT, incubation 1.5h at 65°C and then addition of 20 µL of 10mg/mL proteinase K and further incubation at 37°C overnight with agitation. The next day, two extractions with an equal volume of Tris-HCl (pH = 8) saturated phenol, followed by two chloroform extractions were performed and the final supernatant was precipitated with sodium acetate and ethanol. After centrifugation , the DNA pellet was dissolved in 500 µL of Tris-HCl 10mM, EDTA 0.1mM, pH = 7.4. DNA was extracted out of sperm from 17 Dutch Saanen bucks using a similar protocol. Genomic DNA was extracted from 300 µL of blood samples of 20 Boer goats, 20 Savanna goats and 24 Katjang goats individually using Promega Wizard Genomic DNA Purification System. DNA quantity and purity were measured using the Nanodrop ND1000. Possible degradation was inspected on an agarose gel and only high quality undegraded DNA samples were used to prepare the DNA pools.AluI or HhaI . The digested pools in Orange loading dye (Fermentas) were size-fractionated on precast 10% polyacrylamide in 1xTBE with the Criterion™ Cell (BioRad). After staining, the target fragment size range of 110–130 bp was sliced out of the gel. The gel slice was sheared by nesting a 0.5ml eppendorf tube (with a hole in the bottom formed with a needle) containing the gel slice inside a 2ml eppendorf tube, and centrifuged at 14000 rpm for 2 minutes. The sheared gel pieces were covered with 300ul DNA recovery buffer , vortexed, and eluted at 4°C overnight, followed by 15 minutes incubation at 65°C. The DNA was recovered using the Montage DNA gel extraction devices (Millipore) and resuspended in DNA hydration solution (Gentra Systems).For detection of SNPs on Dutch Saanen bucks, restricted representation libraries (RRL) were constructed as described by Kraus et al Each of the 16 DNAs from Alpine, Creole or Saanen goat was sequenced independently on an Illumina HiSeq2000 machine using one lane for each animal. 3µg of DNA was used for each animal to construct libraries following manufacturer’s protocol (Illumina TruSeq DNA sample prep). Then, libraries were quantified and sequenced on one lane for each on an Illumina HiSeq2000 sequencer using standard Illumina protocol (TruSeq SBS kits v3), with 100 bp paired-end reads and ∼250 bp insert length.Two genomic DNA pools were made for meat goats: pool 1 from 20 Boer goats and pool 2 from 20 Savanna goats and 24 Katjang goats. Each DNA sample was quantified three times (using Hoechst dye and fluorometer) and equal amount of genomic DNA from each sample based on the average concentration of the three readings were used to make DNA pools. The pools were whole genome shotgun sequenced on an Illumina Genome Analyzer IIx machine using the standard Illumina protocol, with 78 bp paired-end reads and ∼300 bp insert length.AluI and HhaI) were combined and prepared using the Illumina Sample Preparation kit The genomic RRLs of the 17 Dutch Saanen bucks , minimum number of reads for each allele = 2. SNP MAFs were estimated by counting the number of reads of each allele for the whole dataset .http://www.cbcb.umd.edu/research/bos_taurus_assembly.shtml and http://www.livestockgenomics.csiro.au/sheep/websites, using MosaikAlignMosaiksamtools , and DNA LandMarks  in-house algorithm. The quality filtering for SNP discovery were a SNP quality of at least 20 and minimum three reads of each allele. Approximately 300 bp of consensus flanking sequence of aligned goat reads were extracted together with the variant and the MAF was calculated as above. These sequences were aligned on the goat scaffolds using BWA .Data from the two pools of meat goats were aligned on cattle UMD3.1 and sheep Oarv2∶0 reference genomes, available respectively on de novo assembled and several stringent filters were applied to resulting contigs to predict SNPs, as described in SNPs were also detected in RNASeq projects (see acknowledgements section) conducted either on gonads (GENIDOV programme) or mammary gland (LGS programme). Sequencing data generated by 454/Roche platform were http://www.ensembl.org/, release 64) and were blasted Finally, previously published SNP in genes of interest  were added. Briefly, the genomic sequence of cattle CSN1S1, CSN2, CSN1S2, CSN3, PRP and DGAT1 genes were retrieved from ensemble genome browser  and the technical quality of the SNP for use on Illumina BeadChips  were assessed.For all the detected SNPs, 120 bp of flanking sequences were extracted. Using Categories were defined to characterize the variant:1: SNP detected in an EST.2: two alleles detected in the five considered breeds.3: two alleles detected in Alpine and Saanen and Creole and (Boer or Katjang/Savanna).4: two alleles detected in two of the three milk and mixed breeds  and in Boer and Katjang/Savanna.5: two alleles detected in Alpine and Saanen and Creole.6: two alleles detected in three out of the five breeds.10: two alleles detected in each of the two milk breeds (Saanen and Alpine).11: two alleles detected in one milk breed (Saanen or Alpine) and one meat breed (Creole or Boer or Katjang/Savanna).12: two alleles detected in at least two meat breeds (Creole and Boer or Katjang/Savanna).13: two alleles detected in one milk breed (Saanen or Alpine).20: other SNPs.90: INDELs.For all the detected variants, the following characteristics of the detected SNP were stored in an in-house database:Goat scaffold nameGoat scaffold positionBase in the goat reference genome and in all the analyzed breeds2 by 60 bp of flanking sequenceEstimated MAF (Minimum Allele Frequency): approximated here by counting the number of reads for each alleleIllumina Assay Design Tool (ADT), using the closest to goat available genome as a reference: cattle; ranging from 0 to 1 and evaluating the probability of success of the SNP assayIllumina SNP score: as determined by Illumina SNP type: I (A/T or G/C) requiring two two probes or II (other SNPs) requiring only one probeDistance to the previous and the next SNP (bp)The following criteria were used to select a first list of SNPs:Illumina score >0.8, Infinium II SNP. This list was ranked using increasing category and decreasing MAF.scaffold >10,000 bp, biallelic, minimum 20bp each side without another variant, MAF >0.2, An in-house algorithm was used to select a SNP list maximizing both the rank criteria presented before and the genome coverage. The algorithm uses the rank sorted SNPs and the scaffold length array as inputs. During the process it fills an array storing starts and end positions of already covered genome locations and populates the list of selected SNPs. The algorithm checks a SNP at each cycle. For each SNP, it first checks if it is located in an already covered area. In this case, it processes the next SNP. If the SNP is located in an uncovered area, the flanking area  is added to the covered positions array and the SNP is added to selected SNP list. Several flanking area sizes were tested. The one which was chosen: 28,500 produced a list of close to 59,000 SNPs.The remaining 1,000 SNP were manually chosen to cover large genomic regions which did not include highly ranked SNPs.BWA .SNP flanking sequences were aligned on cattle UMD3.1 assembly using The final 60,000 selected SNP list was submitted to Illumina.To assess the quality of the SNP chip, a set of 288 DNAs  were used. It included most of the animals that had been used for SNP discovery and the animal used for reference genome sequence. Additional animals were added to represent genetic diversity of international goat breeds. This breed set included 53 Alpine, 26 Angora, 30 Boer, 38 Creole, 16 Jinlan, 15 Katjang, 59 Saanen, 20 Savanna, 27 Skopelos and 1 Yunling goat, with four trios. Genotyping of these 288 DNAs was used to assess the performance of the successfully synthesized SNP assays and generate cluster files.Table S1PRNP, casein and DGAT1 orthologous regions on cattle and goat genomes. For each gene, chromosome number and location are given on cattle UMD3.1 assembly and scaffold number and location are given on goat scaffolds.(XLSX)Click here for additional data file.Table S2SNPs in PRNP, casein and DGAT1 genes. For each SNP, exon number, flanking sequence and variant, scaffold name, position on the scaffold and ss number in dbSNP are indicated.(XLSX)Click here for additional data file.File S160,000 SNP chip design. This “CAPRI-BATCH1” file contains information on the 60,000 selected SNPs as it was submitted to dbSNP (http://www.ncbi.nlm.nih.gov/projects/SNP/snp_viewBatch.cgi?sbid=1057128). The submission includes 60bp flanking sequences on each side of the variant site, MAF value, Illumina_score, Illumina_state (successful or not) and position of the variant on goat scaffolds (Chinese_assembly_v1 column) and goat CHIR_1.0 assembly, (Chinese_assembly_v2_pseudo column), Sheep_OARV2 and Bovine_UMD3.1.(GZ)Click here for additional data file.File S260,000 SNP ss names. This file contains the correspondence between SNP name in File S1 and NCBI_ss# name for the 60,000 selected SNPs.(GZ)Click here for additional data file.


