Z ≈ 14, θ = 0.00) on cat chromosome E1. Genome-wide TDT, sib-TDT, and case–control analyses also consistently supported significant association within the same region on chromosome E1, which is homologous to human chromosome 17. Using haplotype analysis, a ~1.3 Mb region was identified as highly associated for progressive retinal atrophy in Persian cats. Several candidate genes within the region are reasonable candidates as a potential causative gene and should be considered for molecular analyses.Hereditary eye diseases of animals serve as excellent models of human ocular disorders and assist in the development of gene and drug therapies for inherited forms of blindness. Several primary hereditary eye conditions affecting various ocular tissues and having different rates of progression have been documented in domestic cats. Gene therapy for canine retinopathies has been successful, thus the cat could be a gene therapy candidate for other forms of retinal degenerations. The current study investigates a hereditary, autosomal recessive, retinal degeneration specific to Persian cats. A multi-generational pedigree segregating for this progressive retinal atrophy was genotyped using a 63 K SNP array and analyzed via genome-wide linkage and association methods. A multi-point parametric linkage analysis localized the blindness phenotype to a ~1.75 Mb region with significant LOD scores (The online version of this article (doi:10.1007/s00335-014-9517-z) contains supplementary material, which is available to authorized users. The development, structure, and function of the eye involves the interaction of thousands of genes. Genetic mutations in genes involving the eye are likely to be detrimental to the fitness of cats, especially random-bred cats. As of 2012, 232 genetic eye conditions have been mapped to a genomic location in humans and 192 Progressive retinal atrophy (PRA) is a generalized term for a range of hereditary conditions that cause retinal dystrophy or degeneration in animals. The conditions generally involve a progressive degeneration of retinal photoreceptors with variations in the age of onset and rate of progression . The file (feltCat5.2bit) was used as the source file for downstream analysis. The input sequences of the SNPs on the illumina Infinium Feline 63 K iSelect DNA array  were obtained from the design sequences provided by illumina in the “opa file.” The sequences flanked each SNP with a length of ~100 bp, for a total of ~200 bp. The BLAT algorithm was performed on a local host server. A BLAT search was conducted on each SNP’s sequence with all parameter values set as defaults (95 % or greater sequence similarity of 40 bases or longer sequence size).The cat DNA array was generated using SNPs that had been localized and spaced on an early assembly of the cat genome  only search outputs with the highest match score without gaps were considered, (ii) flanking SNP sequences were assigned to chromosomes based on the highest match score, (iii) the chromosomal location was adjusted and the SNP location was defined by averaging the start and end positions of the query sequence in the target sequences, and (iv) SNP sequences that lacked a match in the new genome assembly were assigned to chromosome “unknown” and SNP sequences that had a match score in random segments of unassembled regions were also assigned to chromosome “unknown.”Animal husbandry and housing were conducted according to University of California—Davis Institutional Animal Care and Use Committee regulations (IACUC protocol #16691). A multi-generation pedigree was previously developed to determine the mode of inheritance and define the clinical presentation of the Persian PRA  were selected from the pedigree (n = 202) for SNP genotyping . The individuals selected comprised 17 founders, four cats with a single known parent, and 105 offspring with both parents known. DNA was collected, isolated, and prepared as previously described , (ii) sex inconsistencies between genotypes and reported sex were examined using the function (–check-sex) and individual cats with sex inconsistencies were removed, and (iii) individuals with >5 % Mendelian errors in their corresponding trios were removed. For SNP markers, the following quality controls were applied: (i) SNPs with genotyping rate <90 % were removed using function (–geno 0.1), (ii) SNPs on the X-chromosome that exhibited haploid heterozygous genotypes were removed, (iii) SNPs with >5 % Mendelian errors in the pedigree (81 trios) were removed and remaining SNPs with Mendelian errors (<5 %) were set to 0 (no genotype) when present in specific individuals, and (iv) a minor allele frequency of 0.05 was chosen to prune the remaining of SNP data using the function (–maf 0.05).Quality control analyses for the genotypic data of samples and SNP markers were conducted using MERLIN 1.1.2 pedigree analysis package . A recessive model was used for the disease, assuming a disease allele frequency of 1 % and complete penetrance. The LOD score was estimated for each SNP and the maximum heterogeneity LOD score was selected for presentation. Only informative families, breedings of an affected to a carrier cat or breedings of two carriers, with multiple phenotyped individuals, were included in the linkage analyses.Parametric linkage analysis was performed using the function (–tdt) (Table dfam) (Table nonfounders).Genome-wide analyses were performed in PLINK  Table . A TDT at) Table  was perfm) Table . The sibn = 106), with 37 cases and 69 controls, assuming that all individuals were unrelated (–assoc). The genomic inflation for this initial analysis was obtained using the function (–adjust). To correct the genomic inflation and account for possible sample substructuring in the study population, the samples were analyzed using multi-dimensional scaling (–genome). Control individuals that lacked apparent clustering (n = 20), generally F1 Persian outcrossed cats, were removed from the dataset, and the case–control association was reanalyzed on 86 samples, 37 cases, and 49 controls. Genome-wide significance for each of the analyses  was determined by performing phenotype permutations  over the SNP dataset .A case–control association analysis was first conducted on the Persian cats in the study population . The region was examined to define haplotype(s) block(s) unique to the cases compared to controls. The SNP genotypes within the single haplotype identified in the cases (26 SNPs) were compared across the dataset.n = 6893) and many were incorrectly assigned to chromosomes (n = 510). To correct for SNP position assignments and to enable proper analyses using the SNPs on the array, the array’s SNPs were reassigned to new locations based on the 2011 cat genome assembly (ICGSG Felis_catus 6.2/felCat5). The majority of SNPs within each chromosome remained on the same chromosome, but 1997 SNPs were relocated to other chromosomes or the unknown category (Table S1).Physical locations of the SNPs in the 63 K illumina SNP array were originally defined using the 2008 cat genome assembly (NHGRI/GTB V17e/felCat4), which contained many gaps and unassembled and unassigned contigs  were successfully assigned to chromosomes in the 2011 genome reference, which constitutes ~10 % of the SNPs on the array. The reassigned, previously “unknown” SNPs, constituted as few as 1.75 % of the final X-chromosome SNPs and as many as 22 % of E1 chromosome SNPs. Lastly, 510 SNPs, previously assigned to chromosomes were reassigned to chromosome “unknown” due to the lack of actual known positions on chromosomes. Table S1 shows a detailed summary of the number of SNPs on each chromosome and Table S2 contains the updated map file.Approximately 86 % of the SNPs remained on the same chromosomes but with an updated location (Table S1). Chromosome “unknown” SNPs (n = 126) was selected for genotyping and analyzed via genome-wide analyses. The SNP genotype data were inspected for quality control criteria. Thirteen individuals were removed due to a genotyping success rate <80 % and a single individual was removed because of sex inconsistencies between genotype and reported gender. Five individuals exhibited Mendelian errors >5 % and were removed from the dataset. Therefore, the final dataset was composed of 106 individuals; 37 cases and 69 controls. The final dataset is presented in Fig. S1, where cases are marked by red symbols and controls are marked by blue symbols. Individuals with multiple matings are presented more than once in the pedigree for ease of visualization. The sex ratio was 1:1 with 53 males and 53 females. Fifteen individual founders were in the final study population dataset, where twelve were unaffected parents, one was an affected parent, and two were unrelated singletons. Ninety-one non-founders were in 28 nuclear families . The final study population was also composed of 33 affected trios, 20 were phenotypically discordant trios. Eighty-five phenotypically discordant sib-pairs were available  were removed and 532 X-chromosome SNPs were removed due to detection of haploid heterozygous errors. These SNPs were likely to be in the pseudoautosomal region of the X-chromosome. SNPs showing >5 % Mendelian errors (n = 317) across all trios also were removed, as were 12,494 SNPs that exhibited minor allele frequency <0.05. After quality control, 47,907 SNPs were used in downstream analyses where the genotyping rate in the remaining samples was >99 %.The cat DNA array consists of 62,897 markers distributed over 18 autosomal chromosomes, the X-chromosome, and chromosome unknown. SNPs exhibiting genotyping rate <90 % .TDT was performed on 33 phenotypically discordant trios. A strong association was suggested for chromosome E1. Permutation analysis confirmed the association Figure S2). Ten SNPs with significant . Ten SNPP value (<0.05) after permutation: chrUn5.6839723, chrUn5.6133983, and chrUn5.6766609 with Pgenome values of 1.0E−5, 0.00013, and 0.0028, respectively.Sib-TDT analysis was performed on 85 phenotypically discordant sib-pairs. A significant association between the PRA and the SNPs was suggested on chromosome E1 and the location of association with the disease was also significant after permutation Fig. . The tenn = 106) as unrelated, a case–control association analysis was performed . The most significantly associated SNP, chrUn5.6839723, was located on chromosome E1 at position 1,831,172. Analysis of all individuals exhibited a genomic inflation (λ) of 3.18. To account for the genomic inflation due to population substructure, the individuals were analyzed via multi-dimensional scaling. Twenty unaffected individuals appeared genetically distant from the majority of the individuals . These off-clustered unaffected individuals were removed , and the case–control association analysis was repeated . The most highly associated SNPs are shown in Table Considering all individuals of the study population  to chrUn5.7087155 (position 2076816) . Three control individuals were homozygous and shared the haplotype of affected individuals but each proved to be a mislabeling in the cat records and blindness was