SLC2A9  and near NLN . Six additional genome-wide suggestive SNP  were identified including markers in or near PIK3CB , KCNB1 , ZC3H12C , JPH1 , and MYO3B . This is the first report of specific gene regions associated with congenital entropion in any mammalian species, to our knowledge. Further, none of these genes have previously been associated with any eyelid traits. These results represent the first genome-wide analysis of gene regions associated with entropion and provide target regions for the development of sheep genetic markers for marker-assisted selection.Entropion is an inward rolling of the eyelid allowing contact between the eyelashes and cornea that may lead to blindness if not corrected. Although many mammalian species, including humans and dogs, are afflicted by congenital entropion, no specific genes or gene regions related to development of entropion have been reported in any mammalian species to date. Entropion in domestic sheep is known to have a genetic component therefore, we used domestic sheep as a model system to identify genomic regions containing genes associated with entropion. A genome-wide association was conducted with congenital entropion in 998 Columbia, Polypay, and Rambouillet sheep genotyped with 50,000 SNP markers. Prevalence of entropion was 6.01%, with all breeds represented. Logistic regression was performed in PLINK with additive allelic, recessive, dominant, and genotypic inheritance models. Two genome-wide significant  SNP were identified, specifically markers in  Entropion is an eye health problem where the eyelid rolls toward the eye, causing the hair and lashes to contact the eye and can cause blindness . BlindneThere is evidence of genetic components to the development of entropion in sheep . ReporteGenotyping technologies exist which improve the probability of identifying genomic regions associated with phenotypic traits of interest in sheep. The Ovine SNP50 beadchip  was collAll animal care and handling procedures were reviewed and approved by the Washington State University Institutional Animal Care and Use Committee (Permit Number: 3171) and/or by the U.S. Sheep Experiment Station Animal Care and Use Committee . All efforts were made to minimize discomfort during collection of blood samples.At the U.S. Sheep Experiment Station (USSES), trained technicians determine entropion status within 48 hours of birth with entropion defined as the inward rolling of one or both lower eyelids in lambs. Upper eyelid entropion has never been documented is USSES lambs. All sheep born alive had entropion status recorded as either present or absent. Whole blood was collected from ewes of Rambouillet (N = 414), Polypay (N = 438), and Columbia (N = 146) breeds, ages 1–5 years. Entropion was documented in 60 of these ewes, with all breeds represented. These animals were managed in an extensive rangeland production system and bred in single sire mating pens.Blood was collected by jugular venipuncture into EDTA-coated vacutainer tubes. DNA was isolated using the Invitrogen GeneCatcher gDNA 3–10 ml Blood Kit as per manufacturers’ instructions . The DNAs were checked for quality and quantity using an ND-1000 spectrophotometer  and equilibrated to 50ng/μl for genotyping. Genotyping services were provided by Geneseek Inc.  using the OvineSNP50 Infinium BeadChip  with a set of 54,977 SNP designed by the International Sheep Genome Consortium .-5 per Wellcome Trust consortium guidelines [http://gettinggeneticsdone.blogspot.com/2011/04/annotated-manhattan-plots-and-qq-plots.html, viewed on 11-15-11) was used for visualization of results in Manhattan and quantile-quantile plots. Further, the top SNPs were interrogated using a threshold model similar to those described above, accounting for entropion status, breed/ population cluster and genotype in the probit procedure of SAS 9.4 . Ensembl was used to determine the location of the SNP within genome assembly OAR_v3.1 as well as identify Ensembl annotated genes within 100 kb of the SNP. A preliminary screen for high genotype call rates (>97%) was performed to select individuals for further analysis. Multidimensional scaling analysis (MDS) of breed groups , Polypay (n = 423), and Columbia (n = 142). These included 59 sheep with entropion where all breeds were represented. The average genotyping call rate was 98.06% for the final population of sheep evaluated.http://www.ncbi.nlm.nih.gov/projects/SNP/snp_viewBatch.cgi?ibid=1062140 or http://www.animalgenome.org/repository/pub/USDA2015.0208/ Threshold analysis showed rs424438792, rs720662001, rs420083564, rs703034846, rs419388939, and rs405483139 significantly (P < 0.04) and rs415069937 and rs401620279 were not (P > 0.06) associated with entropion. The sheep genome assemble Oar_v3.1 [SLC2A9, a.k.a. GLUT9), phosphatidylinositol 4,5-bisphospate 3-kinase, catalytic subunit beta isoform (PIK3CB), myosin-IIIb (MYO3B), potassium voltage-gated channel subfamily B member 1 (KCNB1), neurolysin (NLN), zinc finger CCCH-type containing 12C (ZC3H12C), and junctophilin 1 (JPH1).Manhattan plots showing P-values in order of chromosome position, from the genotypic and dominant analysis, are presented in Oar_v3.1  was usedEyelid development in embryonic mammals has long been known to consist of four primary stages: specification, growth, epithelial fusion, and reopening , 38. ManHowever, much less is known about causes of the entropion, and especially about congenital entropion. Studies of entropion with a large infectious disease component (trachomatous entropion) have implicated contraction of the subepithelial fibrous membrane, which is formed by vertically oriented parallel collagen fibers . Age-relThis is the first genome-wide association study to identify gene regions for any form of entropion in mammals, to our knowledge, and we identified two genome-wide significant and 5 genome-wide suggestive regions. None of the genes associated with the identified SNP were found directly within the Activin-MEKK1-JNK or TGFalpha-EGFR-ERK pathways .SLC2A9. This SNP was identified in both the recessive and genotypic analysis, the genotypic model had both the smallest P-value  and the largest odds ratio (41.77) of any SNP in this study. The odds ratio indicated a large difference in odds of entropion by genotype, well above the odds ratio of 2 commonly used as a threshold for large effect size [SLC2A9 gene encodes a glucose, fructose, and urate transporter with alternate splice isoforms expressed on different surfaces of polarized cells [SLC2A9 gene is a major contributor to glucose influx [SLC2A9/GLUT9 has been shown in muscle cells at the mRNA and protein levels, and the GLUT9 protein may play a role in muscle cell proliferation [SLC2A9 could be involved in muscle tone contributions to the development of entropion. While the SLC2A9 gene has been most frequently associated with gout and renal disease in humans [SLC2A9 in connection to eyelid shape and positioning.The first genome-wide significant SNP (s65132) was located on chromosome 6 within ect size . The SLCed cells , 46. In e influx . Expressferation . Thus, in humans , 50, thiNLN in version 3.1 of the ovine genome [SLC2A9 might be functionally related to NLN in the development of entropion through the influence of sugar transport on neurotensin activity.The chromosome 16 genome-wide significant SNP (OAR16_14874751), identified in both the additive and dominant analysis, had a large odds ratio of 11.01 in the dominant analysis, and was within 35Kb of e genome . Neurolye genome . In addie genome . Furthere genome , which ce genome . Interese genome . Since me genome , it is pPIK3CB which is found on the outer membrane of eukaryotic cells and is important in many cellular pathways [PIK3CB may modulate cell morphology [PIK3, has been shown to be required for mesenchymal stem cells to differentiate into osteoblasts, adipocytes, and chondrocytes in vitro [MYO3B which is a protein that mediates movement along actin filaments in the cell [MYO3B may play a role in adipose deposition [KCNB1 which plays a role in apoptosis in neurons [ZC3H12C. This recently identified gene has been found to inhibit inflammation in vitro [JPH1, was identified in the top 5 SNPs of both the additive and dominant analysis. This gene is expressed in skeletal muscle and plays a role in intramembrane Ca2+ movement [JPH1 has been shown in inactive, weak skeletal muscle [The other 6 genomic loci identified in this study were genome-wide suggestive. Specifically on chromosome 1, the two suggestive SNP were adjacent to one another on the Ovine SNP50 beadchip, were counted as one genomic locus, and were in the top 5 SNP of the additive and dominant analysis. This locus encompassed pathways . PIK3CB rphology , cell dirphology , thus poin vitro . The chrthe cell . A GWAS position . In cont neurons  and may  neurons . Cells win vitro . The chrmovement . Reducedl muscle . If the In conclusion, we undertook the first genome-wide association study of entropion in any mammal to provide insight into genes and mechanisms influencing this condition. This research identified five chromosomal regions that were associated with entropion in three common U.S. sheep breeds, including two that were associated with very large odds ratios. Several genes have logical involvement in the development of entropion, but none had been implicated in eyelid traits previously. These results provide insight into the development of entropion in mammals, and they provide target genes and genomic regions for mutation discovery, which is ongoing. Furthermore, identifying and validating one or more markers for marker-assisted selection from future work in these genomic regions would help producers to reduce the incidence of entropion within their sheep flocks and potentially improve production.S1 FigColumbia are included in the top cluster, Polypay in the bottom right cluster, and Rambouillet in the bottom left cluster. The clustering of individuals by breed is clear even from these related breeds. This also shows how the Rambouilllet were split into 2 groups with the small cluster above and to the right of the main cluster .(PDF)Click here for additional data file.S2 FigQuantile-quantile plots from association with entropion, where the expected distribution is the red line.(EPS)Click here for additional data file.S3 FigQuantile-quantile plots from association with entropion, where the expected distribution is the red line.(EPS)Click here for additional data file.S1 Table(DOCX)Click here for additional data file.S2 Table(DOCX)Click here for additional data file.