P<0.01. We identified that in several chromosome regions, multiple suggestive interactions group to form a block-like signal, effectively reducing the rate of false discovery. The strongest cluster of interactions involved the CREB5 gene and a SNP rs1607979 on chromosome 17q22 (P = 9.86×10−11) which also show trans-expression quantitative loci (eQTL) association in Chinese population. We then detected a complicated cis-interaction pattern around the NPC-associated HLA-B locus, which is immediately adjacent to copy-number variations implicated in male susceptibility for NPC. While it remains to be seen exactly how and to what degree SNP-SNP interactions such as these affect susceptibility for nasopharyngeal cancer, future research on these questions holds great promise for increasing our understanding of this disease’s genetic etiology, and possibly also that of other gene-related cancers.This study is the first to use genome-wide association study (GWAS) data to evaluate the multidimensional genetic architecture underlying nasopharyngeal cancer. Since analysis of data from GWAS confirms a close and consistent association between elevated risk for nasopharyngeal carcinoma (NPC) and major histocompatibility complex class 1 genes, our goal here was to explore lesser effects of gene-gene interactions. We conducted an exhaustive genome-wide analysis of GWAS data of NPC, revealing two-locus interactions occurring between single nucleotide polymorphisms (SNPs), and identified a number of suggestive interaction loci which were missed by traditional GWAS analyses. Although none of the interaction pairs we identified passed the genome-wide Bonferroni-adjusted threshold for significance, using independent GWAS data from the same population (Stage 2), we selected 66 SNP pairs in 39 clusters with  Nasopharyngeal carcinoma  mainly occurs in ethnically Chinese populations living in Southern China, Hong Kong, and Taiwan HLA-A, HLA-B, and HLA-C, as well as RAD51L1, MDM2, TP53, and MMP2 genes HLA-A, GABBR1, and HCG9 genes, as a consistent NPC-susceptibility locus HLA-A and HLA-B genes, the association signals occur in the antigen-recognition groove Genes associated with NPC include class I genes of the major histocompatibility complex (MHC), such as Although GWAS analysis has linked different medical disorders with thousands of genetic variants HLA-C interaction with ERAP1 with susceptibility for psoriasis HLA-B27 interaction with ERAP1 with susceptibility for ankylosing spondylitis Interaction analysis successes include associating Because analyzing for genetic interactions throughout the genome imposes a heavy computational burden, most epistasis investigators try to narrow their focus. Therefore, in the discovery stage, prior to the initiation of interaction analysis, investigators typically prioritize single nucleotide polymorphisms (SNPs) according to the significance of their marginal effects cis-interactions within MHC region. Since this region is linked with immunity and susceptibility for NPC, this finding clearly merits further functional analysis. Our study shows how targeted analysis of GWAS data can be used to uncover interactions between and among genes, providing new avenues for research into the genetic etiology of NPC.In this study, we used a genome-wide analytical approach to identify possible SNP-SNP interactions involved in the development of NPC. Many of the interacting pairs of SNPs were analyzed using data drawn independently from an ethnically similar population. We found that in several chromosome regions, many suggestive interactions group together to form a block-like signal, effectively reducing the rate of false discovery. In addition to identifying several regions where multiple signals interact, we also discovered a complicated pattern of P values<1.00×10−4.We conducted a genome-wide two-locus analysis of SNP-SNP interactions for associations with NPC using our previous NPC GWAS data set 11 statistical tests, however, none of the 66 pairs of SNP-SNP interactions with P values<1.00×10−8 identified by our initial analysis achieved a genome-wide level of significance of Pinteraction≤4.34×10−13. The strongest interaction (Pinteraction = 1.97×10−10) detected was between SNPs rs17233815 and rs10871618. .When subjected to 1.15×10Psingle>0.05). Most of the stronger interacting SNP pairs contained SNPs with moderate single-locus effects paired with SNPs with small single-locus effects. The highest single-locus association P value we observed occurred in the pair rs1884008 (Psingle = 7.87×10−1) and rs4561414 (Psingle = 1.09×10−5), which produced a moderate interaction P value (Pinteraction = 2.39×10−7).We further observed that 99.72% of the top 10,000 SNP pairs identified as interacting in our Stage 1 dataset contained one SNP that produced an only small single-locus effect  that interacts with SNPs in a ‘gene desert’ area of 8q24.In Stage 1, the interaction SNP pairs tended to be grouped together in clusters. Our top 100 interaction pairs, for example, contained 16 such clusters. The largest clustering of interaction pairs (19 out of 100) involved MHC-region SNPs interacting with SNPs in nearby Table S1). In our Stage 2 analyses, the lowest P value achieved (Pinteraction = 1.68×10−5) was from an interacting SNP pair ranked 8761st in the Stage 1 analysis. All of the top 100 interaction pairs we had initially identified failed to replicate (Pinteraction>0.01) in Stage 2.To test our initial results, we analyzed a smaller NPC dataset, collected independently from an ethnically similar population, for the top 10,000 interacting SNP pairs identified in Stage 1. Of these 10,000 interacting pairs, 467 (4.67%) could not be tested in the Stage 2 dataset, possibly due to differences in the genotyping platforms that caused SNPs included in the Stage1 dataset to be omitted from the Stage 2 dataset , keeping only results above the permutation P value threshold (Ppermutation<0.01).In the second stage of our study, we sought to increase the power of our analysis by increasing the size of our sample. We therefore combined data from our Stage 1 and Stage 2 datasets and analyzed for SNP-SNP interaction in the 10,000 top pairs identified from Stage 1. All the SNP pairs in the second stage failed to achieve Bonferroni corrected p-value<0.05 threshold, therefore, we only selected SNP pairs with interaction Due to the limited sample size, we recognized that our study could be under-powered and therefore is likely to have missed many true positives. Our study can achieve the power of 0.92 when using Epistasis Power Calculator suggested by PLINK; which were very similar to the result of two previous publications Table S2). Of even greater interest is the fact that these 66 potentially interacting SNP pairs cluster into 39 interacting groups (Table S3), of which 12 (31%) are associated with at least two nearby SNPs from one of the interacting chromosomes.Although the power achieved in these analyses was unlikely to allow for solid conclusions, we were able to identify 66 interacting SNP pairs of potential interest for further analysis .CREB5 intron) and rs1607979 (566 kb upstream of KIF2B), with interaction Pcombined = 9.86×10−11  and CC/AA  for both SNPs are, in fact, at higher risk for developing NPC . Within the CREB5 intron, we identified another SNP, rs2237361, interacting at relatively high levels (Pcombined = 7.44×10−10) with rs1607979. These two more interactive SNP pairs joined with nearby SNPs to produce a block-like signal with strong LD . The expression quantitative loci (eQTL) analysis between rs1607979 genotype and CREB5 expression (performed using Genevar trans-eQTL associations (P<0.05) in Han Chinese populations .The strongest levels of interaction identified by our initial analysis occurred between rs2237353  interact with SNPs in two adjacent genes . In C5 and CNTRL, these interacting SNPs formed two separate blocks that appear to interact with WBSCR17 independently .Our analysis also identified interaction between chromosome 7q11 and 9q33. As shown in S2, and S3).Previous GWAS studies have shown that SNPs located within the chromosome 6p MHC class I region can profoundly affect susceptibility to NPC cis-interaction. Three SNPs  near C6orf15 interacted with two upstream SNPs (rs2523849 and rs2523864) near HCG22. Although the level of interaction did not reach statistical significance, SNPs located near C6orf15 also interacted with SNPs downstream of chromosome 6.  