confirmed by the current owners.ARRB2 is the closest to the SNP with the highest linkage and association and two high ranking candidate genes  are located more distantly . Twenty-two of the genes are either associated with the eye or exhibit high expression in the retina. Three candidate genes cause blindness in humans and constitute likely candidates for investigation. Animal models of human hereditary diseases of the eye are well suited for functional and therapeutic studies. The domestic cat constitutes an ideal model for human eye diseases due to the high similarity in structure and function. Ten ocular disorders have been characterized in cats where analogous human conditions are reported  for a region of 1.75 Mb. The higher LOD scores obtained using parametric linkage analysis reflect the expected increase in power over that of the non-parametric analyses when the mode of inheritance is known.To exploit the SNP dataset and pedigree structure to the greatest possible extent, three genome-wide association analyses were conducted. The first was a test of linkage in the presence of an association in trios that is referred to as transmission disequilibrium test (TDT)  is associated with autosomal dominant cone dystrophy through a missense mutation  cause Leber congenital amaurosis , the GWAS was more significant and the cat assembly was more consistent with the corresponding human region. The GWAS appears potentially as biphasic, most likely suggesting the genome assembly may still need improvements in the region, or perhaps the disease involves a large inversion. The region of high association and linkage (~1.36 Mb) harbors 22 candidate genes with different degrees of involvement in ocular development and function. This region corresponds to the human region 17p13, which contains mutations responsible for several retinal disorders . However, exon 1, which in mainly UTR and only 23 bases of coding, has not been analyzed, as well as other regulatory elements of the gene. Thus, ARRB2 cannot yet be fully excluded as a candidate for Persian PRA. The remaining candidate genes shown in Table S4 are of lower priority and should be investigated if no mutations are identified in the most likely candidates.The additional candidate gene is This study localized a spontaneously occurring autosomal recessive photoreceptor degeneration of Persian cats to a 1.3 Mb region on cat chromosome E1, which is homologous to human chromosome 17. The identified region is gene-rich and has several candidates for molecular investigation. Regional exome capture, whole genome sequencing and additional recombinants from unrelated cats would support the mutation detection. Each analysis, linkage, case–control, and TDT, localized the trait to the same region, suggesting each had sufficient power using the cats available and the density of the current cat DNA array despite the high genome inflation observed in the case–control analysis. Finally, this study considers the linkage and TDT as the most valid methods for the analysis of this population.Supplementary material 1 (DOC 244 kb)Supplementary material 2 (DOC 1696 kb)Supplementary material 3 (XLS 1388 kb)Below is the link to the electronic supplementary material.


Single Nucleotide Polymorphisms (SNPs) identified in Genome Wide Association Studies (GWAS) have generally moderate association with related complex diseases. Accordingly, Multilocus Genetic Risk Scores (MGRSs) have been computed in previous studies in order to assess the cumulative association of multiple SNPs. When several SNPs have to be genotyped for each patient, using successive uniplex pyrosequencing reactions increases analytical reagent expenses and Turnaround Time (TAT). While a set of several pyrosequencing primers could theoretically be used to analyze multiplex amplicons, this would generate overlapping primer-specific pyro-signals that are visually uninterpretable.In the current study, two multiplex assays were developed consisting of a quadruplex (n=4) and a quintuplex (n=5) polymerase chain reaction (PCR) each followed by multiplex pyrosequencing analysis. The aim was to reliably but rapidly genotype a set of prostate cancer-related SNPs (n=9). The nucleotide dispensation order was selected using SENATOR software. Multiplex pyro-signals were analyzed using the new AdvISER-MH-PYRO software based on a sparse representation of the signal. Using uniplex assays as gold standard, the concordance between multiplex and uniplex assays was assessed on DNA extracted from patient blood samples (n = 10).i.e., from 2025 to 470 min) while reducing reagent consumption and cost by approximately 70 % .All genotypes (n=90) generated with the quadruplex and the quintuplex pyroquencing assays were perfectly (100 %) concordant with uniplex pyrosequencing. Using multiplex genotyping approach for analyzing a set of 90 patients allowed reducing TAT by approximately 75 % (This combination of quadruplex and quintuplex pyrosequencing and PCR assays enabled to reduce the amount of DNA required for multi-SNP analysis, and to lower the global TAT and costs of SNP genotyping while providing results as reliable as uniplex analysis. Using this combined multiplex approach also substantially reduced the production of waste material. These genotyping assays appear therefore to be biologically, economically and ecologically highly relevant, being worth to be integrated in genetic-based predictive strategies for better selecting patients at risk for prostate cancer. In addition, the same approach could now equally be transposed to other clinical/research applications relying on the computation of MGRS based on multi-SNP genotyping. Since 2005, a large number of Genome-Wide Association Studies (GWAS) have identified Single Nucleotide Polymorphisms (SNPs) associated with more than 300 complex diseases and traits . For proConsidering that each SNP has only a moderate association with PCa, Multilocus Genetic Risk Scores (MGRSs) were computed in different studies to assess the cumulative association of multiple SNPs. Zheng et al. assessed the cumulative effect of five selected SNPs . Comparei.e., a pyrogram™) which is then translated into the corresponding nucleotide sequence. Usually, genotyping a single SNP is carried out with one separate reaction in one well . Genotyping of multiple SNPs using uniplex pyrosequencing requires therefore performing multiple simultaneous or successive reactions for each patient, which impacts reagent costs and Turnaround Time (TAT). A more efficient alternative would be to use simultaneously a set of several pyrosequencing primers in a single well .While approaches for identifying men at high risk of PCa are still needed, the costs of genetic testing are currently too high to perform large-scale screening . Consoliβ-lactam antibiotics  and provided informed consent, following approval of the study by the Ethics Committee . The extraction of genomic DNA was performed from peripheral blood lymphocytes using the EZ1 DNA Blood kit and BioRobot EZ1 , according to the manufacturer’s protocol.A list of all unique nucleotide sequence (UNS) expected to be found in the 9 selected genomic regions was compiled Table . ConsideIdentification of the nine SNPs was first carried out for all patients (n=10) using uniplex (n=9) PCRs, followed by uniplex pyrosequencing reactions (n=9). All genotypes (n=90) resulting from uniplex pyro-signals were used as gold standard.In the following steps, quadruplex PCR followed by quadruplex pyrosequencing was carried out on four SNPs, while quintuplex PCR followed by quintuplex pyrosequencing was carried out on the remaining five SNPs Table . AlthougμL reaction mixture containing 7 or 10 μL  of the extracted DNA (50 ng), 5 μL of a PCR buffer , 3mM MgCl2, 1U AmpliTaq Gold®; DNA Polymerase , 200 μM of each deoxynucleotide triphosphate  and forward and reverse PCR primers (Table μM each) , those generated by uniplex pyrosequencing were also included in both dictionaries. These experimental uniplex pyro-signals were standardized by dividing all peak heights by the corresponding first unitary peak height (FUPH), as previously recommended *9) versus 470 ([165 +70]*2) min, respectively.TAT of uniplex and multiplex methods were also compared. Considering that each PCR and pyrosequencing plates includes 96 wells and that negative controls are required, the comparison was performed on a hypothetical set of 90 patients. Whereas buffy coat, DNA extraction and quantification  were identical for both methods, the estimated TAT of PCR  and pyrosequencing , in terms of quantities of PCR and pyrosequencing plates, pipette tips and reagent bottles . All quadruplex and quintuplex pyro-signals were converted into 4 and 5 genotypes, respectively.To the best of our knowledge, it is the first time that quadruplex and quintuplex pyro-signals from amplicons generated by a multiplex PCR amplification in a single well are translated into their respective single counterpart and that bi-allelic variants of each target gene are simultaneously identified and assigned. All multiplex results were perfectly (100 %) concordant with uniplex results. The latter were taken as gold-standard in this study, considering that uniplex pyrosequencing distinguishes reliably the specific pattern associated with each of the various genotypes, providing therefore accurate typing results , as confWhile uniplex SNP pyrosequencing does not compete with with faster and more cost-effective methods (TaqMan  or SequeConsidering that the AdvISER-MH-PYRO software and both dictionaries are available in the corresponding package (www.uclouvain.be/ctma.html), and that PCR conditions have already been optimized, these new assays can also easily be implemented in other laboratories now. As evidenced by this study, multiplexing has proved to be particularly relevant when developing new rapid, robust, reliable, cleaner and cost-effective SNP genotyping assays. While the added clinical value of MGRS based on 9 PCa risk-associated SNPs proved indeed not bring a major benefit as previously discussed , it was