Patients may fear the symptoms and consequences associated with hypoglycemia. We tested whether fear of hypoglycemia is independently associated with poorer health-related quality of life (HRQOL).Data were collected using direct-mail survey and enrollment information from adult commercial health plan enrollees with type 2 diabetes during a 12-month period (12/01/2008 to 11/30/2009). HRQOL was evaluated by the EuroQol (EQ)–5D index and 12-item Short Form Health Survey Mental Component Summary (SF-12 MCS) and Physical Component Summary (SF-12 PCS). Fear of hypoglycemia was assessed using the Hypoglycemia Fear Survey (HFS). Two ordinary least-squares (OLS) models of HRQOL controlling for demographics and illness characteristics were specified, and OLS regression coefficients and statistical inferences were compared. Model 1 included 1 variable of hypoglycemia symptoms; Model 2 included both hypoglycemia symptoms and HFS score.P < 0.05). Model 2: hypoglycemia symptoms were significantly associated only with SF-12 MCS score. HFS total score was significantly associated with all 3 HRQOL scores. Hypoglycemia symptoms, Hispanic ethnicity, and longer diabetes duration were associated with greater hypoglycemia fear. Higher income, white race, and treatment without sulfonylurea or insulin were associated with less hypoglycemia fear .Of 3999 patients contacted, 813 responded to the survey. Model 1: hypoglycemia symptoms alone were associated with worse HRQOL  contains supplementary material, which is available to authorized users. Hypoglycemia is associated with increased risk of a variety of adverse clinical outcomes in patients with type 2 diabetes, including microvascular events , macrovascular events , and death -3. High Patients may fear the symptoms associated with a mild episode of hypoglycemia as well as the consequences of severe hypoglycemia, which may include seizure or loss of consciousness .Fear of hypoglycemia is a considerable problem in patients with diabetes ,12 and mP < 0.001), lower scores on the EuroQol (EQ)–5D index , and lower scores on the 12-item Short Form Health Survey (SF-12) Mental Component Summary (MCS) score (P < 0.001) and Physical Component Summary (PCS) score (P = 0.002). The objective of this retrospective analysis was to examine the relationships among symptomatic hypoglycemia, patient self-reported fear of hypoglycemia, and HRQOL while controlling for patient baseline demographic and disease characteristics. We tested the hypothesis that, beyond the effect of symptomatic hypoglycemia itself on patient HRQOL, fear of hypoglycemia is independently associated with lower HRQOL.In a cross-sectional survey study, we found that symptomatic hypoglycemia was associated with worse HRQOL as well as greater fear of hypoglycemia . CompareDetailed methodology for this cross-sectional observational research study was previously published . The stuFor the survey, patients (N = 3999) were contacted directly by mail. The patient survey assessed demographic variables, age at time of type 2 diabetes diagnosis, family history of diabetes, and smoking status. Patients were asked about their current OAD, duration of treatment, and modifications to their treatment within the previous 6 months. Medication adherence was assessed using the Morisky self-report medication adherence 4-item questionnaire .Fear of hypoglycemia was evaluated using the HFS . This vaHealth-related quality of life was assessed using 2 instruments. Overall health status was assessed using the EQ-5D, a standardized measure that rates 5 dimensions of health  across 3 levels  ,20. The The survey packet we used in this study contained a pre-paid cash incentive of $10 to the patients, along with the invitation letter printed on letterhead, the IRB-approved consent form, the survey instrument printed in booklet form, and a postage-paid business reply envelope. We sent reminders to the patients 2 and 4 weeks following the initial survey to increase our response rate.t-test or chi-square test) based on the distribution of the measure. Multivariate analyses of HFS, SF-12, and EQ-5D outcomes were conducted using ordinary least squares. Specific predictors  to be included were selected primarily based on clinical rationale. Associations among symptomatic hypoglycemia, fear of hypoglycemia, and HRQOL were modeled in 2 ordinary least-squares regressions, controlling for sociodemographics, illness characteristics, and treatment factors ) than men (66.7% [315/472]) reported ≥1 hypoglycemia symptom.Of 3999 patients contacted, 813 responded with a complete survey . Of these, 578 (71.1%) reported ≥1 hypoglycemia symptom within the previous 3 months. Among the population characteristics assessed, only OAD group and sex showed a statistically significant difference between patients with and without symptomatic hypoglycemia . Other factors also showed statistically significant associations (Table P < 0.0001), as was white racial categorization compared with nonwhite racial categorizations (P < 0.0001). Hispanic ethnicity was associated with greater fear of hypoglycemia (P = 0.03). Longer duration with a diagnosis of diabetes and better self-reported adherence were also associated with greater fear of hypoglycemia (each P < 0.0001).Symptomatic hypoglycemia was associated with more fear of hypoglycemia  and worse HRQOL . In Model 2, which included the additional variable of HFS, the HFS score was significantly associated with scores on all 3 HRQOL outcomes (each P < 0.0001), whereas symptomatic hypoglycemia showed a statistically significant association only for the SF-12 MCS (P = 0.022). The fully specified regression models for Table The ordinary least-squares model of HRQOL using symptomatic hypoglycemia only and controlling for demographics and illness characteristics . Model 2 showed that the HFS score was still significantly associated with scores on all 3 HRQOL outcomes (each P < 0.05), whereas symptomatic hypoglycemia showed no statistically significant association with any of the 3 HRQOL outcomes (each P > 0.05). For sulfonylurea users (n = 246), Model 1 showed that the symptomatic hypoglycemia was associated with EQ-5D scores and SF-12 MCS scores (each P < 0.05). Model 2 showed that the HFS score was significantly associated with scores on all 3 HRQOL outcomes (each P < 0.05), whereas symptomatic hypoglycemia was only significantly associated with EQ-5D scores and SF-12 MCS scores (each P < 0.05). The fully specified regression models for Table Subgroup analyses were conducted for insulin users Table  and sulfers n = 26, Model In patients with type 2 diabetes treated with antihyperglycemic therapy, hypoglycemia imposes a significant physical and psychological burden . PreviouA significant association was observed between symptomatic hypoglycemia and EQ-5D and SF-12 MCS scores in Model 1, whereas only the association with SF-12 MCS was significant after incorporating the HFS into the model. In contrast, fear of hypoglycemia demonstrated a significant association with all 3 HRQOL outcomes. This finding suggests that, statistically, fear of hypoglycemia may be a more important predictor than hypoglycemia itself of patient well-being and health status.Multiple factors associated with fear of hypoglycemia were identified. Consistent with our study  and otheIn the current analysis, Hispanic/Latino ethnicity, duration of diabetes, and self-reported adherence also showed a positive association with fear of hypoglycemia, whereas household income and white race showed a negative association. These data suggest that demographic variables might be helpful in predicting those patients most likely to experience fear of hypoglycemia. The positive association between duration of diabetes and fear of hypoglycemia could reflect greater insulin use in patients with a longer duration of disease. Alternatively, an independent association between these variables would seem to further indicate that experiencing hypoglycemia increases fear of the event.As no causality can be inferred in this cross-sectional analysis, the positive relationship between self-reported adherence and fear of hypoglycemia is difficult to interpret. In a previous study, patients reporting hypoglycemia symptoms were more likely to report barriers to adherence  . In addiPredictors of hypoglycemia in patients with type 2 diabetes are better understood. In the current analysis, more women versus men and more patients treated with OAD plus insulin versus sulfonylurea without insulin or nonsulfonylurea OAD without insulin reported hypoglycemia. The difference in hypoglycemia with respect to diabetes therapy is to be expected, given that insulin and insulin secretagogues are the most common cause of hypoglycemia in patients with type 2 diabetes . The obsThis analysis has some limitations. Because the study was cross-sectional, no causal relationships can be inferred from the findings. Data on the frequency and severity of hypoglycemic episodes were not collected so their contribution to fear of hypoglycemia could not be assessed. Also, because the survey design used a convenience sample of patients that included only those who were members of specific health plans, with voluntary participation, survey participants may not be representative of all patients with type 2 diabetes. The study also relied on patient self-report, and the survey had a patient recall period of 6 months. Longitudinal cohort studies are warranted to further understand the impact of hypoglycemia and to test potential interventions to address the issue of hypoglycemia fear to improve health for patients with diabetes. In conclusion, in addition to symptomatic hypoglycemia itself, fear of hypoglycemia was independently associated with lower HRQOL  in patients with type 2 diabetes. There is an unmet need for patient education programs that address patient fear of hypoglycemia and use of medications with a lower risk of hypoglycemia.