for the association between SNPs with increased interaction in this region and NPC susceptibility (Table S4). The complicated cis-interaction patterns we detected may therefore reflect some type of haplotype effect.In each case, all of the SNPs located within this small MHC region formed an interaction block characterized by complicated patterns of cis-interaction region identified by our analysis contains (1) a SNP (rs879882) upstream of the POU5F1 gene that interacts a SNP (rs7770216) close to the HLA-B gene, and (2) two SNPs (rs7761965 and rs2596501) downstream of the HLA-B gene that also interact with each other . Results from haplotype analysis suggest a potentially association with NPC (Pcombined = 2.00×10−6), (Table S5).Immediately downstream of this small MHC region, another HLA-DQ/DR gene, was excluded because it had not been included in the genotyping platform of our Stage 1 dataset.) When tested in our Stage 1 dataset, all 18 SNPs obtained good interaction P values . Most interacting counterparts of GWAS-significant SNPs, however, were located outside the MHC class I regions where single-locus GWAS P values were non-significant (Psingle>0.05). In chromosome 21, for example, both GABBR1 and HLA-F genes interact with SLC37A1 while the HLA-A gene interacts with RTCD1 (in chromosome 1), KCNMA1 (in chromosome 10), PGM2L1 (in chromosome 11), and NUDT7 (in chromosome 16). Interesting as the findings potential are, however, most failed to replicate when subjected to further analyses in the Stage 2 (Pinteraction>0.05) and combined (Pinteraction>1.00×10−4) datasets (Table S5), and we found no significant cis-interaction signal in the NPC-associated region near the HLA-A gene . Our analyses have therefore failed to identify interactions in the GWAS-significant MHC class I region rising to the level of significance.We next analyzed the genome-wide interaction profile of 18 significant SNPs identified in previous GWAS analyses To explore SNP-SNP interactions implicated in NPC, we divided genome-wide SNPs into subsets and performed a full pairwise scan. In this way we generated a complete profile of SNP-SNP interactions associated with NPC, which we analyzed in two different stages.cis-interaction close to an HLA-B locus known to be important for NPC susceptibility, none of the interaction pairs identified rise to the level of genome-wide significance.Our analyses revealed a number of interaction regions, each containing many interactive pairs of SNPs. The multiplicity of interaction signals produced per region reduced our likelihood of false discovery. Although we also identified two regions of P value identified in this study (9.86×10−11) failed to achieve the Bonferroni-adjusted threshold for genome-wide significance  may be due to the limited size of our sample. It has further been suggested that–considering the LD structure among SNPs–the cutoff for genome-wide significance in SNP-SNP interaction analysis of approximately 500,000 SNPs should rather be Pinteraction≤4.2×10−11Pinteraction = 9.86×10−11) comes close to achieving significance.The fact that the strongest interaction Ppermutation<0.01 in the 10,000-permutation test in all 3 analytic conditions and datasets . However, the permutation P values are not corrected ones and should be interpreted carefully.Still another suggestion is that rather than the conservative Bonferroni correction for multiple testing, a permutation-based strategy should be used to verify interaction analyses In addition, most of the interactions we identified grouped together to form stronger, block-based signals. The detection of many potentially interaction SNPs within a region of strong LD indicates that these interactions are neither sporadic nor the spurious artifacts of genotyping. Other genome-wide interaction-based association analyses have reported similar interaction patterns Because the assessment of all pairwise interactions demands extensive computational resources, researchers generally prioritize which SNPs identified by GWAS are to be subjected to further testing Table S5), and our highest interacting pairs all displayed small, single-locus effects .Another recognized issue is the number of interaction pairs selected for validation analysis. Most other SNP-SNP interaction studies that have failed to replicate their findings selected only a few top interaction pairs for validation. One prostate cancer study, for instance, conducted validation tests on only 16 of 1,325 pairs of top SNP-SNP interactions CREB5 intron SNPs (rs2237353 and rs2237361) and a SNP (rs1607979) located in a gene desert region of 17q22, where the nearest gene (KIF2B) is 566 Kb away (CREB5 is a member of the cAMP-responsive element (CRE)-binding protein family implicated in tumorigenesis in acute myeloid leukemia and prostate cancer.Our pair-wise genome-wide search of interacting SNPs revealed several suggestive two-locus associations Table 1.CREB5 regions is not currently documented and we know little about the molecular function of the 17q22 region, our finding suggests a possible regulatory function for this locus that need be validated using molecular experiments. Studies have already associated the gene desert locus of 8q24 with increased susceptibility for prostate, colorectal, and breast cancer; and subsequent molecular experiments have revealed a tissue-specific long-range cis-interaction between this region and the proto-oncogene MYCMYC, the trans-regulation of CREB5 may well lie in region 17q22. Further support for this hypothesis comes from the eQTL analysis of a HapMap3 Chinese population, which indicates a suggestive association between rs1607979 and CREB5 expression . In addition, the provirus integration site for xenotropic murine leukemia virus-related virus (XMRV), an infectious retrovirus associated with a predisposition for prostate cancer, has also been mapped to CREB5CREB5, and 17q22 in nasopharyngeal cancer are therefore well worth further investigation.Although interaction between the 17q22 and WBSCR17 intron and the chromosome region in 9q33 that contains C5 and CNTRL  in the formation of lamellipodia and the regulation of macropinocytosis C5 is involved in the formation of the membrane-attack complex CNTRL encodes a centrosomal protein required for abscission mediated by secretory vesicles Another plausible interaction identified in this study involves the GABBR1, HLA-F, HCG9, and HLA-A genes HLA-B gene Previous genetic studies have described the MHC region, especially MHC class I genes, as the major susceptibility locus for NPC cis-interaction pattern in the chromosome 6p21 region (30.97–31.45 Mb) located near the HLA-B gene. In fact, two cis-interaction clusters can be found in this region. In region 1, SNPs downstream of C6Orf15 and upstream of HCG22 interact with SNPs between the two genes. In region 2, SNPs rs7761965 and rs2596501 (both located upstream of HLA-B gene) interact with each other, whereas SNP rs7770216 (located downstream of HLA-B), interacts with SNP rs879882 (located upstream of the POU5F1 gene).We also identified a complicated SNP HLA-B gene cis-interaction pattern identified in the MHC region could result from strong LD or haplotype associations with the MHC region. Indeed, since substantial LD occurs in areas where chromosomes interact, the LD effect cannot be ignored , possibly because strong LD may produce proxy effects without affecting SNP-SNP interactions in nearby genes.High-resolution molecular typing of HLA class I genes suggests that in the genes HLA-A gene may be due to haplotype associations within the chromosome region. Increased risk for NPC has so far been associated with HLA-A*0207 and HLA-B*4601, as well as with the extended haplotype: HLA-A*3303-B*5801/2-DRB1*0301-DQB1*0201/2-DPB1*0401HLA-A-B-C haplotypes, which exhibit both a susceptibility effect (HLA-A*02∶03-B*38∶02-C*07∶02) and a protective effect (HLA-A*11∶01-B*13∶01-C*03∶04)HLA-B locus contains interacting SNPs, suggesting that in some cases, cis-interaction might reflect an underlying haplotype effect .Alternatively, the lack of gene interaction in the strong LD region near the trans-eQTL association observed in Han Chinese suggests that rs1607979 may play some role in the regulation of the CREB5 gene. This study extends the spectrum of possible NPC-susceptibility signals. It also identifies a complicated pattern of cis-interaction in the HLA-B locus, which HLA molecular typing, GWAS, and CNV analysis have shown to contain many signals related to NPC susceptibility.In sum, our genome-wide two-locus SNP-SNP interaction analysis provides a feasible approach that, when refined, should increase the potential for successful replication. The This study was reviewed and approved by the Institutional Review Board and Ethics Committee of Chang Gung Memorial Hospital, Taiwan. Written informed consent was obtained from all study participants.Figure S1).This study is a two-stage search for SNP-SNP interactions in persons with nasopharyngeal cancer whose aim is to identify novel loci associated with elevated susceptibility for the disease . For each individual chromosome, we used a “SET1 × SET1” test with set files containing one chromosome SNP set. Due to the limitation of the software setting, covariates such as age and gender were not included in the analysis.A PLINK epistasis analysis (v1.07) The dataset used to test our results from Stage 1 included data from an additional 181 NPC cases and 187 controls. Data were collected independently from subjects unrelated to the earlier set but similarly of Han Chinese descent living in Taiwan. Stage 2 data additionally contain information on family history and clinical outcome as follows: 73 cases were resistant to radiotherapy, 33 cases had a family history of NPC, and 18 cases had distal metastasis. Subjects were genotyped using Illumina Human610-Quad BeadChips from the Illumina-certified service provider Genizon Biosciences . GWAS analysis was conducted using the same data-processing criteria as specified for Stage 1 above. Quality control criteria included low call rate (<99%), failure on PLINK tests for identity-by-state (IBS) or identity-by-descent (IBD), or failure on the EIGENSOFT package P value<0.00005. The quality control process therefore removed 120,113 markers from the original 620,901 markers, leaving 500,788 markers