Z-score for candidate SNP markers to find a significant change in a gene's expression. Here we analyzed the change caused by SNPs in the gene's promoter via a change in affinity of the TATA-binding protein for this promoter. We provide examples and discuss how to use this bioinformatics application in the course of practical analysis of unannotated SNPs from the “1000 Genomes” project. Using known biomedical SNP markers, we identified 17 novel candidate SNP markers nearby: rs549858786 (rheumatoid arthritis); rs72661131 (cardiovascular events in rheumatoid arthritis); rs562962093 (stroke); rs563558831 (cyclophosphamide bioactivation); rs55878706 , rs572527200 , rs371045754 (hemophilia B), rs587745372 (cardiovascular events); rs372329931, rs200209906, rs367732974, and rs549591993 ; rs17231520 and rs569033466 (both: atherosclerosis); rs63750953, rs281864525, and rs34166473 .The use of biomedical SNP markers of diseases can improve effectiveness of treatment. Genotyping of patients with subsequent searching for SNPs more frequent than in norm is the only commonly accepted method for identification of SNP markers within the framework of translational research. The bioinformatics applications aimed at millions of unannotated SNPs of the “1000 Genomes” can make this search for SNP markers more focused and less expensive. We used our Web service involving Fisher's  HLA-B gene of the human major histocompatibility complex  region of the core-promoters of eukaryotic genes; this approximation was first suggested by us  on the by 10−5 M , 0.2 is y 10−5 M , and KSing site .KSLIDE is our empirical estimate of the equilibrium constant of the TBP sliding along DNA that was determined experimentally  estimates  within the 26 bp DNA window centered by ith position of the promoter DNA analyzed—was heuristically estimated as Additionally, the standard deviation of the −ln[tes (see )—for allThis equation  estimateZ-score and its probability rate, that is, the p value . On this basis, a decision is made.Finally, the results of –4) on t on t4) oFor each SNP processed, the decision  is the m in silico 31 human genes containing 40 known biomedical SNP markers in their core-promoter from our review [Practical use of our Web service  is illusr review , which wr review , we founWe used the ancestral variants of these SNPs from Ensembl  using th italicized and labeled them with the marks “hypothetical” and “this work” in Tables hypothetical” and “this work.” These 17 new candidate biomedical SNP markers are the main result of the present study on how to use the proposed Web service [Finally, the remaining 17 of the 163 unannotated SNPs (10%) appeared to be new candidate biomedical SNP markers near the existing markers. We service  in pract IL1B gene (interleukin 1β). Let us analyze it in detail so that we can later briefly describe the rest of our SNPs on the basis of this example.The results on seven known biomedical SNP markers that increase TBP affinity for human gene promoters are presented in −31ATAAAAcag → gaaagT−31ATAAAAcag. Obviously, the minor allele −31T can significantly increase TBP affinity for the IL1B promoter relative to the ancestral one, −31C. According to  than KD = 5 nM in case of −31С. According to three independent empirical studies [ IL1B promoter corresponds to overexpression of this gene nfection –75.Z-score = 7.63, α < 10−6). According to some studies [ IL1B promoter corresponds to an interleukin 1β deficiency in patients. Because the known SNP marker rs1143627 and the unannotated SNP rs549858786 have opposite effects (relative to each other) on IL1B expression, we performed an additional keyword search for [β deficiency” as a biochemical marker relevant to medicine in the NCBI databases [β deficiency with a high risk of this autoimmune disease. Within the framework of this animal model of the human disease [Using the UCSC Genome Browser , we foun studies –41, thisatabases  in a mur disease , we prop IL1B promoter under study contains one more unannotated SNP rs4986962 (−67G→T) [Furthermore, the(−67G→T) , 4 that (−67G→T) , 77, whi(−67G→T) –27. F3 gene  and has properties that are similar to those of the above-mentioned basic example. Using the Web service [ NOS2 gene (inducible nitric oxide synthase 2) exemplifies the so-called balanced SNPs, which can have both beneficial  [As one can see in  service , we pred service . In turnsistance ) and advsistance ) on humaeukemia) . This ge PGR gene exemplifies the SNP-caused de novo appearance of a spurious TBP-biding site along with the additional pathogenic TSS at position +270 from the normal TSS for transcript number 2 of the same gene [α and β isoforms of the progesterone receptor encoded by this gene; this aberration doubles the risk of endometrial cancer in overweight women [The known SNP marker rs10895068 of the humaname gene . This alht women . CYP21A2 gene [ TNFRSF18 gene [Finally, the two bottom lines of 1A2 gene  and rs11F18 gene . These sThe results on 22 known biomedical SNP markers that decrease TBP affinity for human gene promoters are presented in  HBB and HBD of β- and δ-hemoglobins. As one can see in  DARC gene is of the same “balanced SNP” type; namely, it is associated with malaria resistance and a low white-blood-cell count, as positive and negative effects on human health, respectively [Some of these biomedical SNP markers  were found within the promoters of two gene-paralogs:ectively . CYP2A6 gene  [ CRYGEP gene, rs2276109 in MMP12, and 18 bp deletion within the promoter of CETP, are associated with a reduced risk of Coppock-like cataract [The known SNP marker rs28399433 (low risk of lung cancer among smokers) was found here within the humanme p450) , 99. Ourme p450)  predictsme p450) , 99. In cataract , asthma cataract , systemicataract , psoriascataract , and athcataract , 105 dueIn addition, the known SNP marker rs34223104 within the core-promoter for the undocumented alternative TSS (located 48 bp upstream of the major TSS of the CYP2B6 gene) transforms the canonical form (TATA-box) of the TBP-binding site, 5′-gatgaaatttTATAAcagggt-3′, into the C∖EBP-binding site , which causes increased bioactivation of the anticancer prodrug cyclophosphamide . In thisSOD1) [TPI1) [ESR2) [HSD17B1) [MBL2) [ADH7) [APOA1) [Furthermore, the remaining six known SNP markers, rs7277748 (SOD1) , rs18002) [TPI1) , 109, rs) [ESR2) , 111, rsHSD17B1) , rs72661) [MBL2) –115, and) [ADH7) , includi [APOA1)  and −33A [APOA1) , are of HBB), rs281864525 (HBB), rs34166473 (HBD), rs55878706 (DARC), rs572527200 (MMP12), rs17231520 (CETP), rs569033466 (CETP), rs563558831 (CYP2B6), rs562962093 (MBL2), rs72661131 (MBL2), rs372329931 (ADH7), rs36773297 (F7), and rs549591993 (F7), as one can see in Finally, near these 22 known biomedical SNP markers, we found and proposed 13 candidate SNP markers: rs63750953  within theF9), −42T→A (F9), rs16887226 (StAR), and rs28399433 (GH1), are among the oldest known SNP markers that were discovered by means of the electrophoretic mobility shift assay (EMSA) before the advent of the reference human genome, gh19 [GSTM3), rs7586110 (UGT1A7), rs10465885 (GJA5), rs35594137 (GJA5), and rs13306848 (THBD)—have properties similar to those of the SNPs above, in terms of bioluminescence (LUC) assays [F9), rs544850971 (StAR), rs200209906 (GSTM3), rs574890114 (UGT1A7), rs542729995 (UGT1A7), and rs587745372 (GJA5), which can significantly disrupt the above-mentioned TBP-binding sites and thereby may cause the same diseases in humans as do the six candidate SNP markers  assays –132 inst markers .5–8A in comparison with the norm: A (TA)7A. The longest of them, rs587745372, is an integral part of several haplotypes associated with a high risk of hyperbilirubinemia and jaundice [Finally, the last two biomedical SNP markers—rs587745372 and rs398048306—taken together are the well-known unique genetic variation in the TBP-binding site length, A (TA)jaundice , whereasjaundice  and TablThus, in the vicinity of the 40 known biomedical SNP markers within the TBP-binding sites in humans, we first found 17 candidate SNP markers: rs55878706 , rs562962093 , rs563558831 (cyclophosphamide bioactivation), rs549858786 (rheumatoid arthritis), rs372329931 , rs72661131 (cardiovascular events in rheumatoid arthritis), rs200209906 , rs572527200 , rs371045754 (Leiden hemophilia B), rs587745372 (cardiovascular problems), rs367732974 and rs549591993 , rs17231520 and rs569033466 (both: atherosclerosis), and rs63750953, rs281864525, and rs34166473 . This is the main result of our study.Because the mainstream method of searching for candidate SNP markers is now based on a statistical estimate of the similarity between the projections of unannotated SNPs and known SNP markers on various genome-wide maps, here we simplified the procedure by limiting it to unannotated SNPs only that are located near the known SNP markers in the TBP-binding sites of human genes. Within this framework, we found and analyzed 40 known SNP markers and 163 nearby unannotated SNPs shown within the first column of Tables The second most frequent group of SNP markers, 37 of 203 (18%), disrupts TBP-binding sites within core-promoters of human genes and thereby reduces expression of these genes; this deficient gene expression is more often associated with adverse than beneficial effects on human health. This finding is in agreement with the commonly accepted bioinformatics notion that the SNP-caused damage to genetic information is more frequent than SNP-caused genetic benefits. de novo occurrence of a spurious TBP-biding site , transformation of a normal TBP-binding site into another regulatory genomic signal , a change of the composite unit containing the TBP-binding site , a deletion of the DNA fragment either around or inside the TBP-binding site , and a duplication of the DNA fragment inside the TBP-binding site .The third most frequent group of SNP markers, 13 of 203 (7%), increases the TBP binding affinity for core-promoters of human genes and, hence, causes overexpression of these genes. This overexpression can be pathogenic, neutral, or beneficial for human health at approximately equal probabilities. This finding points to huge diversity of genetic effects of SNPs within the human genome. Indeed, the remaining manifestations of SNPs constitute only rare examples, such as “silent SNPs” , “balanced SNPs” , aAs for the SNP-caused pathological changes, the majority  of the SNP markers of diseases are either increasing or decreasing the risk of human diseases, whereas the rare types of SNPs are associated with drug resistance , prodrug bioactivation , disease complications , and ethnic differences . In addition, 10 of the 17 proposed candidate SNP markers are codirectionally changing TBP affinity for the core-promoters of human genes with respect to the nearby known SNP markers, whereas the remaining 7 candidate SNP markers do so in the opposite direction. Accordingly, we did additional keyword searches , 63 by hα < 10−7) to borderline significance (α < 0.05). In contrast, KD values when expressed in moles  vary frKD-values, Z-score, α-value, p value, etc.) and biomedical justification . At the