Bos indicus cattle (e.g. Nelore breed) are characterized, in general, by lower levels of linkage disequilibrium between genetic markers at short distances, compared to taurine breeds. Thus, it is important to evaluate the accuracy of imputation to better define which imputation method and chip are most appropriate for genomic applications in indicine breeds.Genotype imputation from low-density (LD) to high-density single nucleotide polymorphism (SNP) chips is an important step before applying genomic selection, since denser chips tend to provide more reliable genomic predictions. Imputation methods rely partially on linkage disequilibrium between markers to infer unobserved genotypes. Accuracy of genotype imputation in Nelore cattle was evaluated using different LD chips, imputation software and sets of animals. Twelve commercial and customized LD chips with densities ranging from 7 K to 75 K were tested. Customized LD chips were virtually designed taking into account minor allele frequency, linkage disequilibrium and distance between markers. Software programs FImpute and BEAGLE were applied to impute genotypes. From 995 bulls and 1247 cows that were genotyped with the Illumina® BovineHD chip (HD), 793 sires composed the reference set, and the remaining 202 younger sires and all the cows composed two separate validation sets for which genotypes were masked except for the SNPs of the LD chip that were to be tested.Imputation accuracy increased with the SNP density of the LD chip. However, the gain in accuracy with LD chips with more than 15 K SNPs was relatively small because accuracy was already high at this density. Commercial and customized LD chips with equivalent densities presented similar results. FImpute outperformed BEAGLE for all LD chips and validation sets. Regardless of the imputation software used, accuracy tended to increase as the relatedness between imputed and reference animals increased, especially for the 7 K chip.If the Illumina® BovineHD is considered as the target chip for genomic applications in the Nelore breed, cost-effectiveness can be improved by genotyping part of the animals with a chip containing around 15 K useful SNPs and imputing their high-density missing genotypes with FImpute.The online version of this article (doi:10.1186/s12711-014-0069-1) contains supplementary material, which is available to authorized users. Genomic information from dense single nucleotide polymorphism (SNP) chips provides the opportunity to increase the rate of genetic progress in breeding programs, if a sufficient number of markers and animals with phenotypes  are genotyped . BecauseThe Nelore (indicine) breed is the most important beef cattle breed in Brazil . For thiThe objective of this study was to assess the accuracy of genotype imputation in Nelore cattle, using different imputation methods, different commercial and customized SNP chips and sets of animals whose genotypes were to be imputed. The importance of relatedness between validation and reference animals was also evaluated for the different chips and methods.Both sires and dams of the Nelore breed were genotyped. Sires that were widely used for artificial insemination were chosen as representative of the main Nelore breeding programs in Brazil. A total of 995 sires born between 1955 and 2008 were genotyped, spanning over 10 generations. A total of 1247 dams born between 1993 and 2008 were also genotyped. They included part of the genomic selection reference population of a commercial breeding program (DeltaGen) and were chosen among the dams that had the highest EBV accuracies for weaning, yearling and reproductive traits. Details about pedigree information of the genotyped animals are in Table G) was defined as G = MM′/Σ2pi(1-pi), in which M is the incidence matrix of markers whose elements in the ith column are 0-2pi, 1-2pi and 2-2pi for genotypes AA, AB and BB, respectively, and pi is the frequency of allele B at the ith marker [G, only the HD markers that passed quality control (described later) were used. As shown in Table To evaluate the accuracy of genotype imputation, the animals were divided into reference and validation sets. The reference set comprised 793 sires that were born before 2005. Two separate validation sets were considered, including the animals most likely to have their genotypes imputed in practice: the first with the 202 younger sires and the second with the dams. Reference sets with different numbers of animals were not considered because the effect of size of the reference set on imputation accuracy is well documented in the literature e.g. . StatistAll sires and dams were genotyped with the HD chip, which contains approximately 777 K SNPs. Animals from the validation set had their HD genotypes masked, except for the genotypes of markers present on the LD chip under evaluation, thus mimicking a situation in which these animals were genotyped with LD chips.Imputation from commercial LD panels to the HD chip was simulated by assuming that genotypes of animals from the validation set were available only for markers that were present on the HD and the following commercial chips: Illumina® BovineLD (7 K), Illumina® BovineSNP50 v2 (50 K) and GeneSeek® Genomic Profiler 20 K and 75 K for indicine breeds .2 [2 with other SNPs from the same window was selected from each window.Eight customized (mimicked) 15 K LD chips were also tested, with varying densities and SNP selection criteria. Testing customized LD chips with less than 15 K SNPs was not relevant because their cost-effectiveness would not be attractive . Using markers of the HD chip that passed quality control (see below), four 15 K chips were simulated based on selection of one marker from each window of 29 subsequent markers, according to the UMD v3.1 assembly. For the first 15 K chip (15 K_e), SNPs were evenly spaced by selecting the last marker from each window. For the second 15 K chip (15 K_em), the SNP with the highest minor allele frequency (MAF) was selected from each window, and for the third chip (15 K_el), the SNP with the highest average linkage disequilibrium with other SNPs from the same window was selected from each window. The r2  was adopThe remaining four customized LD chips were developed based on the add-on concept offered by Illumina®, where additional SNPs can be added to an existing commercial chip in a cost-effective way. The Illumina® Bovine LD chip (7 K) was used as the base chip. Additional SNPs were selected using the same criteria as used for the 15K_eml chip. Windows containing 39, 25, 16 and 9 subsequent markers were used to compose chips with densities around 18 K (11a7 K), 24 K (17a7 K), 34 K (27a7 K) and 55 K (48a7 K) markers.−5; (4) SNPs with a MAF less than 0.02; and (5) SNPs with a call rate per SNP less than 0.98 . After these edits, 439 595 SNPs remained. All samples from the reference set had a call rate per individual greater than 0.9 for SNPs passing quality control and were kept for the analyses.Quality control was performed for HD genotypes of the reference set, using the following criteria for excluding SNPs: (1) SNPs that were located in non-autosomal regions; (2) SNPs that had the same genomic coordinates, i.e. mapped to the same positions (just the replicates were removed); (3) SNPs with a p-value in the Hardy-Weinberg equilibrium z-test  less thaSNPs excluded from the reference set were also discarded from the validation sets, in addition to masking subsets of HD SNPs as previously described. For each LD chip, the numbers of SNPs that were shared with the HD chip before and after quality control are in Table Imputation of genotypes from the LD chips to the HD chip was performed using the software packages BEAGLE v.3.3  and FImpConsidering the two sets of animals to be imputed (young sires and dams), the 12 SNP chips to be tested , and the three methods (BEAGLE and FImpute considering or ignoring pedigree), a complete factorial comparison would require 72 imputation analyses. As illustrated in Figure Analyses 1 to 12 were used to compare SNP chips. Results from analyses 13 to 16 were compared to those of their counterpart analyses  to evaluate the gain in accuracy when considering family information with FImpute. Results from analyses 17 to 23 were compared to those from analyses 1 to 4, 8, 9 and 12, to evaluate imputation accuracy when using different sets of animals to be imputed. Finally, analyses 24 to 29 and 30 to 35 were performed to infer accuracy of the imputed genotypes of Nelore young sires and dams using BEAGLE and different LD chips.Two criteria were used to assess imputation accuracy. The first was the percentage of correctly imputed genotypes (PERC). For incorrectly imputed genotypes, either one or both alleles can be imputed incorrectly. To distinguish between these two cases, the Pearson’s correlation (CORR) between imputed and observed genotypes  was also computed, as in Hickey et al. . Both PEThe impact of genetic relatedness between validation and reference animals on imputation accuracy was assessed by regressing CORR on the average of the top 10 genomic relationships between each animal in the validation set with all the animals in the reference set .Results for CORR and PERC obtained with the different LD chips  are in Table Imputation accuracy was greater than 0.97 for all chips except for the 7 K chip. Considering the proportion of SNPs to be imputed (99.1%), the imputation accuracy was high even for the 7 K chip. As documented in the literature ,16, impuAmong the commercial chips, imputation accuracies with the 50 K and GGP20Ki chips were similar and outperformed that of the 7 K chip, while the GGP75Ki chip had the best accuracy. Among the virtual 15 K chips, selecting SNPs based on MAF (15K_em) rather than on linkage disequilibrium (15K_el), in addition of being evenly spaced, tended to result in slightly higher imputation accuracies. The highest accuracy was observed when both criteria and even spacing were combined to define the SNP content of a virtual chip (15K_eml). However, the increase in accuracy was nominal compared to the 15K_e evenly spaced chip. Imputation accuracy of the 11a7 K chip was comparable to that of 15K_eml, with the potential benefit of the former being cheaper to be manufactured/acquired by adding SNPs on the existing 7 K chip.Although commercial and customized chips resulted in similar imputation accuracies, the customized chips that had the highest accuracies outperformed commercial chips with a similar density . For instance, the average accuracy of the 15K_eml chip was 0.6% and 0.7% higher than those of the 50 K and GGP20Ki chips, respectively.Results of FImpute analyses with and without pedigree information are in Table Imputation accuracy was lower for dams than for young sires, especially for lower density chips Table . For insFImpute outperformed BEAGLE for the different