for use in future tests.Nine samples and 14 duplicate samples failed quality control assessments and were eliminated. We also eliminated cases and controls where the SNPs missing data rate was >3% or a minor allele frequency (MAF)<0.1, and controls with a Hardy-Weinberg Equilibrium (HWE) P value. Because a similar genotyping platform was used in both Stages, we could in most cases select exact SNP combinations from the Stage 2 dataset for replication. Using PLINK epistasis analysis, we analyzed the top 10,000 SNP pairs identified from Stage 1 in the Stage 2 dataset.We then pooled all two-locus SNP-SNP interaction results in Stage 1 and ranked them by interaction P values for the top 10,000 interaction pairs identified in Stage 1.Stage 1 and Stage 2 data sets contained 464 cases and 478 controls. Following the sample quality control process, samples that failed to pass the call rate (<99%), IBS, IBD, or PCA tests were removed. This left a combined sample with data from 454 cases and 477 controls. We then used this combined dataset to calculate interaction P values were calculated using the permuted phenotypes. Permutation P values were calculated for each interaction pair as Ppermutation = (b+1 )/(m+1 ), where b was the number of permutations yielding a pseudo-interaction P value at least as extreme as that observed using the original data, and m was the number of permutation tests.Permutation test was performed 10,000 times using permuted phenotype sets generated by “–make-perm-pheno 10,000” command in PLINK. Pseudo-interaction To examine the haplotypes of interacting SNPs, we used the R (version 2.13.1) Figure S1Data-processing flowchart. GWAS data were divided into 24 sets according to chromosomal position. PLINK epistasis analysis by pairing two sets on different chromosomes or one set on one chromosome. The 10,000 pairs with the highest interaction scores were then tested in independent GWAS samples, and the 66 most suggestive interaction pairs were selected.(PDF)Click here for additional data file.Figure S2Odds ratios for the top 10 interaction pairs and pairs in the MHC region. X and Y axes show interacting SNP genotypes. Z axis shows SNP pair odds ratios estimated relative to the baseline double-homozygote of major alleles in the combined data set.(PDF)Click here for additional data file.Figure S3(lower left and upper-right). In the single-locus signal plots, red is the position of suggestive-interacting SNP pairs in the corresponding regions. In the single-locus signal plots, solid black dots denote logistic regression test P values. In the interaction-signal heatmap plot, interaction P values, transformed by a negative logarithm, are coded by color (key in upper-left box). Interaction signal heatmap and LD plot (right panel). Heatmap is aligned with each SNP equidistant from LD plot positions. Interaction-signal heatmap and LD plots are color coded (key upper right). Single-locus signal plots (lower left) same as above, except that coordination is not based on chromosome position. In detailed LD structures estimated in control samples (upper left and lower right) increasing intensities of red represent higher D’ values, and solid red triangles denote the positions of potentially interacting pairs of SNPs. Genes annotated in this region are also depicted according to their relative positions on the LD plot as follows: The top 10 interactions regions selected, shown in Regional signal plots of all SNPs within 100 kb of the suggestive interactive SNP pairs identified. In each figure, the left panel shows the interaction signal heat-map and single-locus signal plots. The heat-map is aligned by chromosome positions based on NCBI build 36. Single-locus signal plots with gene annotations (PDF)Click here for additional data file.Figure S4HapMap3 eQTL analysis between rs1607979 genotype and CREB5 gene expression profile. Genevar (PDF)Click here for additional data file.Table S1Top 10 000 SNP pairs identified in Stage 1 analysis ranked by interaction significance.(PDF)Click here for additional data file.Table S2Suggestive interactions associated with NPC susceptibility.(PDF)Click here for additional data file.Table S3Suggestive regions associated with NPC susceptibility.(PDF)Click here for additional data file.Table S4LD analysis of SNPs in interacting region.(PDF)Click here for additional data file.Table S5Haplotype analysis of the interaction regions located in the MHC region.(PDF)Click here for additional data file.Table S6Interaction P values for SNP pairs identified by other GWAS studies.(PDF)Click here for additional data file.


ANK3 as a susceptibility gene for bipolar disorder (BP). We examined whether epistasis with ANK3 may contribute to the “missing heritability” in BP. We first identified via the STRING database 14 genes encoding proteins with prior biological evidence that they interact molecularly with ANK3. We then tested for statistical evidence of interactions between SNPs in these genes in association with BP in a discovery GWAS dataset and two replication GWAS datasets. The most significant interaction in the discovery GWAS was between SNPs in ANK3 and KCNQ2 (p = 3.18 × 10−8). A total of 31 pair-wise interactions involving combinations between two SNPs from KCNQ2 and 16 different SNPs in ANK3 were significant after permutation. Of these, 28 pair-wise interactions were significant in the first replication GWAS. None were significant in the second replication GWAS, but the two SNPs from KCNQ2 were found to significantly interact with five other SNPs in ANK3, suggesting possible allelic heterogeneity. KCNQ2 forms homo- and hetero-meric complexes with KCNQ3 that constitute voltage-gated potassium channels in neurons. ANK3 is an adaptor protein that, through its interaction with KCNQ2 and KCNQ3, directs the localization of this channel in the axon initial segment (AIS). At the AIS, the KCNQ2/3 complex gives rise to the M-current, which stabilizes the neuronal resting potential and inhibits repetitive firing of action potentials. Thus, these channels act as “dampening” components and prevent neuronal hyperactivity. The interactions between ANK3 and KCNQ2 merit further investigation, and if confirmed, may motivate a new line of research into a novel therapeutic target for BP.Genome-wide association studies (GWAS) have implicated  ANK3 . A subsequent community-wide effort to combine GWAS from existing studies of BP, referred to as the Psychiatric GWAS Consortium (PGC), identified several additional susceptibility loci and provided further evidence for the association with ANK3   offer an unbiased, high-throughput approach for searching the entire genome to identify disease-causing variants. This approach has generated much enthusiasm in the study of complex disorders such as bipolar disorder (BP) that have been more difficult than Mendelian diseases to genetically map with linkage and candidate gene association approaches. However, there was little agreement in the findings from the initial GWAS of BP  sample, and the Translational Genomics Research Institute Bipolar Disorder (TGEN) sample. The methods for collecting, diagnosing, and genotyping these samples have been described elsewhere  bipolar disorder sample and a German bipolar disorder sample. Methods for collecting, diagnosing, and genotyping both these samples have been described elsewhere . Then, we analyzed the GAIN-TGEN dataset for statistical evidence of gene–gene interactions associated with BP between ANK3 and genes encoding the proteins identified by STRING. We used logistic regression to test all possible pair-wise interactions between SNPs in ANK3 and SNPs in each of the STRING-identified genes, with the gene boundaries defined according to the most inclusive RefSeq transcript (hg18) ±10 kb to include potential regulatory regions. The logistic regression models included terms for each SNP entered as allelic dosages representing the estimated number of copies of the minor allele and an interaction term between the two SNPs. The models also included terms to control for study site and the first two principal components (PCs) from a principal components analysis (PCA) using Eigenstrat . We used the same logistic regression approach in the analysis of the replication samples as was used in the discovery sample.We attempted to replicate any SNP pairs that passed the permuted L1CAM and DMD) are located on chromosome X and were therefore excluded from the analysis, leaving 14 genes of interest . In total, 31 SNP pairs between KCNQ2 and ANK3 were significant after permutation.A query of the STRING bioinformatics database yielded a total of 16 known proteins that had strong biological evidence (confidence score > 0.7000) for interacting with ANK3. Two of these genes (BP Table . KCNQ2 wKCNQ2: rs2282150 (an intronic SNP) and rs2297385 (a synonymous coding SNP), both of which are in perfect LD (r2 = D′ = 1.0) with each other. These two SNPs interacted with 16 unique ANK3 SNPs that were all intronic . However, a cluster of these ANK3 SNPs fell within the boundaries of ankyrin repeat domains of the protein, which are known to mediate protein–protein interactions.Interestingly, the most significant SNP pairs clustered within a distinct peak Figure , and werp = 0.015–0.048) in the WTCCC sample. The remaining three were suggestive (p = 0.052–0.078). None of these SNP pairs were significant in the German