Additionally, all software is ready for immediate use from an Atmosphere Virtual Machine Image available from the iPlant Collaborative (www.iplantcollaborative.org).Sequencing the transcriptome can answer various questions such as determining the transcripts expressed in a given species for a specific tissue or condition, evaluating differential expression, discovering variants, and evaluating allele-specific expression. Differential expression evaluates the expression differences between different strains, tissues, and conditions. Allele-specific expression evaluates expression differences between parental alleles. Both differential expression and allele-specific expression have been studied for heterosis (hybrid vigor), where the hybrid has improved performance over the parents for one or more traits. The Allele Workbench software was developed for a heterosis study that evaluated allele-specific expression for a mouse F1 hybrid using libraries from multiple tissues with biological replicates. This software has been made into a distributable package, which includes a pipeline, a Java interface to build the database, and a Java interface for query and display of the results. The required input is a reference genome, annotation file, and one or more RNA-Seq libraries with optional replicates. It evaluates allelic imbalance at the SNP and transcript level and flags transcripts with significant opposite directional allele-specific expression. The Java interface allows the user to view data from libraries, replicates, genes, transcripts, exons, and variants, including queries on allele imbalance for selected libraries. To determine the impact of allele-specific SNPs on protein folding, variants are annotated with their effect , and the parental protein sequences may be exported for protein folding analysis. The Allele Workbench processing results in transcript files and read counts that can be used as input to the previously published Transcriptome Computational Workbench, which has a new algorithm for determining a trimmed set of gene ontology terms. The software with demo files is available from  Sequencing the transcriptome can answer various questions such as determining the transcripts expressed in a given species for a specific tissue or under a certain condition, evaluating differential expression (DE), discovering variants, and evaluating allele-specific expression (ASE). DE studies analyze the expression similarities and differences between species, tissues and conditions. ASE studies analyze the difference in expression between the parental alleles, where if one allele has significantly greater expression, it is referred to as allele imbalance (AI). Both ASE and DE require that the reads be trimmed and aligned to the reference genome. ASE further requires variant calling, calculation of SNP and/or transcript read coverage, and a test for AI. DE further requires the number of reads aligned to each transcript and a test for significance. This manuscript is mainly focused on allele imbalance, however, the data can also be used for DE analysis, and is briefly discussed.When mapping reads to a reference genome, there can be significant bias against reads with alternative alleles. One solution has been to mask the alleles, but as shown by Degner et al.SNP discovery is important for problems ranging from human disease studies to species comparisons. Over 20 variant callers have been published in the recent years. Many of them concentrate on the problems of finding rare variants in pooled samples, genotype calling, tumor-normal pairs, exome studies, and low coverage. These problems are found in human disease studies, i.e. most variant software is written for human disease studies, though most have been used for other SNP discovery challenges. The variant callers may incorporate quality, alignment, experimental errors, and dbSNP alleles. Most of them use Bayesian statistics. Yu and Sun ASE has been studied for imprinting and cis-regulation , as the name will be retained through each step of the pipeline, and then used in the AW program to build the database, which maps each file to its respective library conditions, e.g. strain and tissue. The AW pipeline steps and Java processing are listed in The Allele Workbench comes with a number of scripts and third-party tools organized to facilitate batch processing of NGS data for DE and ASE studies. These fall into four categories:Sequence preparationVariant calling, if necessaryAlignment to reference genome and extraction of heterozygous SNP coverageAlignment to transcripts and quantification of heterozygous and total transcript read countsThe batch scripts are designed in a modular fashion so that the user can easily substitute steps with an alternative method, and verify the results of each step before continuing. The exact steps will vary somewhat depending on data, as described below. Each script takes as its primary input a directory of files, calls one or more underlying tools to process these files, performs any necessary post-processing, and outputs to a standardized directory named "Results". Most of the scripts generate an overall summary, which should be checked before going to the next pipeline step. For tools that are not natively threaded, the scripts implement multiprocessor operation by splitting the set of input files. All necessary tools are supplied with the package . QC.sh. The script calls the underlying tool FastQC NGS reads should be trimmed to remove barcodes, adapters, and/or low quality sequence from the beginning and end of the reads. Unless these sections are identified and trimmed off, the majority of reads will not align properly. To identify regions needing trimming, the AW provides a batch script trim.pl is provided, which runs Trimmomatic QC.sh, and additional trimming performed if necessary.To perform the trimming, a batch script Align.pl script, preferably using alternative inbred reads, if available. For variant calling, the batch script Variants.pl runs the samtools caller on each sample alignment file (.bam), generating one call set (.vcf file) per alignment, and then combines all of these into a single consensus call set using a user-adjustable threshold .If variants are not already known for the species, then they must be called using the samples. The RNA-Seq reads must first be aligned to the reference genome using the The alignment files from this step are not suitable for ASE computation because they were not built from a masked genome, hence will suffer from reference bias.GSmask.pl script ; for this reason the alternate aligner STAR has been included in the package, and the batch script The eXpress-generated count files (extension ".xprs") are placed into the "Results" directory, and may be loaded into the AW database using runAW. A directory "ResultsTCW" contains the total counts, i.e. the transcript counts summed over both alleles and counts where there are no variants. These read count files may be loaded into TCW for DE analysis between samples.The runAW graphical interface builds the AW database using no external programs. The required input is the genome annotation file, VCF file, genome sequences files, and SNP coverage.bed files. Optional input is the NCBI gene annotation file, a variant effect file, and the eXpress transcript count files. The user defines the library conditions and abbreviations, which are used to map the SNP coverage and transcript read count files to their respective libraries. The runAW program is first run to build the database and provide the computations discussed below, but can then be run again to add or update any of the optional files. The initial build populates the libraries, gene, transcripts, exons and variant tables, and provides the relations between these entities as shown in The AW represents read coverage per SNP, summed SNP coverage per transcript and gene, read counts per transcript, and summed transcript read counts per gene. SNP coverage counts are summed to approximate transcript and gene allele-specific expression. The corresponding p-value has the caveat that counts for nearby SNPs are not fully independent; AW mitigates this problem by only counting one representative SNP from each cluster of closely spaced SNPs. For the summed SNP coverage, all transcripts that contain a SNP include its count. In contrast, the read count computation only assigns any given read to one transcript taking into account variants and alternative isoforms. The transcript with the highest read count represents the most likely expressed transcript.Both SNP and read counts are performed per replicate, which are summed for a total count. Each gene is assigned a start coordinate of its leftmost 5′ transcript start and an end coordinate of its rightmost 3′ transcript end, includes all SNPs within that range, and uses the summed read counts from its transcripts. The p-values for each SNP, transcript, and gene are calculated using the respective heterozygous counts and applying the binomial test. SNPs must have heterozygous counts ≥20, and genes and transcripts must have at least one SNP with heterozygous counts mg20, otherwise the p-value is marked undefined. For genes and transcripts, the heterozygous read counts must be ≥20, otherwise the p-value is undefined. The transcripts for a gene are ranked according to the number of reads, where rank = 1 is the most likely transcribed isoform.SNPs and transcripts are flagged if at least one replicate has a significantly opposite direction  from their summed value. Transcripts are flagged if, for any library, they have at least two AI  SNPs in different directions.The genome sequence, GTF annotation and VCF variants are used to create files of the parental transcripts and protein sequences. If variant effect annotations are not input from an external program, the runAW computes basic variant effects . It does not assign the effect terms to the same detailed level as snpEFF The AW query interface  has a BioMart The various views are necessary to help understand the complexities of the data. There will be inconsistencies, as they are inherently part of genomic data. Though the TCW has been previously published, it will be briefly summarized here for DE analysis. The TCW takes as input a transcript file and total count files (as provided by the AW pipeline). The runSingleTCW program for building the TCW database includes an interface to edgeR Since there are many GO terms, "GO slim" sets Higher level GO nodes contain lower levels, where a lower level node has either a ‘part-of’ or ‘is-a’ relation to the higher level node. Therefore, there is considerable redundancy in DE enrichment scores between different levels of the hierarchy; hence, the TCW trim algorithm singles out those GOs that appear to be primarily responsible for the enrichment in a given sub-tree using the following rules:If a parent (i.e. higher level node) has a better DE score than any of its children, that indicates that the differential effect is happening biologically at the level of the parent, and being inherited by those children, if any, which also are DE-enriched.Conversely, if a parent has a worse DE score than one of its children, it indicates that the differential effect is happening biologically at the child level (or below), and being seen in diluted form on the parent.By applying the computation to all GO terms for biological process with a GOseq p-value <0.001 for DE between all B6 reference and hybrid liver and muscle libraries, it reduced 76 terms to 24 see .Two atmosphere images have been created, one for all the AW software and the other for all TCW software. To run the AW the user only needs to upload their data to their iPlant account and start up an AW instance. The user interacts with the AW software through a desktop emulator. The demo files are part of the image, so the user can immediately try the software. To run the AW software on their own files, they may need to increase their iPlant resource allocations, and then they link to their files on their account and start the pipeline. The user can download the MySQL