chips and validation sets in terms of average accuracy Table . The greThe impact of relatedness between validation and reference animals on imputation accuracy is illustrated in Figure Although imputation accuracy was in general high, SNP-wise imputation accuracy is relevant also. For brevity, only the result of analysis 9 for bovine autosome 1 is presented in Figure Imputation methods rely partially on linkage disequilibrium between markers to infer unobserved genotypes. The Nelore breed has lower levels of linkage disequilibrium at short distances than taurine breeds ,7. NeverImputation accuracy has a large influence on the reliability of genomic predictions ,4,20. MuDifferent (mimicked) customized LD chips were tested in order to evaluate changes in imputation accuracy when criteria for SNP selection were modified. As in Mulder et al. , selectiThe customized chips that showed the highest imputation accuracy slightly outperformed the commercial chips with an equivalent density. It is important to mention that commercial and customized chips cannot be properly compared, since the design of the customized chips used information on genotypes from the same population than that to be imputed. However, it does highlight the importance of using population-specific information to design LD chips.Imputation accuracy was not as much affected by pedigree information as by using different imputation methods. FImpute resulted in higher imputation accuracies for the different chips and validation sets than BEAGLE. Ma et al.  found thAnother factor that influenced imputation accuracy was the level of relatedness between imputed and reference animals. In agreement with the literature ,19,24,25For some genomic applications (e.g. genome-wide association (GWA) studies), SNP-wise imputation accuracy is relevant to prevent the propagation of genotyping errors. As in Erbe et al.  and VanRFinally, it is not clear if the Illumina® BovineHD chip should be considered as the target high-density chip for genomic applications in the Nelore breed. Recent genomic prediction results (not published) have revealed that the 50 K and HD chips share similar predictive abilities for different traits in Nelore cattle. At present, it is not clear to what extent the size and composition of the reference population influence these results. As reported by VanRaden et al. , the benOur results indicate that if the HD chip is considered as the target chip for genomic applications in the Nelore breed, cost-effectiveness can be improved by genotyping part of the economically marginal animals with an LD chip that contains around 15 K useful SNPs and imputing the missing HD genotypes. A denser LD chip (50 K useful SNPs) is recommended for animals that are poorly related to the reference population. For the current Nelore population, FImpute is preferred over BEAGLE for imputation of missing genotypes.


Bemisia tabaci (Gennadius) (Hemiptera: Aleyrodidae) is a major pest of economically important crops worldwide. Both the United Kingdom (UK) and Finland hold Protected Zone status against this invasive pest. As a result B. tabaci entering these countries on plants and plant produce is subjected to a policy of eradication. The impact of B. tabaci entering, and becoming established, is that it is an effective vector of many plant viruses that are not currently found in the protected zones. The Mediterranean species is the most commonly intercepted species of B. tabaci entering both the UK and Finland. The implications of maintaining Protected Zone status are discussed.The sweetpotato whitefly,  Bemisia tabaci (Gennadius) (Hemiptera: Aleyrodidae) is a major pest of economically important crops worldwide .,16.B. taBemisia has also increasingly entered on imported bedding and pot plants, particularly on Mandevilla, in the spring-time [Bemisia are planted outdoors. The Mediterranean species, due to its tendency of resistance development and, among other things, better development rates at both high and low temperatures [Both the Mediterranean and MEAM1 species are also regularly intercepted and, as a result, undergo a process of eradication in Finland . The majing-time . This deeratures , is estieratures .B. tabaci transmitted viruses on crops and particularly by those begomoviruses causing yellow leaf curl diseases in tomato, which have a severe impact on crop cultivation and yield in all regions where tomato is grown. The synergy between the two highly invasive whitefly species (MEAM1 and Mediterranean) and both TYLCV and TYLCSV, which are highly infectious to tomato (as well as pepper and beans), efficiently transmitted by B. tabaci and adaptable to environments where tomato is cultivated, are characteristics of an invasive virus. Most B. tabaci transmitted viruses cause high impact in crops and, generally, losses from those virus diseases can be considered as very economically important [Impact is predominantly related to diseases caused by mportant .Bemisia-associated viruses in Finland, and the likelihood of TYLCV/TYLCSV entering and establishing in Finland via the pathway of imported ornamental plants is considered small [Lisianthus [Bemisia and TYLCV/TYLCSV.Thus far there are no known introductions of ed small . This issianthus  are eithsianthus . Furthersianthus , are allBemisia-transmitted plant viruses in the PZs. Firstly, it is not known what proportion of the intercepted insects are viruliferous, and how often virus-infested but symptomless plants enter the country together with the vector. It is known that viruliferous insects do sometimes arrive into PZ areas as TYLCSV has been recently detected in B. tabaci entering the UK [E. pulcherrima that are regularly imported to the PZ countries and are often infested with B. tabaci. The vector can acquire TYLCV/TYLCSV from some symptomless non-solanaceous host plants [B. tabaci, either with or without plant viruses, moving from ornamentals to vegetable production in the PZ countries is not known. For viruses entering with B. tabaci on ornamentals, there is a risk of outbreaks when tomato and ornamentals are grown in the same environment at the same time. Such virus outbreaks can be eliminated if detected in time, as exemplified by an eradication campaign conducted in The Netherlands in 2007 [There are three major knowledge gaps with respect to the impact of g the UK . Secondlt plants , whereast plants . Finally in 2007 , specifi in 2007 , and the in 2007 . HoweverB. tabaci moving from ornamentals to vegetable production needs to be understood better to be able to estimate impacts of B. tabaci establishing in PZ countries. Imported ornamentals were most likely the initial access route for the glasshouse whitefly Trialeurodes vaporariorum to Finnish vegetable production; currently this pest is causing the most problems in vegetable production owing to changes in production strategies. Heikkilä and Vänninen [B. tabaci spreading from ornamentals to vegetable production. The likelihood of the move depends on issues such as the geographical proximity of ornamental and vegetable production sites, the extent to which the pest can be controlled in ornamental production, and the logistic networks of traded ornamentals within the country. These factors are coupled with outdoor conditions that impact pest mortality to a higher or lesser extent should the pest attempt spreading through the outdoor environment. The results are likely to differ by PZ country since conditions influencing the spread of the pest vary.The likelihood of Vänninen  used stoT. vaporariorum can be used as a proxy for exploring the establishment, spread and impacts of B. tabaci. In production clusters particularly, the advent of year-round production from the 1990s onwards has resulted in region wide management difficulties of the glasshouse whitefly in both the new and the old  production forms. The growers routinely apply preventive control against the glasshouse whitefly in both tomato and cucumber, since practically all crops get infested at some point of the cropping cycle [B. tabaci in terms of temperature [B. tabaci in Finnish vegetable production in the same way as T. vaporariorum now persists and spreads there, especially in production clusters [The establishment potential and impacts of the pest itself, in the absence of plant viruses, should be estimated taking specific characteristics of glasshouse production in each PZ into account. A characteristic feature of Finnish glasshouse horticulture is year-round production using artificial lighting. In these conditions, ng cycle . Year-roperature  and daylperature ,39. Uninclusters ,41.B. tabaci populations would still apply. Revoking the status would place PZ countries in a similar position to other northern countries where B. tabaci cannot establish, with systematic inspections limited to material imported from outside the EU. For host material produced within the EU, there would be no routine response to outbreaks or pre-emptive measures including statutory inspections. A free flow of EU traded commodities would likely increase occurrences and outbreaks of B. tabaci and with that the likelihood that viruses are transmitted to significant crops, tomato and cucurbits, causing high impact and severe crop losses. Maintaining the PZ status with the current Annex 1AI status would still support the zero tolerance approach towards B. tabaci, regardless of its origin with the benefit of a significantly reduced risk of entry of B. tabaci and the viruses it transmits. A UK Plant Health consultation on the future of PZ status in the UK [If PZ status was to be lifted for PZ countries, restrictions from Annex IV, Part A, n the UK , had an B. tabaci are large, they are considerably less than the potential benefits obtained by excluding the pest. The potential impact of the pest (and vectored diseases) in terms of crop losses in salad crops potentially being much more important than the effect of the pest in terms of increasing the costs associated with controlling it. The loss of PZ would mean that growers of ornamental crops would have to control (but not eradicate) both B. tabaci and T. vaporariorum; however, salad growers could be confronted for the first time with the problem of controlling B. tabaci as well as T. vaporariorum whereas currently they have only had to deal with T. vaporariorum. Studies suggest that in tomato B. tabaci can replace T. vaporariorum due to the former’s superior competitive abilities [According to Defra , calculabilities . Having B. tabaci are no longer compensated for by government funds. Even so, the status quo was maintained due to risks associated with the Bemisia-vectored plant viruses and the specificities of Finnish vegetable production with highly conducive conditions for the pest’s reproduction. A cost-benefit analysis (CBA) of the PZ for a number of scenarios was conducted