dataset. However, interestingly, the two driving SNPs from KCNQ2 were both involved in significant interactions with five other SNPs in ANK3 (p = 0.033–0.039), suggesting the possibility of allelic heterogeneity. One of these ANK3 SNPs (rs10994406) is just over 10 kb away from the ANK3 SNP (rs10994397) implicated in the recently reported PGC mega-analysis of BP , a rare autosomal dominant inherited form of epilepsy  , expression studies , or epigenetic studies . These results may be further probed experimentally via animal models. If the interactions of ANK3 with KCNQ2 can be confirmed to play a role in the etio-pathogenesis of BP, it would motivate a new avenue of research into a novel therapeutic target for this disease. This would be advantageous because KCNQ2/KCNQ3 channels have been the focus of extensive pharmacologic research in relation to epilepsy, and several compounds that act either as activators or inhibitors of the complex have already been identified and are in various stages of clinical investigation (Miceli et al., As far as we are aware, this is the first comprehensive effort to search for evidence of gene–gene interactions with The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Next generation sequencing technologies allow to obtain at low cost the genomic sequence information that currently lacks for most economically and ecologically important organisms. For the mallard duck genomic data is limited. The mallard is, besides a species of large agricultural and societal importance, also the focal species when it comes to long distance dispersal of Avian Influenza. For large scale identification of SNPs we performed Illumina sequencing of wild mallard DNA and compared our data with ongoing genome and EST sequencing of domesticated conspecifics. This is the first study of its kind for waterfowl.More than one billion base pairs of sequence information were generated resulting in a 16× coverage of a reduced representation library of the mallard genome. Sequence reads were aligned to a draft domesticated duck reference genome and allowed for the detection of over 122,000 SNPs within our mallard sequence dataset. In addition, almost 62,000 nucleotide positions on the domesticated duck reference showed a different nucleotide compared to wild mallard. Approximately 20,000 SNPs identified within our data were shared with SNPs identified in the sequenced domestic duck or in EST sequencing projects. The shared SNPs were considered to be highly reliable and were used to benchmark non-shared SNPs for quality. Genotyping of a representative sample of 364 SNPs resulted in a SNP conversion rate of 99.7%. The correlation of the minor allele count and observed minor allele frequency in the SNP discovery pool was 0.72.We identified almost 150,000 SNPs in wild mallards that will likely yield good results in genotyping. Of these, ~101,000 SNPs were detected within our wild mallard sequences and ~49,000 were detected between wild and domesticated duck data. In the ~101,000 SNPs we found a subset of ~20,000 SNPs shared between wild mallards and the sequenced domesticated duck suggesting a low genetic divergence. Comparison of quality metrics between the total SNP set  and the validated subset shows similar characteristics for both sets. This indicates that we have detected a large amount  of accurately inferred mallard SNPs, which will benefit bird evolutionary studies, ecological studies (e.g. disentangling migratory connectivity) and industrial breeding programs. Anas platyrhynchos) is the world's most abundant and well-studied waterfowl species. Besides being an important game and agricultural species, it is also a flagship species in wetland conservation and restoration. Waterfowl (Anseriformes: Anatidae) and especially ducks are focal organisms in long distance dispersal of Avian Influenza in the wild [The mallard . Being the third most consumed species on the poultry market globally [As a general pattern, mallards breeding in temperate areas migrate from northern breeding grounds to more southerly wintering areas avoiding freezing conditions at breeding sites . Howeverglobally , the domglobally ,14. TherMeleagris gallopavo) [Parus major) [A general limitation in developing a SNP-set in non-model organisms has been the unavailability of extensive genomic sequence information from multiple individuals that represent a sufficient portion of the genetic variability of the population or species under study. However, the Illumina sequencing technology -17 couplllopavo)  and greas major) . Also ins major) , pig [22s major)  and catts major)  next gende novo assembly was in progress and almost completed by the Beijing Genome Institute (BGI). This allowed for SNP discovery by next generation sequencing of an RRL of pooled wild mallard samples and mapping locations of almost 13 million of the resulting reads to a draft mallard reference sequence. Identified SNPs were compared with those observed within the reference genome sequence of domestic duck  and EST sequencing  resulting in more than 20,000 shared high quality SNPs. A set of putative SNPs can contain large numbers of incorrectly inferred SNPs  and thus we also aimed to estimate the quality of our set. Quality, here, is a measure of the reliability of the SNP set. This includes not only the percentage of false SNP inferences but also evaluation of the way in which these SNPs will be usable for many purposes; i.e., if they cover a large spectrum of minor allele frequencies, or if these were reliably inferred by our analyses .Here, we describe the discovery of more than 180,000 novel SNPs in the genome of the mallard, which currently lacks a published sequenced genome. Lacking this reference genome we initially aimed for paired-end sequencing on an Illumina Genome Analyzer of an RRL of fragments in the size range of 110-130 base pairs (bp) and with a read length of 76 bp. This would create an overlap between the forward and reverse DNA sequence reads of continuous sequences, permitting the reads to be merged. This in turn helps in providing sufficient flanking sequence (i.e. DNA sequence on either side) of a SNP which is a requirement for genotyping and is hard to retrieve in the absence of a reference genome. However, at the time when our study had started, genome sequencing of the domestic duck genome and AluI and one by digestion with HhaI, representing 4% and 1% of the mallard genome, respectively.We targeted for a sequencing depth of about 40 times at limited sequencing cost by sequencing a fraction, representing 5% of the mallard genome (reduced representation library (RRL) approach). Restriction enzymes were screened for suitability for RRL construction, with the goal of a 20-fold complexity reduction of the mallard genome within the targeted size range of 110-130 bp. Restriction enzyme analyses showed that these requirements are met by combining two RRLs, one created by enzymatic digestion with in silico digest of the chicken genome, which is very similar [AluI, but only 0.2% for HhaI (data not shown). We prepared two pooled DNA samples of nine wild mallard individuals from three locations across Europe. To prepare the RRLs, we digested these samples with AluI or HhaI and isolated fragments in the 110-130 bp size range from a preparative polyacrylamide gel. The genomic libraries were combined in the sequencing sample preparation procedure. Due to a lack of a reference genome we aimed for paired-end sequencing on an Illumina GAII of the combined RRLs and a sequence read length of 76 bases. This created an overlap between the forward and reverse reads of a pair which allows merging of the reads. Merging the reads helps in providing sufficient flanking sequence of a SNP. This sequence is necessary for genotyping and is hard to retrieve in the absence of a reference genome. Merged paired reads, possibly supplemented with single reads, are subsequently clustered for SNP discovery.An  similar ,26, predWe generated 34.8 million 76 bp reads using three sequencing lanes on an Illumina GAII of which two lanes were run in paired-end mode. The raw data files from the sequencing instrument are deposited in the NCBI short read archive under accession number SRA024498. It was shown that a phred quality score  thresholMore than 98.8% of the SNPs were flanked by at least 40 bp on either side and met the requirements for probe design constraints for all genotyping platforms whereas all SNPs met the flanking sequence requirements for an iSelect (Illumina) genotyping assay. For the 2,565 SNPs that showed more than two alleles, we only considered the most frequently observed minor allele because tri- or tetra-allelic SNPs are very rare  and it iSequencing errors are more abundant in the tails of next generation sequencing reads and are thought to cause an excess of false SNP predictions. An increase in the number of SNPs towards the end of the reads is expected if sequencing errors are the cause of a substantial number of predicted SNPs in the dataset. To validate our sequence filtering and SNP detection constraints we plotted the distribution of the SNPs over the 62 positions in the sequence reads Figure . PositioBecause of the length of the RRL fragments (~110-130 bp), there is an overlap between paired forward and reverse reads (62 nucleotides each) from position 48 onwards. This overlap results in a higher sequence depth and a tiny increase in the number