database to their computer from their iPlant account, download the viewAW executable from the AW website, and make the data and results available to the public for query and display.Though this manuscript is not intended to provide heterosis mouse results , we used the mouse data from the F1 hybrid progeny in order to demonstrate the AW features and provide some performance results. The dataset has 4 tissues and 4 replicates of Illumina RNA-Seq data ; deposited to the Short Read Archive (SRA) at GenBank under accession SRP050309. The hybrid parents are the mouse inbreds (C57BL/6J and BALB/cByJ). The reference genome GRCm38 (strain C57BL/CJ) was downloaded from Ensembl The processing used the AW-pipeline. Variant calling was only performed for timing since a VCF file was available that was created from genome data. A masked reference genome was created using the VCF file, and trimmed reads were aligned to it and SNP coverage was computed. An AW database was created, which produced the parental transcripts. The trimmed reads were aligned to the transcripts and read counts  were computed and entered into the AW database. The reference protein sequences (output from AW) and the total read counts from both inbreds and the hybrid were entered into a TCW database and annotated with the UniProt The hybrid dataset comprised 15 paired files (one replicate failed). The raw read files were between 7 and 14 gigabytes each, approximately 157 total gigabytes, whereas the final AW database was 400 Mb. The following results are from the AW database overview . Performing the same comparison on transcripts with rank  = 1 using the SNP coverage, there were 2,255 ref>alt and 1,773 alt>ref (0.79 ratio); using the read count, there were 4,392 ref>alt and 3,849 alt>ref (0.88 ratio). Residual reference bias can result from several causes: SNPs that are false-positives ; SNPs that are uncalled , and hence unmasked, leading to mapping problems at those loci; or indels, which can only be partially addressed by masking even where they are known accurately. These problems may be exacerbated in this dataset since the VCF file was created for alternative genome BALB/cJ, whereas the RNA-Seq data was from the related strain BALB/cByJ.Of the 47,234 transcripts, 7,843 had rank  = 1. The following queries were performed using filter rank  = 1 so that only one transcript per gene was reported. There is a difference between querying for all transcripts that have at least one SNP with coverage ≥20  versus having the summed SNP coverage ≥20 , where the latter can include transcripts where all SNPs have <20 coverage. There were 2,659 transcripts with SNP AI  for at least one library and 5,653 with read AI  for at least one library. Using SNP coverage, kidney and liver shared the most AI transcripts with 514, and brain and liver shared the least with 362, whereas with read count, brain and muscle shared the most with 1,732 while again brain and liver shared the least with 1,604.To select parental transcript sequences for protein folding analysis, there must be at least one missense SNP in order to have any difference in the amino acid sequences. Setting the filters for rank  = 1, at least one missense, and any library with read count p-value <0.01, there were 2,265 transcripts. Since the SNP effects came from Ensembl EVP, they included SIFT annotations, which predicts whether the missense SNP will effect protein function. Adding the filter that at least one SNP must be (SIFT) damaged, there were 451 transcripts.Cirsium. Pemrumba et al. Publications on both allele specific expression and heterosis include a wide range of analyses. Though the AW and TCW could strive to cover every possible analysis, clever labs will come up with new types of analysis and questions to ask. Moreover, trying to cover every possibility can create confusing software. Therefore, providing the basic analysis allows the user to concentrate on what makes their study unique, i.e. only use custom software on the unique aspects of a study. The following three studies addressed very different problems yet used similar upstream computations of RNA-Seq trimming, determining SNPs, alignment, and performing DE and ASE studies. Bell et al. Align.pl in place of using MOSAIK with the 454-contigs. From the AW and TCW graphical interfaces, they could export the contig names and associated values  for their project specific analysis. The TCW would at least partially provide their GO analysis. Pemrumba et al. could use the pipeline as is except for the variant calling. The AW and TCW would provide the union queries and variant annotation, but they would need to export information for the DAVID pathway analysis. Zhai et al. could use the pipeline as is except for the variant calling. The AW and TCW would provide their comparison between stages. To compare the DE and AI transcripts, they could export the two tables to further analyze with a custom script. The AW overview provides SNP substitution counts, and the TCW GO analysis would provide much of the GO classification.As shown in Whether these pipeline substitutions would be adequate is hard to say since Method sections in biology publications rarely cover the analysis to the depth necessary for a user to repeat the analysis; given that they are not software publications, one would not expect that level of detail and justification. However, it is a good reason to use published software when possible, as the software publication should provide the details. Moreover, one must assume that the custom scripts are correct, whereas published software is typically well tested and benefits from feedback from many users. The lack of computational detail also makes it hard to compare results between publications, for example, Zhai et al. identified 480(17%) of 2,793 transcripts with SNP coverage ≥20 to have significant AI  in either stage, whereas the AW mouse database shows 2,659 (44%) of the 6,011 transcripts with SNP coverage ≥20 to have significant AI  in any library; is this difference real or an artifact of the difference in processing?Most studies, including the three cited here, do not consider the correlation between ASE SNPs and transcription and translation. A synonymous SNP can have an effect on transcription  for organized and permanent storage of results, (2) to clearly understand the data, (3) extensibility, and (4) to make it easy to share results with collaborators. It is now typical that data is submitted to Genbank, but each large-scale experiment generates large amounts of results that are only available from the publication, whereas these should also be made publicly available in digital format, preferably via a web-based queryable database. In summary, with the vast amount of high-throughput sequencing results that are being published, it is no longer good practice to have results in flat files and rely on many custom scripts.https://code.google.com/p/allele-workbench. The AW and TCW packages  are available from www.agcol.arizona.edu/software , where each provides a 'tour' of the software. The AW URL has a link to the AW applet to view the hybrid mouse database used in the Results section. The AW and TCW for the full mouse database (48 libraries) are available from www.heterosis.iplantcollaborative.org. The AW and TCW packages are available as Atmosphere Virtual Machine Images at www.iplantcollaborative.org.The AW code is available from


The Mx protein is one of the best-characterized interferon-stimulated antiviral mediators. Mx homologs have been identified in most vertebrates examined; however, their location within the cell, their level of activity, and the viruses they inhibit vary widely. Recent studies have demonstrated multiple Mx alleles in chickens and some reports have suggested a specific variant (S631N) within exon 14 confers antiviral activity. In the current study, the complete genome of nine elite egg-layer type lines were sequenced and multiple variants of the Mx gene identified. Within the coding region and upstream putative promoter region 36 SNP variants were identified, producing a total of 12 unique haplotypes. Each elite line contained from one to four haplotypes, with many of these haplotypes being found in only one line. Observation of changes in haplotype frequency over generations, as well as recombination, suggested some unknown selection pressure on the Mx gene. Trait association analysis with either individual SNP or haplotypes showed a significant effect of Mx haplotype on several egg production related traits, and on mortality following Marek's disease virus challenge in some lines. Examination of the location of the various SNP within the protein suggests synonymous SNP tend to be found within structural or enzymatic regions of the protein, while non-synonymous SNP are located in less well defined regions. The putative resistance variant N631 was found in five of the 12 haplotypes with an overall frequency of 47% across the nine lines. Two Mx recombinants were identified within the elite populations, indicating that novel variation can arise and be maintained within intensively selected lines. Collectively, these results suggest the conflicting reports in the literature describing the impact of the different SNP on chicken Mx function may be due to the varying context of haplotypes present in the populations studied. The Myxovirus-resistance (Mx) proteins are interferon-induced, dynamin-like, large GTPases that were first identified because of their association with influenza virus resistance in laboratory mice th coding exon) and the S631N variant, though other non-synonymous variants have been identified in other exons, and multiple haplotypes have been recognized.Polymorphisms of the Mx gene have been reported in multiple breeds of chickens, including Australorp, Fayoumi, Japanese native chickens, Indonesian native chickens, White Leghorns, Broilers and inbred laboratory lines In vivo work by Ewald et al.et al.The association between the 631 variant and antiviral activity was investigated in various breeds and with different systems. The significance of variation in the Mx gene and its potential role in resistance to avian influenza is intriguing. We report here variation in the promoter, 5′ untranslated, and coding regions of the Mx gene in 9 elite chicken lines representing the three different breeds used by Hy-Line International for commercial egg production. These chicken lines contribute to over 40% of the commercial egg layer production birds in the world and thus represent a considerable proportion of global commercial egg production.A DNA archive consisting of multiple generations of males (1996–2011) from 9 different chicken lines was utilized for all studies. These lines are the elite pure lines used to produce commercially utilized egg-production chickens. DNA was obtained from blood from 135–1,264 birds from multiple generations of each line. Genotype and phenotype information from 7,964 samples was utilized for all subsequent trait association analyses. These 9 lines encompass three different breeds; six White Leghorn lines (WL) that produce