by Heikkilä [Bemisia spreads in six years to all poinsettia and in 30 years to about one sixth of the total cucumber and tomato area, or alternatively that the spread on tomato is faster than this. Accounting for uncertainty regarding the costs of control, the benefit:cost ratio was estimated to be 0.52‒2.63. This means that it could not be established with certainty whether to abolish or to maintain the PZ. Given the baseline parameter values, however, five of the six scenario comparisons favoured maintaining the PZ. The CBA also concluded that if Bemisia would spread to tomato or cucumber production and cause even moderate crop losses, maintaining the PZ would always be profitable. If, on the other hand, the risk of Bemisia spreading to vegetable production was small and the costs of control on flower farms remained moderate, then giving up the PZ could be an option. The CBA did not include Bemisia-associated viruses in the scenarios, but only the insect itself as a pest.In Finland, the debate about PZ status has been accentuated by a new plant health law according to which eradication costs of Heikkilä . The thrB. tabaci species complex predominates in specimens entering both the UK and Finland. Therefore, control and eradication programmes need to be able to deal with this species [B. tabaci species other than MEAM1 or Mediterranean are now also becoming more common on imported plant material [Bemisia cryptic species and resistance status, as well as the presence of plant viruses in the intercepted insects [The Mediterranean species of the  species . Most im species . Furthermaterial . Determi insects , are impBemisia tabaci is a worldwide pest and major virus vector. Originally known as a pest of sub-tropical crops, the species is now widely distributed under glass in temperate areas including most of Europe and on outdoor crops in southern Europe. Currently, it is not established in the UK or Finland. However, it could establish in protected environments, where it has the potential to be a major pest, particularly of glasshouse salad crops such as tomato and cucumber, given its potential to spread damaging viruses. The co-existence of year-round and seasonal vegetable production may specifically increase the establishment potential of the pest if it was to move from ornamental production to vegetable crops. The maintenance of protected zone status offers countries like the UK and Finland better control over their respective borders in regards to plant imports and in terms of protecting against introductions on EU traded material. Keeping the respective countries free from highly damaging plant viruses is vital to maintaining a healthy protected horticultural industry.


The domestic dog is a rich resource for mapping the genetic components of phenotypic variation due to its unique population history involving strong artificial selection. Genome-wide association studies have revealed a number of chromosomal regions where genetic variation associates with morphological characters that typify dog breeds. A region on chromosome 10 is among those with the highest levels of genetic differentiation between dog breeds and is associated with body mass and ear morphology, a common motif of animal domestication. We characterised variation in this region to uncover haplotype structure and identify candidate functional variants.MSRB3, which is involved in human hearing. These SNPs are in strong genetic linkage with another set of variants that correlate with body mass within the gene HMGA2, which affects human height. In addition we find evidence that this region has been under selection during dog domestication, and identify a cluster of SNPs within MSRB3 that are highly differentiated between dogs and wolves.We first identified SNPs that strongly associate with body mass and ear type by comparing sequence variation in a 3 Mb region between 19 breeds with a variety of phenotypes. We next genotyped a subset of 123 candidate SNPs in 288 samples from 46 breeds to identify the variants most highly associated with phenotype and infer haplotype structure. A cluster of SNPs that associate strongly with the drop ear phenotype is located within a narrow interval downstream of the gene We characterise genetically linked variants that potentially influence ear type and body mass in dog breeds, both key traits that have been modified by selective breeding that may also be important for domestication. The finding that variants on long haplotypes have effects on more than one trait suggests that genetic linkage can be an important determinant of the phenotypic response to selection in domestic animals.The online version of this article (doi:10.1186/s12864-015-1702-2) contains supplementary material, which is available to authorized users. The huge phenotypic variation in domestic dog breeds is the result of their unique evolutionary history, which involved two main phases. Firstly, the domestication of dogs from wolves, likely more than 15,000 years ago, involved selection for phenotypes necessary for life with humans . SubsequST between dogs and wolves and reduced heterozygosity in dogs, consistent with selective sweeps, has identified genetic variants that were likely selected during the descent of modern dogs. These include genes involved in starch digestion and brain function, which may underlie adaptation to new diet and behavioural changes [Recent studies have identified specific mutations involved in both phases of dog domestication. These studies give insight into the nature and timing of the dog domestication process and selective pressures involved , 9. Scan changes , 11. Stu changes –14. Thes changes , 16.Ear phenotypes are of particular interest because phenotypes of many domestic animals are floppy (drop) compared with their wild ancestors, including cattle, goats, rabbits and pigs. The presence of drop ears as a common correlate of domestication is argued to be related to pleiotropic effects of selection for tameness , 18. In ST values in this region are among the highest in the genome [IGF1 gene [MSRB3) gene and 5′ (upstream) of the high-mobility group AT-hook 2 (HMGA2) gene [A region on CFA10 (9.8 - 11.8 Mb canFam2.0) harbours a locus with highly divergent SNP frequencies between dog breeds. Fe genome –14. AcroGF1 gene  and the GF1 gene , 13. DerGF1 gene . WhereasGF1 gene , 13, bodGF1 gene , 21. TheA2) gene . InteresA2) gene . HoweverA2) gene . It is cHere we identified variants that are potential candidates for controlling phenotypic variation in ear morphology and body mass within this region on canine chromosome 10. We first performed targeted sequencing of the region in five pools of samples, each from a single dog breed, that differ in ear morphology and body mass. We compared patterns of variation in these sequences with those obtained from whole genome resequencing of a further five pools of dogs from various breeds and one pool of wolves. This enabled us to identify candidate SNPs that we then genotyped in a larger panel of dogs from various breeds, which allowed inference of haplotypes with strongest correlation to phenotype. We also analysed patterns of variation in this region in dogs compared with wolves and show that it has evidence for selection during dog domestication and identified a cluster of wolf-dog fixations, which could represent SNPs under selection during domestication.n = 242), 12 prick ear breeds (n = 108) and 14 intermediate ear breeds (n = 159). We estimated genome-wide significance using the breed permutation procedure used in ref. [raw = 7.5 × 10−92, pgenome-wide < 0.001), which lies between the MSRB3 and HMGA2 genes .We first tested for associations with body mass and ear type using a set of 509 samples from 46 breeds Table  typed usraw = 4.3 × 10−65, pgenome-wide = 0.001). These SNPs overlap the IGF1 locus previously implicated in body mass variation among dog breeds [raw = 8.2 × 10−45, pgenome wide = 0.033), which lies between MSRB3 and HMGA2 . The average body mass of drop ear, prick ear and intermediate ear breeds is 25.2 kg, 22.9 kg and 23.1 kg respectively. This indicates that the associations between body mass and ear type in the CFA10 region are independent of each other. We also performed GWAS for body mass within each of the three categories of ear type . Among 12 prick ear breeds there was a strong genome wide significant association with body mass on CFA15 near the IGF1 gene  but the signals within the CFA10 region were abolished, without any suggestive signals , which is highly associated with body mass and CFA10:11,000,274 (FST = 0.77) with is highly associated with ear type (see above). The extreme population differentiation in this region is indicative of strong artificial selection.In addition to correlations with morphology, previous studies have identified this CFA10 region as being one of the most highly differentiated among breeds , 13. In FST identified by refs [The evidence above suggests that a critical region on CFA10 harbours genetic variants responsible for ear type and body mass and has experienced selection due to the creation and maintenance of different dog breeds. We therefore decided to assay sequence variation in a 3 Mb interval encompassing this region (CFA10: 9.5 Mb - 12.5 Mb) in breeds with a variety of phenotypes in order to identify candidate genetic variants that control this variation. This region was selected to encompass the 1–2 Mb highly differentiated interval defined by  by refs . Using s by refs . These c by refs . We refeWe identified common SNPs in the SC dataset based on a cutoff of minor allele frequency >0.1 and then inferred the frequency of each SNP in each pool in all samples based on proportion of reads matching each allele (see methods). We identified 5,181 variable SNPs in the SC data using this approach. Each SNP was then classified as fixed for reference allele, fixed for non-reference allele, polymorphic or uninformative in each pool  mapped to the same region in 5 pools of dog samples from one or more breeds, and one pool of wolf samples presented by Axelsson et al. . All of MSRB3 gene and encompasses the HMGA2 gene. A cluster of seven candidate SNPs for ear type are found immediately downstream of the MSRB3 gene between 11.0 and 11.1 Mb  that associated with phenotype but did not identify any such cases. We used the SC data to scan the 500 kb associated region and flanking sequence using 100 bp windows to identify asymmetrical read depth between pools that could result from copy number variation  interval (CFA10:11.02-11.43 Mb) that encompasses the ear type region and extends into the 5′ end of the gene HMGA2  and/or body mass (raw p < 10−15) spanning 340 kb and inferred haplotypes present in each sample at these SNPs. We were able to infer the haplotypes present in 273 of 288 samples. In total, we inferred 29 different haplotypes. The