of SNPs being detected at these nucleotide positions Figure .We estimated the possible errors in SNP calling due to sequencing errors by looking at transition (TS) - C/T pyrimidine to pyrimidine or A/G purine to purine changes - versus transversion (TV) ratios, which are all the four other possible pairs of changes. Random mutations or sequence differences due to errors should give a TS:TV ratio of 1:2. In reality, a bias due to a higher rate of C => T mutations due to the deamination of methylcytosines in CpG dinucleotides induce a much higher TS rate -35. For Sequencing errors were also evaluated per read position by plotting the TS:TV ratio observed over the 62 positions in the sequence reads , covering both chromosomes of a single individual, resulted in the identification of 2,826,871 putative SNPs . Domestic duck EST sequencing identified a total of 6,456 SNPs (further referred to as d-EST) in protein coding regions of the genome .The To benchmark d-RRL we compared it with these two external and independent datasets and identified SNPs that are shared with either d-WGS or d-EST. We observed 20,180 SNPs (16.5%) in common between d-RRL and SNPs in the d-WGS dataset. Furthermore d-RRL had four SNPs in common with d-EST whereas d-WGS shared 244 SNPs with d-EST Figure . Only a Although reduced, also d-Shared showed a peak of the SNP distribution on read positions three to six, as we observed in d-RRL. However, TS:TV ratios for these positions were at the expected level of >2.3 indicating that most SNPs in these read positions likely resulted from true nucleotide polymorphisms. Finally, compared to d-RRL, the d-Shared subset of SNPs showed a higher average TS:TV ratio of 2.7 and indicated a relative increase of (C/T) over (A/G) transitions  and assessed the quality of this set of SNPs by plotting the TS:TV ratio per nucleotide position and plotting the distribution of the SNPs over the 62 nucleotide positions in the sequence reads Figure . The disGallus gallus, chicken; divergence time 80-90 million years ago, see discussion section) was used for estimating the physical distribution of the identified SNPs. Common and high quality mallard SNPs (d-Shared) were aligned to the chicken genome and the distribution of this SNP-set was plotted over the chicken chromosomes  of identified SNPs would result in a SNP every ~375 bp. The actual number of true SNPs in the sets d-RRL and d-Between is expected to be lower considering the overrepresentation of predicted SNPs in the read positions one to six together with low TS:TV ratios in these read positions. Also the comparison of d-RRL with d-WGS, in which common true variants remained and false SNPs were discarded, show that SNPs predicted in read positions one to six should be used cautiously. The distribution of d-Shared does not show overrepresentation of SNPs on position one to six. Furthermore, expected TS:TV ratios in d-Shared were observed for positions three to six and expectedly lower TS:TV ratios in position one and two due to the RRL enzyme restriction motif. Therefore we think that a considerable fraction of SNPs in read positions one to six in d-RRL and d-Between are false positives. Because standard sequencing error rates of the Illumina GAII are low (< 0.5%) in the first 20 bases of a read  and thathttp://www.timetree.org)[We showed that d-RRL shares one sixth of the SNPs with d-WGS and an almost negligible number of SNPs with d-EST. ESTs only represent a few percent of the genome, of which only a fraction was sampled by the RRL. Due to this limited shared genome fraction and because SNPs in protein coding regions are rarer than in non-coding regions, a large overlap in SNPs between these sources was not expected. Between d-WGS and d-EST we observed a 2.6 times larger overlap, which can be explained by a more or less complete overlap in sampled genome fraction and a better representation of rare alleles in d-WGS. The relatively large overlap between d-WGS and d-RRL indicates a low genetic divergence between wild mallard and domestic duck. A relatively large fraction of shared SNPs between two independent studies also suggests a low false discovery rate. As stated earlier, the SNPs identified in this study will be used to study mallard population structure and movements in a population genetic framework . Becausetree.org). GenotypWhen performing SNP identification studies using next generation sequence technologies, it is important to know what limitations in sensitivity and specificity can be expected, particularly at low sequence coverage. We show that sensitivity decreases with decreasing base calling quality towards the ends of sequence reads which can be compensated for by increasing the sequence coverage in the ends. SNP distribution and TS:TV ratio over read positions are helpful metrics for the assessment of systematic errors in the sequencing dataset in particular when statistics can be compared to a high quality subset of the data. We showed that the fairly large subset of predicted SNPs that is shared between independent SNP detection studies in wild and domestic duck is likely to represent true SNPs, and suggests a low divergence between these forms.We present for the first time a solid and scalable genotyping environment applicable to mallards and its domestic form. Not only do we provide over 100,000 most reliable SNP markers that can be used in duck breeding and molecular genetics, we also evaluate a sub-set of 384 SNPs for use in ecological genetics. The power of this set combined with relatively low genotyping costs through down-scaling of the marker set will allow long needed studies into the molecular ecology of mallards with regard to various relevant topics, including the study of genetic variation and genetic structure, resolution of unresolved ambiguities of mallard migration systems or inference of both small and large scale movement patterns.AluI or HhaI . The digested pools in O'range loading dye (Fermentas) were size-fractionated on precast 10% polyacrylamide in 1×TBE with the Criterion™ Cell (BioRad). The gel was run 190 minutes at 100 volt and stained for 30 minutes in ethidium bromide solution. After staining, the target fragment size range between 110-130 bp was sliced out of the gel. The gel slice was sheared by nesting a 0.5 ml Eppendorf tube (with a hole in the bottom formed with a needle) containing the gel slice inside a 2 ml Eppendorf tube, and centrifuged at 14000 rpm for 2 minutes. The sheared gel pieces were covered with 300 μl DNA recovery buffer , vortexed, and eluted at 4°C overnight, followed by 15 minutes incubation at 65°C. The slurry was divided over two Montage DNA gel extraction devices (Millipore) and centrifuged at 5000 g for 10 minutes to purify the eluted gel. DNA was precipitated by adding 1/10 volume 3 M sodium acetate pH 5.2, 1 volume isopropanol and 1/500 volume glycogen, washed with ethanol and resuspended in DNA hydration solution (Gentra Systems). The genomic libraries were combined and prepared using the Illumina Sample Preparation kit [Mallard DNA samples were prepared from ethanol preserved whole blood collected from nine individuals from three locations across Europe: two females and a male each from Coto de Doñana (Spain), Northern Netherlands and Ottenby (Sweden). Each of these individuals was either directly caught from the wild, or was a first generation descendant from local wild mallard parents. Ducks were sampled under the approval of the animal ethical committee of Wageningen University; the Spanish Ministry of Environment and Consejeria de Medio Ambiente of Junta de Andalucia; the KNAW  Animal Experiment Commission; and the Swedish Board of Agriculture and its Research Animal Ethics Committee. DNA extraction was performed using the Gentra Systems Puregene DNA purification Kit according to the manufacturer's instructions. Briefly, ~200 μl blood was digested with 9 μg Proteinase K (Sigma) in Cell Lysis Solution (Gentra Systems) at 55°C over night. Proteins were subsequently precipitated with Protein Precipitation Solution (Gentra Systems) and spun down. DNA from the supernatant was precipitated with isopropanol and washed twice with 70% ethanol. DNA quantity and purity were measured using the Nanodrop ND1000. Possible degradation was inspected on an agarose gel and only high quality DNA samples were used to prepare the DNA pool. Equal amounts of DNA from the nine mallards were combined into two pools of 25 μg each. Aliquots of 5 μg for each pool were digested with either tion kit  and sequAluI and HhaI for creating the genomic libraries we expect that the sequence reads start with a 'C'. Therefore, reads not starting with 'C' were discarded as unreliable or contamination. All reads of the sequencing dataset were trimmed from the position where the average quality score dropped below 12. Reads containing a base that was called with a quality lower than 12 were discarded unless an identical copy of the read occurred in the dataset, since it is unlikely that two fragments of such a long sequence of nucleotides are identical by chance. We removed reads that - based on the theoretical raw sequencing coverage of the RRL (38×) - were more than four times overrepresented to limit the number of sequences from repetitive regions in the dataset. This is to prevent the prediction of SNPs within multi-copy genes or other repetitive regions [Prior to analysis we applied