white-shell eggs, one Rhode Island Red derived line (RIR) that produces brown-shell eggs and two White Plymouth Rock derived lines (WPR) that produce brown-shell eggs.Initially twelve individuals from each line were sequenced using Sanger sequencing  on an ABI3100 (Applied Biosystems), and the resulting sequence data was analyzed using Vector NTI v. 10 (Invitrogen) to determine the number of potential SNP and haplotypes within these lines. Primer pairs (12) used to produce the template amplicons for sequencing (exons only) are as defined in The confirmation of SNP was accomplished using fluorescence-based competitive allele specific PCR with KASP chemistry Two mortality related traits were measured. Mortality during grow and lay (LM) were recorded from the sire families which had been placed in multiple field test locations under typical commercial environments. Mortality traits were recorded as sire family means in percent based on 30 daughters per sire, and was measured across generations and genetic lines. The second mortality trait was from multiple generations of a Marek's disease virus (MDV) The performance traits recorded are typical for commercial egg production lines and include egg production ), sexual maturity , and egg quality traits . These traits were also calculated as the mean progeny average for each pure-line sire family across generations and genetic lines Two sets of statistical models were tested for association between Mx genotype and mortality and performance traits for each genetic line. The first model tested the SNP's allele substitution effects (ASE) for multiple SNP and genetic lines. In this model, the effects of generation (test) and the number of copies of the reference allele for each tested SNP were fit. The second model fit the effects of generation (test) and haplotype for each trait, and was used to test the overall haplotype effect on each trait within line. For those cases with a significant effect, LSM (Least Squares Means) were calculated and separation and test between haplotypes was performed. Analyses were carried out using JMP 11.0 (SAS Institute Inc).http://www.datamonkey.org) Full length nucleotide coding sequence for each of the 12 haplotypes from the commercial lines identified here, plus 53 additional chicken Mx sequences representing multiple breeds reported in GenBank, were analyzed for evidence of recombination as well as site-by-site selection using the Datamonkey webserver .The 12 Mx haplotypes identified in the nine elite lines reported here were each analyzed using the RaptorX protein structure prediction server (raptorx.uchicago.edu) which identified protein data bank record 3szr The protocols for all experiments involving the collection of blood samples and phenotypic observations used in this study were reviewed and approved by the Institutional Animal Care and Use Committee (IACUC) at Hy-Line International.de novo sequencing and the NextGen resequence data of the elite commercial lines, revealed multiple SNP within the Mx gene and its immediate upstream region  and egg shell color (seen in two lines). Significant ASE for egg weight and albumen height was seen in two lines, whereas shell puncture resistance and egg defects each had only one instance of significant association.Significant haplotype effects found in each line are summarized by trait in Trait association between haplotypes and performance traits of progeny showed at least one significant association (p<.05) in four of the lines. Haplotype association with shell color was found in two lines and was highly significant (p<.0001) in both lines. Haplotype 11 showed consistent advantage for four traits .Full length coding sequence of the 12 Mx haplotypes identified herein, were aligned with 53 additional chicken Mx sequences obtained from GenBank. Overall, these sequences resulted in the identification of 72 SNP within the Mx cds (data not shown). These sequences were then analyzed for individual codons with evidence of either purifying (synonymous substitutions (dS) > dNS) or diversifying (dN > dS) selection using various models. Twenty codons were identified by at least one of the six models, 11 with evidence for diversifying selection and nine with evidence of purifying selection . Of theset al.et al.s that were not resolved. Also, as the chicken Mx has approximately 40 additional N-terminal amino acids not found in the mammalian Mx proteins The RaptorX protein structure prediction server Examination of these different SNP within the context of functional domains demonstrated several dS and dNS SNP surrounding the GTPase active site within the G-domain (ueure 3 and 4A). While this structure was not fully resolved for MxA B and α3B than with α1B, with L401, which was found to be under purifying selection  that forms the transition between BSE2 and stalk there are 4 α-helices and 3 loop regions  that make up the MD portion of the stalk. This is followed by loop L4S and serves as the transition between the MD and GED regions of the stalk. The GED then contains an additional 2 α-helices and 1 loop  before a final loop that connects the stalk with BSE3 (L2BS)  and the GTP effector domain (GED) . From th3 (L2BS) . This re3 (L2BS) , one obselection .S, L3S, α3S region of the MD, with the one remaining site (S631N) located in α4S of the GED , resulting in a change from serine to asparagine at amino acid 631 The Mx genes, and the large GTPase protein they encode, are among the best-studied interferon-stimulated antiviral effector molecules. Their identity, and even their name, is based on their ability to inhibit virus replication, specifically influenza virus. Roughly 10 years after the Mx genes of chickens and ducks were first identified and reported to have no antiviral activity Since this initial observation, several laboratories have surveyed various poultry populations and reported over 72 potential SNP either in the literature or in the GenBank database (data not shown). However, most efforts have focused on MxCDS1892, which is often referred to as the “resistance allele” This current study surveyed and calculated the frequency for SNP found within nine elite commercial egg production lines and analyzed them for association with various mortality and performance traits. Out of the 36 SNP identified in these genetic lines, seven SNP were significantly associated with one or more traits; however, interestingly MxCDS1892 was not among those seven. Examination of each of these seven SNP and the favorable allele for each trait indicates that, within a given line, the same SNP may be associated with multiple traits but have different favorable alleles for each; making it difficult, if not impossible, to understand its true biological significance.There are multiple reports of Mx variation in different chicken lines. These previous studies attempted to correlate “functional” variants to observations of resistance or susceptibility to viral infection. While this approach is often a first step in understanding how a specific sequence is associated with a trait of interest, it does not account for the context of the variants Mx has at least 4 functional domains, each playing a key role in the protein's ability to exert an antiviral function. The GTPase activity in the N-terminal G-domain has long been recognized as required for Mx activity et al.In addition to the GTPase activity, Mx functions as part of a large complex oligomer dependent on key secondary and tertiary structural elements. These oligomers are made up of 16 Mx dimers that form a large ring around viral ribonucleoprotein complexes wherein the G-domain's enzymatic activity delivers its antiviral effects Historical analysis of the haplotype frequencies within the lines evaluated herein indicated that there has been a significant shift in the haplotypes present within six of these lines. Simultaneously, these lines are under intensive selection for numerous traits related to egg production, general animal health, and resistance to MDV. The change in frequency of specific haplotypes is correlated with genetic progress in these lines, suggesting specific advantage of certain haplotypes. The associations found between Mx haplotype and various production traits are interesting. Many common avian viral diseases are known to cause mild to severe reduction in egg production, decrease appetite, depress the immune system and affect the physiology of the reproductive tract. Anti-viral properties of Mx variants could be providing enhanced resistance to viruses routinely encountered throughout the lifecycle of a bird, consequently providing a slight overall improvement in performance.Review of previous reports of chicken Mx sequence diversity and its functional role in viral resistance provides few conclusive answers. This current work has focused on developing a comprehensive understanding of the significance of sequence diversity of the Mx gene in multiple lines of chicken with multi-generation genotypes and extensive production trait information. Among these lines additional sequence variants were identified that had not been described previously, and more importantly new discrete haplotypes were observed whose frequency appears to be under selection across multiple generations. Thus it is apparent that in future studies of chicken Mx the complete haplotype of the gene should be considered as the functional unit rather than a single SNP.In addition to the haplotype, it is also important to understand these differences in context of their location within the three dimensional structure of the mature protein. The important functional components of the mature protein can be identified either in structural modeling studies or by evaluating the selection pressures on individual residues as described. These variant sites may provide insight into how Mx functions in the response to virus. The degree of variation contained within the chicken Mx, particularly within commercial stocks, is counter to preconceptions that commercial stocks are highly inbred with limited variability. These levels of diversity within the commercial lines provide vast opportunities for subsequent functional studies. The identification of two novel recombinants within these lines indicates that novel variation does arise and can be maintained within highly selected commercially utilized genetic lines.Collectively, these data represent the most exhaustive survey of genetic diversity within the Mx gene of commercial layer-type chickens. In addition to the identification of novel SNP, this data reports the association of Mx SNP with both disease resistance and performance traits, and highlights the need for a better understanding of the haplotypes formed by all of the SNP. Mx is a large, complex protein with multiple functional domains. Each domain plays a role in the Mx oligomer and its ultimate function in the host. Understanding how these various SNP and haplotypes interact with each other to function properly in the cell will be key to our understanding the role Mx plays in the interferon-mediated response to viral infections in chickens.