six haplotypes that are present at frequencies >1.5 % in the dataset are shown in Fig. We selected 15 SNPs with the strongest associations to ear type . This reflects the apparent lack of recombinant haplotypes in the region , which is potentially indicative of a selective sweep. The region 11.0–11.1 Mb shows very high heterozygosity which is consistent with the presence of two haplotypes corresponding to the drop and prick eared phenotypes in this region. We leveraged the data from both sequence capture and WGS sequences to identify genetic variants that were fixed in dogs and wolves in this region . These SNPs are close to the cluster of SNPs that most strongly correlate with ear type  and 260 kb upstream of HMGA2 (CDS: 11.34–11.48 Mb).We have identified sets of SNPs strongly associated with ear type and body mass respectively and another set which have highly differentiated allele frequencies between dogs and wolves. Figure MSRB3 in the inner ear is localized in the auditory and vestibular sensory epithelia. There is therefore evidence that MSRB3 may be involved in ear function, and the SNPs could potentially exert their functional effects on ear morphology by modifying its expression, although a putative mechanism is elusive. We do not identify overlap between any of the SNPs and evolutionary constrained elements. Likewise, none of the SNPs are located within a known coding-region. Interestingly, although previous RNA-seq experiments across multiple tissues have not identified transcription in this region [MSRB3 or HMGA2. The cluster of dog-wolf fixed SNPs within the MSRB3 gene is restricted to intronic regions and the SNPs show no overlap with conserved elements or coding nucleotides. Any functional consequences of these SNPS are most likely to be regulatory.MSRB3-catalyzed reduction of methionine sulfoxides to methionine is essential for hearing  and a nos region , all sevHMGA2 gene, which is a strong candidate for involvement in body mass variation and correlates with several morphological phenotypes including height in humans [HMGA2. In the human genome, this SNP maps to a position  overlapping a H3K27Ac mark in HUVEC cells, suggesting a function in endothelial cells. It is also found within a DNaseI hypersensitive cluster observed in several cell types and an RNA polymerase II transcription factor binding site assayed by ChIP-seq [SNPs across the entire 340 kb haplotype region show similar levels of association with body mass. These include a cluster of SNPs within an intron of the n humans , 25. OneChIP-seq  in multiThe genetic basis of phenotypic variation is simplified in dogs due to inbreeding and strong artificial selection for variants with large effect , 13. OneMSRB3 and all of HMGA2. However, despite the presence of strong associations with phenotype and a highly localized signal, the number and identity of variants in the region with functional effects that control body mass and ears is unclear. Dissection of the haplotype structure in this region is consistent with the hypothesis that two tightly linked loci control the ear type and body mass traits independently. A number of SNPs immediately downstream of MSRB3 are good candidates for controlling ear type, whereas SNPs within the HMGA2 gene are good candidates for influencing body mass.Our analysis suggests that genetic variants controlling two traits are found on haplotypes that span a 340 kb region on chromosome 10 encompassing a region 3′ of HMGA2 locus are consistent between this study and another that assayed variation at this locus [HMGA2  lie within an 18.0 kb interval where no SNPs were identified by ref. [HMGA2 gene  lie with a 4.2 kb interval where no SNPs were found by ref. [The correlations between body mass and variation in the is locus . Interesis locus   produce the pleiotropic effects in chicken [cis-acting regulatory mutation in intron 6 of STX17 (syntaxin-17) is responsible for the pigmentation and melanoma susceptibility phenotypes in horse [The findings presented here demonstrate how response to artificial selection in domestic species can be affected by the genetic architecture of the trait under selection. There are a large number of examples of traits that commonly appear together. For example, there is an association between white coats and deafness in both cats and dogs , 41. Chi chicken  and a ciin horse .Drop ears are observed in several domestic animals including dogs, rabbits, cattle, pigs and goats and are often associated with other traits such as piebald coats, curly tails and smaller skulls. A similar suite of traits has also been observed in foxes selected for tameness in the classic Farm-Fox experiment . The leaWe have characterised a region on canine chromosome 10 that contains genetic variants that affect ear type and body mass. We suggest candidate mutations for both of these traits and provide further evidence that the region may have been under selection during dog domestication. This study demonstrates how the presence of linked variants influencing different traits could limit the combinations of phenotypes available for selection. Such genetic correlations restrict the palette available for both natural and artificial selection to work with to produce desired phenotypes and can reduce the rate of genetic adaptation.All DNA samples were collected from privately owned pet dogs with the owners’ consent according to relevant national and international guidelines. Ethical approval was granted by the Ethical board for experimental animals in Uppsala, Sweden (Dnr C138/6) and from US dogs by the Broad Institute based on a protocol approved by the MIT CAC (*0907-068-10 and 1109-127-12).et al. [Genome wide association studies for variation in body mass and ear morphology were performed using a dataset of 509 dogs from 46 breeds reported in Vaysse et al.   of 5 libraries, each comprised of a pool of 5 samples from a single dog breed, to enrich for a 3 Mb region 9.5–12.5 Mb on CFA10 (canFam2.0). The breeds used included two small breeds with non-drop ears  one large breed with non-drop ears (German Shepherd) and two large breeds with drop ears   compared with other pools. Sites from this breed were therefore considered missing unless a call could also be made from the other breed with the same phenotype  thus preventing the pattern of segregation in breeds with this phenotype from only being represented by the ESS sample. In order to be a considered a candidate variant for affecting body mass, a SNP was required to be fixed for one allele in the two pools derived from small breeds and fixed for another allele in all the three pools derived from the large breeds, whereas to be considered a candidate for affecting ear type, it was necessary that one allele was fixed in the two pools from drop ear breeds and another allele fixed in the three pools from prick ear breeds. The patterns of segregation among breeds of each phenotype were used to identify candidate mutations for the ear and body mass phenotypes using both loose and strict criteria.et al. study [We compared variants identified in SC data to the sequence data in the same region from whole genome sequencing (WGS) of pools of samples used in the Axelsson l. study . Out of Candidate SNPs were selected as those with the strongest correlation between patterns of segregation and either the drop or prick ear trait. This was performed by taking the SNPs that were selected as candidates for association with a trait in the SC pools, and comparing patterns of segregation in the WGS pools. Candidates from the SC pools were not considered further if one or more of the WGS pools were fixed for an allele inconsistent with an association with phenotype. We gave highest priority to candidates that matched according to the strict cutoff for fixation where 99 % of reads were required to match an allele for it to be considered fixed in a pool. Additional candidates were taken from the looser cutoff definitions. Within the critical interval 11.0–11.5 Mb, only 12 SNPs were identified with a cutoff of 70 %, of which all were included in the subsequent SNP panel. Within the same interval, 80 body-mass-associated SNPs were included in the SNP panel out of 145 identified at the 70 % cutoff but only 83 identified at the 90 % cutoff.We also utilised the combined SC and WGS pooled resequencing data to identify SNPs fixed for alternate alleles in all sequenced dogs compared to the wolf pool. These were identified as those with more than 3000x coverage in total in all dog pools and >3x coverage in the wolf pool and fixed for alternative alleles, with no more than 1 % of reads supporting a different allele. This produced a list of potential functional variants involved in selection related to dog domestication.We analysed the SC reads to identify potential structural variants that could explain the difference in phenotypes coverage by looking for variation in coverage across the 3 Mb region. This was done by scanning each pileup file in nonoverlapping sliding windows of 100 bp. We first calculated the depth of coverage in each window relative to the average coverage in the pool across all windows. These values were then normalised across pools to identify deviations in relative coverage in subset of pools in specific windows, which could result from the presence of copy number variation in a particular region.We scanned the critical region 11.0–11.5 Mb on CFA10 for windows with asymmetrical depth of coverage across pools, focussing on windows with coverage where one or more breeds had coverage more than two times the average or where one or more breeds completely lacked coverage. The distributions of reads in these windows were then manually inspected in Integrative Genome Viewer (IGV)  and crosCandidate SNPs for controlling variation in ear type and body mass were first selected using the strict criteria for fixation in a pool and supplemented with SNPs identified using loose criteria (see above). We selected a panel of 123 SNPs for genotyping in a larger sample of breeds from the candidate SNPs identified in the sequencing data. We genotyped these SNPs in 288 samples from 46 breeds selected for wide variation in both body mass and ear type (Table p < 10−45) or body mass phenotypes (p < 10−15) and inferred haplotype patterns across these SNPs using the EM algorithm implemented in plink, assigning the most probable pair of haplotypes to each sample. We then enumerated the set of haplotypes associated with each of the breed phenotypes.We selected 15 SNPs with the strongest associations to either ear  and conhttp://www.ensembl.org/info/docs/tools/vep/index.html).We also translated the dog coordinates onto the human genome build and analysed predicted effects using the Variant Effect Predictor (All sequence data has been submitted to NCBI Sequence Read Achive under BioProject ID PRJNA253907.