quality filters to the raw reads. Due to the use of restriction enzymes  regions ,20.As reference we used a domestic duck genome sequenced by next generation technology by the Beijing Genome Institute . MAQ  was emplWe mapped d-EST SNPs on the genome reference to identify their genomic locations whereas SNPs in d-RRL and d-WGS were predicted on an identical genome reference coordinate system. Mallard SNPs (with on average 116 bp of flanking sequence) being predicted in EST sequences by the group of Alain Vignal  were mapped to the reference genome using GMAP . ResultsTo examine the distribution of SNPs over the genome, we comparatively mapped our predicted SNPs (including 100 bp flanking sequence at each side) to the repeat masked chicken genome (assembly WASHUC2). Mapping was performed using BLAT  with par® Genotyping assay on an Illumina® BeadXpress with VeraCode™ technology. Selection criteria for the SNPs were based on the Illumina design score (above 0.8) and the assayed 384 SNPs should distribute evenly along the chicken genome to minimise the extent of linkage between neighbouring SNPs. Oligo-nucleotides were designed, synthesised, and assembled into oligo pooled assays (OPA) by Illumina Inc. The Illumina OPA file can be found as Additional file Anas platyrhynchos duck species specimens , ~500 wild mallards from Europe, North America and Asia and the nine mallards that made up the SNP discovery panel. Genotyping results were analysed in Genome Studio (Illumina). Using the cor-function in R [SNPs were validated by genotyping an animal panel using the Illumina GoldenGateion in R  the PearHHDK and RHSK designed and carried out SNP detection and drafted the manuscript. RHSK collected, prepared and genotyped DNA samples. HHDK interpreted genotyping results. NL and YH provided the BGI reference genome sequence of the domestic duck including the BGI SNPs and analytical support. AV provided the EST SNPs. MAMG and RPMAC coordinated the research and helped in drafting and revising the manuscript. PvH, JJvdP, JE and HHTP contributed to study design. All authors read, edited and approved the final manuscript.Oligo pooled assay (OPA) data file. This file was used in the genotyping method as indicated in the methods section to generate the raw data. The genotyping assay can be re-ordered from Illumina using this file. Format is plain text, comma separated.Click here for file


M SNPs is determined based on solely the p-value from an association analysis, where M is determined by financial/time constraints. For many studies of complex diseases, multiple analyses have been completed and integrating these multiple sets of results may be difficult. One may also wish to incorporate biological knowledge, such as whether the SNP is in the exon of a gene or a regulatory region, into the selection of markers to follow-up. In this manuscript, we propose a Bayesian latent variable model (BLVM) for incorporating “features” about a SNP to estimate a latent “quality score”, with SNPs prioritized based on the posterior probability distribution of the rankings of these quality scores. We illustrate the method using data from an ovarian cancer genome-wide association study (GWAS). In addition to the application of the BLVM to the ovarian GWAS, we applied the BLVM to simulated data which mimics the setting involving the prioritization of markers across multiple GWAS for related diseases/traits. The top ranked SNP by BLVM for the ovarian GWAS, ranked 2nd and 7th based on p-values from analyses of all invasive and invasive serous cases. The top SNP based on serous case analysis p-value , was ranked 8th based on the posterior probability of being in the top 5 markers (0.13). In summary, the application of the BLVM allows for the systematic integration of multiple SNP “features” for the prioritization of loci for fine-mapping or functional studies, taking into account the uncertainty in ranking.One difficult question facing researchers is how to prioritize SNPs detected from genetic association studies for functional studies. Often a list of the top  M SNPs is determined based on the p-value from the association analysis and carried forward into the next stage of the study, where M is determined by financial constraints. However, this approach is not optimal as rankings are very variable  and the “causative” variant may not be at the top of ranked order of SNPs Many genome-wide association studies of complex diseases and phenotypes have been completed in the last decade M SNPs together where the SNP effects are modeled with both the mean and variance of the multivariate normal distribution depending on prior information. Bayesian analysis of case-control studies using power priors to incorporate historical knowledge was proposed by Cheng and Chen Alternative approaches, that do not prioritize for follow-up based only on ranked p-values, are based on statistical models in which prior knowledge about the SNP can be incorporated into the association analysis, using hierarchical, mixed, or multi-level models In this manuscript, we present a Bayesian latent variable model (BLVM) K SNPs in the association analysis, let θk, k = 1, …, K represent the latent “quality score” for each SNP. We wish to estimate the latent variables θk based on a set of observed features for the SNP, with Xkj representing the jth observed feature for the kth SNP. Some possible features may included: −log10, effect size, minor allele frequency (MAF), function of SNP, previously reported SNP or in a pathway or interest. A model is then specified to associate the features with the latent variables. One possible (simple) model is as follows: th continuous feature for the kth SNP, For i are typically taken to be independent standard normal distributions, N. To ensure unique labeling, one can impose strong or constraint priors for a few of the R = diagonal matrix consisting of 1). Another choice for modeling the dependency in the SNP quality scores would be to model the dependency between the latent SNP quality scores as a function of LD or spatial distance ij representing the distance between the two SNPs .To complete the model specification, prior distributions are then placed on all parameters in the model. To ensure proper posterior distributions, proper prior distributions, as opposed to improper prior distributions, are placed on all parameters in the model Ovarian cancer is the fourth leading cause of cancer death among women in the United States. In 2009, it is estimated that 21,550 new cases will be diagnosed in the United States, and 14,600 women will die from the disease The ovarian cases from the US GWAS that will be used for illustration of the latent variable model for ranking SNPs includes four North American studies: (1) FOTS, a population-based study from Ontario 1995–1999, (2) MAYO, a clinic-based study of cases and matched controls in the American upper Midwest 1999–2007, (3) NCOCS, a case-control study covering 48 counties in North Carolina, and (4) TBOCS, a population-based study conducted in Tampa, Florida. The study protocol was approved by the institutional review board at each center , and all study participants provided written informed consent. Eligibility for cases is confirmed epithelial ovarian cancer  with invasive disease . Eligible controls are matched within each study to cases on age, race and residence. All cases and controls were additionally required to have adequate DNA, no prior history colorectal cancer at age less than 50, and no prior history of ovarian, breast, endometrioid cancer; in addition, known non-Caucasian, Jewish, Hispanic, and related participants were excluded. After all samples were genotyped using the Illumina Infinium Human610-Quad BeadChip and quality control had been completed, a total of 1,815 ovarian cancer cases  and 1,900 controls were available for association analysis.Analysis for association of genetic markers with cancer status , along with subtype analysis of invasive serous ovarian cancer cases versus controls, was completed using PLINK software f = −logit, g(MAF) = logit(2*MAF), h(OR) = log(OR) if OR>1 and log(1/OR) if OR<1. The transformation selected for the odds ratios resulted in making the effects all in same direction (“risk”) with the log transformation allowing h(OR) to be modelled with a normal distribution censored at 0. Since MAF is between 0 and 0.5, we double the MAF to get a value that ranged between 0 and 1 for the logit transformation that allowed for modeling g(MAF) with a normal distribution. Lastly, we chose to transform the p-values using the minus logit with subsequent modeling of f with a normal distribution. These transformations also allow use of set constraints on the latent variable model to allow for identifiability of the model parameters, with large values for features , g(MAF) and h(OR)) indicating larger SNP quality scores.Below, we outline five specific latent variable models for the ranking of SNPs which were applied to the ovarian cancer GWAS. The five BLVMs for prioritization of SNPs involve the following SNP “features”: Minor allele frequency (MAF), p-value and odds ratio (OR) from analysis involving all cases and p-value and OR from analysis involving a subset of the cases . All features were first transformed such that “large” values of the factor would result in a “large” SNP quality score. In addition, transformations for the various features