EAAT2) and the same approach was initially used here to investigate this promoter region in a cohort of newborns.Single-strand conformational polymorphism (SSCP) is still a frequently used genotyping method across different fields for the detection of single nucleotide polymorphisms (SNPs) due to its simplicity, requirement for basic equipment accessible in most laboratories and low cost. This technique was previously used to detect rs4354668:A > C (g.-181A > C) SNP in the promoter of astroglial glutamate transporter  of 0.0023. In contrast, g.-200C > A and -181A > C were more common  and showed string evidence of linkage disequilibrium (LD). In a systematic comparison, 16% of samples were miss-classified by SSCP with 25-31% errors in the identification of the wild-type and homozygote mutant genotypes compared to pyrosequencing or Sanger sequencing. In contrast, SSCP and pyrosequencing of an unrelated single SNP (rs1835740:C > T), showed 94% concordance.Unexpectedly, four distinct DNA migration patterns were identified by SSCP. Sanger sequencing revealed two additional SNPs: g.-200C > A and g.-168C > T giving a rise to a total of ten EAAT2 promoter to human diseases.Our data suggest that SSCP cannot always detect reliably several closely located SNPs. Furthermore, caution is needed in the interpretation of the association studies linking only one of the co-inherited SNPs in the  Genetic analysis is one of the fastest-growing areas of clinical diagnostics. Changes to a single nucleotide, known as single nucleotide polymorphism (SNP) is one of the major types of variants identified in the human genome. On average, in the human genome SNPs are distributed at 1 SNP per 1000 base pairs . Some ofPCR amplification followed by Sanger DNA sequencing is one of the most commonly used methods of identifying SNPs in a sample cohort ,4. HowevPyrosequencing , a non-gEAAT2 (SLC1A2) at position -181bp (g.-181A > C) in genomic DNA of newborn infants. The rational for looking at this particular SNP was that previous studies using SSCP found association of this SNP with increased extracellular glutamate levels and neurodegeneration in adult stroke patients [EAAT2 promoter; g.-200C > A and g.-168C > T. The g.-168C > T SNP was only found in one individual in a heterozygous form in the entire cohort. In contrast, g.-200C > A and g.-181A > C sequence variants were much more common and they were in Linkage Disequilibrium (LD). SSCP was not discriminatory enough to clearly show differences between the various genotypes and 31% of homozygote mutants  and 25% wild-type (WT/WT) genotypes were identified incorrectly using this technique when compared to sequencing data. In contrast, pyrosequencing detected all naturally occurring variants in the highly GC-rich region and showed 100% concordance with Sanger sequencing suggesting that it can be used successfully to detect closely positioned and linked SNPs. Our data also indicate that the interpretation of the studies [In this study, we investigated a previously identified SNP  in the patients ; with a patients  and the patients . Unexpec studies  attributNewborn dried blood spots (DBS) were collected from predominantly Caucasian infants  born in the greater Bristol area (UK) participating in an association study to investigate the genetic background of newborn infants to white matter brain injury. The study received ethical approval in April 2008 from the National Research Ethics Service, UK (REC reference number 10/H0106/10 ). Sample2, 200 μM of each dNTP, 100 pmol of each oligonucleotide primer, 1 unit of high fidelity Taq Polymerase  and 2 μl (~1-30 ng) of gDNA. Additionally, a final concentration of 1× GC-rich solution  was added to each reaction. Reaction parameters were 95°C for 5 min followed by 35 cycles of 95°C for 30 s, 60°C for 45 s and 72°C for 1 min. A final extension at 72°C was carried out for 10 min.Previously described primers EAAT2F and EAAT2R were used to amplify the EAAT2 promoter fragment , 1.5 mM MgCl2, 200 μM of each dNTP, 100 pmol of each oligonucleotide and 1 unit of high fidelity Taq polymerase  per reaction. Two microlitres of genomic DNA (containing 4-6 ng DNA) was used per reaction. Amplification was performed with the following conditions: 95°C for 5 min; 50 cycles of 94°C for 30 s, 60°C for 30 s and elongation at 72°C for 30 s; followed by the final extension for 10 min at 72°C. Pyrosequencing and Sanger sequencing were carried out as we described previously [Two sequence-specific primers .EAAT2 variants : A/A and A/A , the non-random association between alleles of different loci, are consistent with the SNPs being linked (Table Successful amplification was obtained in 209 samples (87.5% success rate). Failure of the remaining samples was likely due to low quality genomic DNA. Some of the samples were 22 years old and showed DNA degradation . Overalled Table . The anaversus 16%) of samples were misclassified as with the EAAT2F and EAAT2R primers.To compare the concordance between SSCP and pyrosequencing for a single SNP, SNP rs1835740 was analysed in the same 239 samples. Three distinct SSCP patterns were observed for the different genotypes , through the 1000 Genomes Project  and was EAAT2 promoter at position -168bp was added to the NCBI SNP Database . In the 51 samples that we sequenced only the C allele was present. Furthermore, in the entire cohort (n = 214) only one T allele was found in a heterozygous form  . Our datEAAT2 promoter [SSCP was used initially in this study because this method has previously been applied to genotype exactly the same region of the promoter . We usedpromoter  but modipromoter . Our SSCEAAT2 promoter is highly GC-rich  all mutations in the region are known, (ii) the SSCP genotype readout is validated by another method, and (iii) in case of two SNPs in the region the indicative bands for both mutations are clearly and easily distinguishable.Currently, the detection of g.-200C > A;-181A > C cost £1.79 and £1.43 by pyrosequencing and SSCP, respectively. For pyrosequencing the cost includes all reagents and a charge for the use of the pyrosequencer. For SSCP the cost was calculated from the reagents and Sanger sequencing of 10% of the samples. Following PCR amplification, the pyrosequencing required 1 h preparation time and 21 min run time for the two SNPs for 96 samples. For a single SNP (such as rs1835740:C > T) the run time is usually ~10 min for 96 samples. In contrast, SSCP analysis of 100 samples requires 2-3 h post-PCR preparation time, 12-16 h gel electrophoresis and 0.45-1.5 h silver staining. In addition, at least 10% of the samples need to be prepared for Sanger sequencing. Whilst pyrosequencing provides a less labour intensive, low cost and high throughput platform to genotype samples, in laboratories with no access to this facility SSCP may be used reliably for genotyping if Our data suggest that SSCP cannot always detect reliably several closely located SNPs. Furthermore, caution is needed in the interpretation of the association studies linking only one of the co-inherited SNPs in the EAAT2 promoter to human diseases.The authors declare that they have no competing interests.SR designed and carried out all experimental work and data analysis. DO carried out the statistical analysis. KL and DH arranged access to the adult and newborn clinical samples, and contributed to clinical study design. KL obtained research ethics, NHS R&D permissions, University of Bristol research sponsorship for use of human tissue and consenting processes. MW assisted with the pyrosequencing analysis. EM and AV advised on experimental design. SR and AV wrote the manuscript and all authors reviewed the manuscript prior to submission. All authors read and approved the final manuscript.