Brassica napus L.) plant, and the amount of acid detergent lignin (ADL) in the seeds and the hull content (HC) are often used as yellow-seeded rapeseed screening indices. In this study, a genome-wide association analysis of 520 accessions was performed using the Q + K model with a total of 31,839 single-nucleotide polymorphism (SNP) sites. As a result, three significant associations on the B. napus chromosomes A05, A09, and C05 were detected for seed ADL content. The peak SNPs were within 9.27, 14.22, and 20.86 kb of the key genes BnaA.PAL4, BnaA.CAD2/BnaA.CAD3, and BnaC.CCR1, respectively. Further analyses were performed on the major locus of A05, which was also detected in the seed HC examination. A comparison of our genome-wide association study (GWAS) results and previous linkage mappings revealed a common chromosomal region on A09, which indicates that GWAS can be used as a powerful complementary strategy for dissecting complex traits in B. napus. Genomic selection (GS) utilizing the significant SNP markers based on the GWAS results exhibited increased predictive ability, indicating that the predictive ability of a given model can be substantially improved by using GWAS and GS.A stable yellow-seeded variety is the breeding goal for obtaining the ideal rapeseed ( B. napus. Research has demonstrated that the yellow-seeded rapeseed is a desirable variety because of its quantities of oil and protein compared with the black-seeded rapeseed ×100% ×100% , where mge qqman  for Manhp-value cutoff was 0.001, the minimum genotype was 75%, the MAF was 0.05, and the maximum number of Mendel errors was 1.Haplotype blocks were constructed via the four-gamete rule with Haploview 4.2 . The paret al. [2) between adjacent SNPs was 0.23, and the median was 0.09. LD > 0.1 was used as the determination standard. Each QTL determination interval was recorded as an R-QTL. The LD value between the first left SNP and the significant SNP was computed, with the significant SNP utilized as a reference standard. If LD < 0.1, no further calculations were performed, and this SNP was accepted as the left border of the R-QTL. If LD > 0.1, then the LD value between the next SNP and the significant SNP was calculated. The analysis continued until the LD of a SNP and the significant SNP reached < 0.1, at which point the SNP was accepted as the left border of the R-QTL. The same operation was performed for the right borders of the R-QTLs. When two significant SNPs were adjacent, the SNP on the left side was associated with the left R-QTL, whereas the SNP on the right side was associated with the right R-QTL based on the approach applied to determine the R-QTL interval boundaries of the left and right sides, respectively.As described by Lund et al. , the anaWe utilized the R package rrBLUP  for genoAnalyses of the means and coefficients of variation and the correlation of the evaluated traits were performed with SPSS 17.0. The effects of genotype, environment, and genotype by environment interaction (G × E) on phenotypic variation were assessed using PROC GLM of SAS 9.2. Upon removal of the outliers, a previously described procedure  was implB. napus association panel comprising 520 accessions was measured in three replicates in two consecutive years. As presented in p < 0.01)  . The resUpon removal of the outliers, a four-trait normality test was conducted using the SAS 9.2 UNIVARIATE procedure, and the normal distribution was measured across skewness and kurtosis. The analysis results indicate that 13ADL  and 14ADL  conformed to the normal distribution, but 13HC and 14HC did not. Thus, we first performed a Box-Cox data transformation of the two phenotypes (2013HC and 14HC) using the Minitab 16.0 software for GWAS to adhere to or approximate a normal distribution in the next step. The software applied the data transformation depending on the lambda value, and the obtained best Box-Cox transformation lambda values of 13HC and 14HC were 0.16 and 0.77, respectively. Through the Box-Cox transformation, 13HC and 14HC reached normal or obviously superior levels than those before the raw data transformation .B. napus  was 9.27 kb from the key lignin biosynthesis gene BraA.PAL4   . The fir2 = 0.2) .2 = 0.1). This average determination interval was much smaller than that delineated by traditional linkage mapping. The major locus on A05 was divided into two R-QTLs. The 74.08-kb QTL determination interval contained two peak SNPs (rs11682 and rs11683), whereas the other R-QTL encompassed two additional peak SNPs (rs11690 and rs11689) spanning a 22.22-kb determination interval, which was identical to the result for the LD blocks. Three additional R-QTLs were noted on A05. Within these R-QTL regions bracketed by significant SNPs, a total of 32 potential candidate genes related to the trait were noted  via local BLAST searching. The results are presented in BnaA.CAD2/BnaA.CAD3 in the marker intervals of ssr1-144 and KBrH108D07, where they overlapped with the QTL that was reported by Snowdon et al. [et al. [BnaA.CCR1 and was communicated by Liu et al. [et al. [To compare the positions determined by GWA mapping and QTL mapping, we used common markers from previous studies to anchor the latest n et al.  and Stei [et al.  based onu et al. ,29 and S [et al.  was anchBnaA.CAD2/BnaA.CAD3 located in the determination interval of the peak SNP rs21157 overlapped with previous QTLs reported by Snowdon et al. [et al. [et al. [In addition, our R-QTLs overlapped with previous QTLs on A09. The R-QTL that contained n et al.  and Stei [et al.  and was  [et al. ,29. Thesh2 = 75.23%). To evaluate whether GWA mapping performed high-efficiency detection, we filtered candidate genes for each identified locus. All potential candidate genes 250 kb upstream or downstream of the lead SNPs (r2 = 0.1) of the loci are listed in Although 167 loci were highly significantly associated with seed HC in 2013 and 2014, only 4 loci were detected in both years. Given that the genotype and the environment together affect seed HC, this result makes biological sense because the latter had a relatively small value of broad-sense heritability  is an important aspect of plant breeding. In this study, we used the R package rrBLUP to conduct a genome-wide prediction for seed ADL content and HC. The results indicate that genomic selection using the significant SNP markers identified via GWAS had a higher predictive ability, indicating that the predictive ability of a given model can be improved by combining GWAS and GS. Molecular marker-assisted selection is the main aim of molecular breeding, using close linked markers for molecular marker-assisted selection can ensure the accuracy of the selection and improve the efficiency of the selection . SSR marB. napus and other crops.At present, GWAS investigation have entered a post-GWAS world ; the mosWith the GWAS Q + K model, significant association signals for seed ADL content were found not only on the recognized chromosome, A09, but also on chromosomes A05 and C05 in two experimental years. A comparison of our GWAS results and those of previous linkage mappings revealed a common chromosomal region on A09 and a major locus on A05 associated with seed ADL content, as detected by GWA mapping. Various key lignin biosynthesis genes were identified in the R-QTLs of seed ADL content based on the LD analysis. In total, 123 candidate genes associated with seed HC were identified by GWA mapping. Greater than 65% of these genes are involved in the phenylpropanoid pathway and are transparent testa genes. The beneficial allele and candidate genes will be useful in rapeseed breeding for achieving the ideal yellow-seeded rapeseed variety through a molecular design approach.S1 Fig(TIF)Click here for additional data file.S2 Figp-values from the compressed MLM are plotted on the vertical axis. The blue horizontal lines indicate the 1% FRD-adjusted significance threshold (6.27 × 10−6), and the bottom of the panel shows the related candidate genes with green boxes in the R-QTL region. Two previously identified genes, (a) CCR1 and (b) CAD2/CAD3, were significantly associated with seed ADL content.The top of the panel shows an R-QTL region based on a significant SNP, whose position is indicated by a vertical green line. The negative log10-transformed (TIF)Click here for additional data file.S1 Table(XLSX)Click here for additional data file.S2 Table(DOCX)Click here for additional data file.S3 Table(DOCX)Click here for additional data file.S4 Table(XLSX)Click here for additional data file.S5 Table(DOCX)Click here for additional data file.S6 Table(DOCX)Click here for additional data file.