were selected such that they could be modeled using a normal distribution (for speed in computation of the MCMC). It should be noted that additional SNP features could be included in the model, such as, whether or not the SNP is a non-synonymous coding variant or associated with mRNA expression (eSNP) Model 1 involves J = 5 features including p-values and effect sizes for two analysis along with the minor allele frequency for K SNPs, assuming the J features and K SNP quality scores are independent. First, we specify the likelihood model for the J = 5 features as f will have a higher quality score than those SNPs with low values for f). The prior model is specified as Model 2) was similar to Model 1. However, the odds ratio features were removed leaving only the p-values and MAF features in the model. The rationale for removing the effect sizes from the BLVM was that on observations, it appeared that too much weight might be given to SNPs with very low MAF, as these are the markers that often have the larger effect sizes (but larger standard errors). The third model (Model 3) explored was one that was the similar to Model 2  but with the latent quality scores assumed to be dependent and model with the conjugate multivariate Normal – Wishart prior. That is, we model the latent SNP quality scores as Model 4 is identical to Model 2 but with the Model 5) was again similar to Model 2 but with fewer constraints for identifiability, with only constraints placed on the parameters The second model we investigated .The BLVM can be fit and parameters estimated within a Markov chain Monte Carlo. For application of the BLVM to prioritization of SNPs, we are mostly concerned with the latent SNP “quality scores”, θTo illustrate the use of the BLVM for the prioritization of SNPs from multiple GWAS studies along with the incorporation of functional information for the variants, we simulated 10 replicate sets of results  for 100 markers and four disease phenotypes  for four scenarios. The objective of the application of the BLVM is to determine possible genetic variants relevant with the four phenotypes that should be prioritized for functional studies. In simulating the SNP association p-values, we treated markers 10, 20 and 40 as non-synonymous coding variants, with the remaining markers considered “non-coding” variants. Scenario 1 represents the case in which none of the markers was associated with the phenotypes . The second and third scenarios involving markers being associated with the first two disease phenotypes; marker 10 (coding SNP) associated in scenario 2 and marker 60 (non-coding SNP) associated in scenario 3. The last scenario involved the setting in which marker 60 was associated with all four disease phenotypes. The 100 p-values for the four association studies were simulated, assuming independence, from a Uniform distribution for the case of a “null” SNP association and from a Uniform distribution for the case of a “non-null” SNP association.f = −logit. We coded the functional feature of the kth SNP as Ck = 1 if coding SNP and Ck = 0 if non-coding SNP. The model we applied to the simulated data (Model 6) involved J = 5 features for each SNP , modeling all SNP features and quality scores as independent. Let As outlined for the BLVM for the ovarian GWAS, we chose to transform the four p-values for association for each SNP using The five different latent variable models outlined above were first assessed using the top 100 SNPs on chromosome 20 from the ovarian GWAS. All five models were fit using the WinBUGS software package 10 for the case-control analysis using all cases  from the BLVM, with median rank of 7 and probability in the top 5 of 0.46, was ranked 1st and 9th based on the analysis of all cases, regardless of histological type, and the cases with serous histological subtype invasive ovarian cancer. In contrast, the top ranked marker (marker 8) based on the subtype analysis p-value  ranked in the top 5 markers with probability 0.13 based on the BLVM. The probability of being in the top 5, as opposed to the rank based on the mean of the posterior distribution of the quality score, takes into account the variation in rankings. This can also be seen in the 95% credible intervals for the rankings of the markers.Based on the results from the ranking of the top 100 SNPs on chromosome 20 using the five BLVM, we next used the simplest model (Model 2) to rank the top 500 markers using the features: p-value from case-control analysis involving all cases, p-value from case-control analysis involving only cases with serous histology, and the MAF for the marker. The BLVM (Model 6) was applied to each of the 10 simulated datasets, in which the four disease association p-values and the function (coding or non-coding variant) for the 100 SNP markers were included in the latent model. The models were fit using the WinBUGS software package Over the past few years, numerous GWAS for various complex disease and drug-related phenotypes have been completed, resulting in more than 350 publications and over 1500 SNPs implicated for association with multiple (>80) disease phenotypes or traits K SNPs. The proposed Bayesian latent variable model (BLVM) incorporates these SNP “features” to estimate a latent “quality score”, with SNPs prioritized based on the posterior probability distribution of the quality score rankings. We illustrate the method using data from an ovarian cancer GWAS of 1815 cases (1070 serous subtype) and 1900 controls, and compare the results from the BLVM to the standard ranking of SNPs based on the association p-value. In addition to the application of the BLVM to the ovarian GWAS, we outlined five BLVM models and compared the rankings from these five models. In the end, we opted for the BLVM simplest model for the ranking of SNPs for prioritization for functional studies. Results from the BLVM applied to the ovarian GWAS results for chromosome 20 indicate that if there is only resources to functionally validate a few markers, one should select the two markers with posterior probability of being in the top 5 markers of 0.46. However, for this study, the same two SNPs are selected for follow-up based on the p-value rankings from the analysis of invasive ovarian cancer and controls. In addition, depending on whether the follow-up involves replication of the association, as opposed to completion of functional studies, selection of only one of these two markers is necessary, as they are in high LD.The BLVM allows researchers to incorporate various “features” about the SNP into the ranking, including results from analysis of multiple phenotypes and prior knowledge, such as whether or not the SNP is a non-synonymous variant or associated with mRNA expression (eSNP) In addition to the ability of the BLVM to systematically integrate multiple features about the SNPs, the model is flexible in terms of model choice, choice of features to incorporate into the prioritization and weight/importance given to the different features. For example, in the simulated data, we illustrate the use of the BLVM for synthesizing results from multiple genetic association studies conducted on related diseases/traits, as a means for detecting pleiotropic effects . In the application of the BLVM to the simulated data, we also incorporated information regarding whether or not the marker was a coding SNP. The results showed how the inclusion of knowledge about the “functional” aspect of the SNP impacted the results, along with the effect of having all four traits associated with the marker, as compared to only two traits. The application of the BLVM to both the ovarian GWAS and the simulated data, further illustrates the flexibly in model choice and which features to include in the model. For instance, imputation of untyped markers for association analysis in GWAS is becoming a commonly used analysis technique Lastly, sensitivity analysis is possible (and recommended) to assess the impact of modeling choice on the results, as illustrated with the comparison of the five BLVM and the ovarian cancer GWAS. Currently, there is a limitation on the number of markers one can model with BLVM, due to the computational nature of the Bayesian model . Thus, following the genome-wide analysis, a couple thousand markers can be selected  for which BLVM can be applied using SNP “features” the investigator feels are important in the prioritization, to determine which markers to carry forward into follow-up studies. Another possible approach to reduce the model space would be to remove SNPs in high LD prior to analysis using the BLVM. However, as this approach might be acceptable for follow-up studies involving replication, it might not be an appropriate approach for selecting SNPs for functional studies as one could be removing functional variants in high LD with non-functional variants. Future work is needed to determine the optimal approach to deal with markers in high LD and algorithms to speed up the computation time of the BLVM. In summary, the BLVM is a flexible model that allows for the systematic integration of multiple SNP features, along with the ability to assess the uncertainty in the ranking, for the prioritization of markers for future functional studies.Table S1Results for 500 markers from Chromosome 20 with markers sorted by Posterior probability of being in top 5 markers.(XLS)Click here for additional data file.