Triticum aestivum) is one of the most important crops in human and livestock diets, and the development of wheat varieties that produce higher yields, combined with increased resistance to pests and resilience to changes in climate, has meant that wheat breeding has become an important focus of scientific research. In an attempt to facilitate these improvements in wheat, plant breeders have employed molecular tools to help them identify genes for important agronomic traits that can be bred into new varieties. Modern molecular techniques have ensured that the rapid and inexpensive characterisation of SNP markers and their validation with modern genotyping methods has produced a valuable resource that can be used in marker assisted selection. CerealsDB was created as a means of quickly disseminating this information to breeders and researchers around the globe.The increase in human populations around the world has put pressure on resources, and as a consequence food security has become an important challenge for the 21st century. Wheat  contains supplementary material, which is available to authorized users. A rapidly expanding human population has meant that food security has become one of the primary global concerns that scientists and governments are seeking to address. Cereal crops are a major source of nutrition for both humans and domesticated animals. The development of novel varieties that result in an increase in yield, and have other beneficial characteristics such as disease and drought resistance, should help to safeguard the future supply of these economically important crops.Triticum aestivum) that is easily accessible to wheat breeders and researchers around the world.Single nucleotide polymorphisms (SNPs) have become the marker of choice used by plant breeders in marker assisted selection (MAS). The CerealsDB website was developed in response to a need to collate and present validated SNP data for wheat  contains links to functional SNPs which are defined as those SNPs with a high probability of affecting phenotype In addition, version 3.0 has employed the use of web services to expose the underlying data in the CerealsDB database in formats that enable integration of its data with other online genomic data resources. The CerealsDB web services have been developed in collaboration with partners from the Wheat Initiative  Wheat InCerealsDB version 3.0 uses a MySQL relational database management system (RDMS) database version 14.14) running on a Linux server (Ubuntu OS version 12.04) and hosted using the Apache2 web server (version 2.4.7) with Perl and PHP scripts used for all data retrieval and output. The site was migrated to a new server that has significantly more resources than the previous web server , with 256 GB RAM, 32 CPU’s and 2.6 TB of disk space. The migration of the site to a new server was considered essential as the amount of data hosted and the number of users accessing it . The ‘axiom820’ contains 819,571 SNPs, and 164,250 of these have been mapped .Two tables in the database relate to the 35 K wheat breeders array ‘axiom35’ and the 820 K array ‘axiom820’ described in Winfield et alTaqman SNPs are stored in a table called ‘taqman_SNPs_mapped’ which contains 4800 entries, of which 4735 have been mapped. TaqMan Assays are a qPCR genotyping application developed by Thermo Fisher and based on 5’ nuclease chemistry that uses specific fluorescent probes to discriminate SNP variants. A separate table ‘taqman_core_set’ contains 935 SNPs that are known to be evenly distributed across the wheat genome.The table called ‘funcSNPs’ contains 19,641 non-synonymous SNPs of which 2738 have been characterised as having a major effect on the protein function. Functional SNPs are defined as those with a FATHMM score less than -3.0 or greater than 3.0.Aegilops), and more distantly related species such as Secale (Rye) and Thinopyrum.The largest table in the database is called ‘axiom820dataab’ consisting of 476 rows linking the axiom 820 K SNP codes to the genotyping data for 475 varieties of wheat and closely related species such as the A,B and D genome progenitors .Alignment information is contained in three tables called ‘snps_vs_brachy’  and the ‘burdock’ table. These tables map SNPs to the fully contiguous SNP data were included from previous studies , 8, 9 ase.g., BS00000001). For the axiom SNPs unique IDs were created for each axiom probe using an 11-character code, beginning with an ‘AX-‘ prefix followed by 8 digits . Users can enter this code on the CerealsDB site to retrieve information about the SNP, such as its position within a contig, the contig sequence, map location and adjacent SNPs. Links are also provided to the Ensembl Plants database [The CerealsDB database currently consists of data on 1,017,400 SNPs characterised on at least one of the four genotyping platforms, of which 86,514 (8.5 %) have been mapped to a genetic position. These SNPs are uniquely identified by a 10-character code, beginning with a Bristol SNP (BS) prefix, followed by 8 digits  see Fig. There are also links to a download page for the draft wheat genome, a wheat EST database, a search page for wheat DArT data and a page describing the mapping of DArT markers using wheat deletion lines.e.g. SNP name and Axiom code) are exact. The initial page for each of genotyping platforms contains statistics on the SNPs for that particular platform. All SNPs listed in CerealsDB have been assigned a unique ID and the output page for a query gives information about the gene name and locus. As described previously [The SNP database allows SNP name and contig queries using a BS code or Axiom code. Some queries  or westdb cloneID. The wheat DArT markers can be searched by marker name or wheat line and the facility is available to download the DArT datasets in Excel format. Mapped DArT markers can also be viewed on their respective chromosome by clicking the appropriate chromosome image of the ideogram.Users can create Flapjack files based on the axiom genotyping data (.map and .dat) that can be imported into the flapjack software to allow comparison of the user-selected wheat varieties see Fig. . Flapjaci.e. data can be exchanged between different programming languages such as Perl and Python, or different software that may run on different operating systems such as Windows and Linux). JSON is a text-based open standard for data exchange that has been designed to be human-readable and easy for machines to parse and create. JSON uses a simple methodology to store information as key-value pairs, facilitating the structuring of data so that it can be consumed by other services. For the web services we adopted a JSON-based schema for messaging between the CerealsDB server and its clients.A web service can be described as a collection of open protocols and data standards that allow data to be exchanged between software applications or computer systems. These applications can be implemented in a wide variety of programming languages and run on a diverse set of platforms. Web services exchange data over local or global computer networks and through the use of consistent data interchange formats such as JSON, can enable interoperability , and provides resource representation such as JSON or XML and a set of HTTP Methods. The web services that have been implemented on CerealsDB are based on the WheatIS Grassroots API to ensure that we share common nomenclature with the wider community. The Grassroots API is able to expose data and analytical services through a consistent descriptive specification and industry-standard software packages, so that when two or more data resources use the Grassroots API, they can easily be federated together and enable a single view over these multiple disparate resources.Figure The available web services can be accessed by sending a JSON request to CerealsDB (via cURL or a perl script for example). The initial web services request to CerealsDB is described in the Additional file The CerealsDB web services were written in Perl and example Perl and Python scripts are available to download from the website to help users construct queries. Additional examples of querying CerealsDB via the web services are provided on the site, describing the use of the cURL program on the command line and via a web browser’s address bar. These examples are available via the ‘WebAPI’ link in the horizontal menu bar.BLAST services on CerealsDB have been exposed to the WheatIS network using the Grassroots API  developehttp://www.cerealsdb.uk.net/cerealgenomics/CerealsDB/funcSNPs_select.php. Functional SNP prediction was performed using the functional analysis through hidden Markov models (FATHMM) technique described by Shihab et al. [e.g. Disease resistance) or FATHMM score. The response from CerealsDB contains information on the Contig that contains the SNP, along with the translated amino acid sequence and the location of the amino acid change. If a BLAST annotation is available for this SNP it is displayed with the BLAST e-value, alongside the SNPs associated FATHMM score and an indication as to whether the SNP has an effect on the proteins function. If the amino acid sequence has been annotated with a genbank accession, a “Uniprot button” appears to the right of the screen, which links out to the Uniprot website [A new search page that allows users to identify functional SNPs has been added to CerealsDB see Fig.  and can b et al. . Users c website  where ushttp://www.cerealsdb.uk.net/cerealgenomics/CerealsDB/KASP_SNPs_070616.vcf).CerealsDB has evolved into a resource for researchers and plant breeders who are primarily interested in wheat SNPs. In addition to CerealsDB, there are a number of websites, such as Ensembl Plants , the URGSince its inception, CerealsDB has maintained a policy of making its data freely available to the public without restrictions or intellectual property rights. Users of CerealsDB are not required to register and there are no areas of the site that are password protected. The inclusion of web services means that data can automatically flow from CerealsDB to other databases, integrating resources and expanding the user base. Potential developments for CerealsDB could include the addition of annotations from other sources such as haplotype and phenotypic information currently being generated, however this would clearly need to integrate with the sites simple functionality to avoid becoming overly complicated and more importantly it should aim to be distinct from existing wheat resources which are in danger of homogenisation.http://www.cerealsdb.uk.net/cerealgenomics/CerealsDB/funcSNPs_select.php) and would then enter the term ‘disease’ in the ‘search term’ field. This would produce a list of SNPs linked to disease resistance based on the annotation given to the contig in which the SNP is located. Some of these SNPs, such as BS00023108, will already have an assay designed for them and the primer sequences can be downloaded. Entering the SNP’s BS code in the ‘Quick search’ box in the right hand menu, reveals that the SNP is available in the KASP and Axiom platforms and includes information such as KASP marker primer sequences that have been validated for this SNP, along with the contig sequence for BC000011796 (with SNP highlighted in red) and a chromosomal location on 7D. Should the user require additional detail, a simple click on the UniProt link takes them through to that website for information on gene ontologies and domains.An example case study is included here to illustrate how simple it is to use the website. A user wanting to search for SNPs linked to disease resistance genes (these play a major role in plant responses to pathogens and pests and are of major interest to plant breeders) would start by selecting ‘Functional SNPs’ from the search page  have been adhered to, helping to guarantee their wider adoption by the community.The CerealsDB website has a large user base across the globe as evidenced by the number of hits the site receives each month. The simplicity and functionality of the site along with the policy of releasing all data into the public domain without restriction has ensured that both plant breeding and wheat research communities continue to employ CerealsDB as an important tool in their efforts to develop new varieties of wheat that could help to solve global food shortages and deliver food security to the world’s burgeoning population.http://www.cerealsdb.uk.net/cerealgenomics/CerealsDB/indexNEW.php. The SNP database is publicly and freely accessible, requiring no registration and with no restrictions on use. The non-synonymous and functional SNPs are available at the following URL: http://www.cerealsdb.uk.net/cerealgenomics/CerealsDB/funcSNPs_select.php.CerealsDB can be accessed online from the following URL: It is recommended that one of the following browsers is used: Mozilla Firefox 3 on Linux, Mac OSX or Windows, Internet Explorer 8 on Windows, Safari 4 on Mac OsX or Windows, Chrome on Linux or Windows.API, Application programming interface; BC, Bristol contig; BLAST, Basic local alignment search tool; BS, Bristol SNP; CPU, Central processing unit, CSS, Cascading style sheets; DArT, diversity array technology; ER, Entity-relationship; EST, Expressed sequence tag; FAO, Food and Agriculture Organisation of the United Nations; FAQ, Facts and questions; Gb, gigabytes; HTTP, Hypertext transfer protocol; ID, identification; JSON, Javascript object notation; MAS, marker-assisted selection; Mb, megabytes; NCBI, National Center for Biotechnology Information; NGS, Next generation sequencing; OS, Operating system; PHP, Hypertext pre-processor; RDMS, Relational database management system; REST, Representational state transfer; SNP, Single nucleotide polymorphism; TGAC, The genome analysis centre; URGI, Unité de Recherche Génomique Info; URI, Uniform Resource Identifier; URL, uniform resource locator; VCF, variant call format