Spartina pectinata Link), were analyzed using KASP technology in order to validate the SNPs. Fifty-nine SNPs were validated using a core collection of 38 natural populations and a phylogenetic tree was created with one main clade. Samples from the same population tended to cluster in the same location on the tree. Polymorphisms were identified within 52.6% of the populations, split evenly between the tetraploid and octoploid cytotypes. Twelve selected SNP markers were used to assess the fidelity of tetraploid crosses of prairie cordgrass and their resulting F2population. These markers were able to distinguish true crosses and selfs. This study provides insight into the genomic structure of prairie cordgrass, but further analysis must be done on other cytotypes to fully understand the structure of this species. This study validates putative SNPs and confirms the potential usefulness of SNP marker technology in future breeding programs of this species.Single nucleotide polymorphisms (SNPs) are one of the most abundant DNA variants found in plant genomes and are highly efficient when comparing genome and transcriptome sequences. SNP marker analysis can be used to analyze genetic diversity, create genetic maps, and utilize marker-assisted selection breeding in many crop species. In order to utilize these technologies, one must first identify and validate putative SNPs. In this study, 121 putative SNPs, developed from a nuclear transcriptome of prairie cordgrass ( Spartina pectinata Link) is a native grass species of the North American Prairie that has a geographic distribution, ranging from the southern U.S.  to northern Canada, and from the east coast through the Midwest to the western coast of the U.S. , hexaploid (2n = 6x = 60), and octoploid (2n = 8x = 80) , is a PCR-based homogeneous fluorescent SNP genotyping system, which determines the alleles at a specific locus within genomic DNA . The second and third steps are yet to be completed for polyploid prairie cordgrass. Several parameters, such as sample size, number of SNPs to be used for analysis, cost effectiveness, and the SNP genotyping platform, must be considered in these analyses  to validate SNP polymorphisms identified in the nuclear transcriptome of natural populations of prairie cordgrass in the U.S. and (2) to assess the fidelity of specific tetraploid crosses and selfs, and to elucidate inheritance patterns of SNP markers.In a separate study by Gonzalez et al.  at South Dakota State University, a transcriptome of prairie cordgrass was assembled using ~1.2 billion Illumina paired-end reads from various vegetative tissues  under various conditions  in order to obtain an abundance in diversity, with regards to the number and type of transcripts. The assembly was developed using CLC Genomics Workbench 7.0  and annotated against the sorghum genes models. About 146,549 contigs, or transcript assemblies, of 230 bp or more with an N50 of 973 bp were used to mine over 1 million SNPs, insertions, and deletions using the variant detection function in CLC Genomics Workbench. Putative SNPs were filtered based on coverage (minimum of 100 X), a window of 80–100 bp free from additional SNPs and an allele frequency of 20–80%. Initially, nine bi-allelic SNPs were selected for analysis, associated with enzymes within the lignin biosynthesis pathway. Additional SNPs were selected without regard to putative function of the transcript assembly. A total of 121 bi-allelic SNPs were identified for use in this study  and KASP-by-Design assay mix . The PCR thermocycling conditions for all primers, except pcg_1186, was 15 min at 94°C followed by 10 cycles of 94°C for 20 s and 61°C for 1 min  followed by 26 cycles of 94°C for 20 s and 55°C for 1 min. The PCR thermocycling conditions for primer pcg_1186 was 15 min at 94°C followed by 10 cycles of 94°C for 20 s and 65°C for 1 min  followed by 26 cycles of 94°C for 20 s and 57°C for 1 min. After amplification, PCR plates were read with a Spectramax M5 FRET capable plate reader  using the recommended excitation and emission values. Data was then analyzed using Klustercaller software  to identify SNP genotypes.Spartina species samples, namely; S. alterniflora, S. patens (Flageo vt.), S. patens (Sharp vt.), S. patens, and S. bakeri. All of the KASP genotyping assay results were recorded as a two-letter code, or SNP code, i.e., AA, AG, GG. A DNA fingerprint was made using all the SNP genotypes creating a concatenated DNA-like sequence, which was then imported into MEGA 6  × PC20-102  and 13 F1 individuals from PC20-102  × PC17-109  were sampled. F1 seeds were planted in greenhouse setting. Leaf tissue samples of each seedling were collected and stored at −80°C until DNA extraction was performed. Total genomic DNA was extracted from frozen leaf tissue as described previously. For the F1 individuals, 12 KASP genotyping assays were selected based on the parental SNP genotypes  of two populations differing in morphological characteristics of potential agronomic importance was developed. PC17-109 is a tetraploid population from Illinois with a phalanx rhizome type and low seed mass, whereas PC20-102 is a tetraploid population from Kansas with a guerilla rhizome type and high seed mass. In a greenhouse, the female inflorescence was covered ~1 day prior to stigma emergence, while pollen was collected from the male parent. Pollen was directly applied to the stigmas with a brush, and rebagged until anthesis was completed. A total of 83 individuals, 70 F2 individuals were generated and genotyped. In a greenhouse, the prairie cordgrass inflorescence was covered ~1 day prior to stigma emergence with bags constructed to view progression of inflorescence development of F1 plants. When anthesis was reached, the bags were shaken to promote self-pollination. Bags remained until anthesis was complete. F2 seeds were collected and planted in a greenhouse setting. A total of eight F1 individuals were selfed (6 F1 of PC17-109 × PC20-102 and 2 F1 of PC20-102 × PC17-109) and 8–11 individuals were sampled from the planted seeds of each of the selfed plants . Leaf tissue samples were stored at −80°C until DNA extraction was performed. All 12 of the KASP genotyping assays selected to score the F1 individuals were also tested on the F2 individuals. All of the assay results were recorded as a SNP code as done in the F1 analysis. All SNP codes that were not accurately identified were removed from analysis.To assess the utility of the KASP marker system in identifying selfed individuals in the tetraploid background and gauge the segregation pattern, FTwenty-six (21.5%) SNPs failed KASP marker development. From the remaining 95 (78.5%), 11 SNPs were found to be monomorphic when tested on the core collection DNA, resulting in 84 SNPs that were true allelic variants. Three of the eleven monomorphic markers were selected to discover if future plant samples would reveal the SNP polymorphisms previously identified in the transcriptome. From the 84 allelic variants, 56 of the most highly polymorphic SNPs were selected for further use in this study, resulting in 59 total KASP genotyping assays (Table The resulting data set from the DNA fingerprint contained 118 characters. There was an average of 3.8 missing character data points (SNP codes) per population. The maximum parsimony tree identified one clade after correcting for the missing data . These four individuals, after being analyzed across all 12 assays, were identified as being selfs, and were removed from the χ2 analysis  genotype category across multiple homozygous primers were considered outcrosses and not true selfs of the F1 rather than being homozygous (selfed) as expected. Ninety-one percent of the F2 progeny were identified as successful selfs. Because of the protogynous nature of this species, there is already a natural element working against selfing. This could explain why outcrossed individuals were identified. There is also a possibility that some of the early-maturing stigmas were exposed to pollen in the greenhouse before bagging. This could explain why more F2 progeny were identified as unexpected genotypes (outcrosses) than the expected genotype (selfs) of the F1 progeny.The FWaxy lineages of Spartina genus support the allotetraploid origin of S. pectinata  progeny. Utilizing the biallelic nature of the KASP system, χThe 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.


High-throughput next-generation sequence-based genotyping and single nucleotide polymorphism (SNP) detection opens the door for emerging genomics-based breeding strategies such as genome-wide association analysis and genomic selection. In polyploids, SNP detection is confounded by a highly similar homeologous sequence where a polymorphism between subgenomes must be differentiated from a SNP. We have developed and implemented a novel tool called SWEEP: Sliding Window Extraction of Explicit Polymorphisms. SWEEP uses subgenome polymorphism haplotypes as contrast to identify true SNPs between genotypes. The tool is a single command script that calls a series of modules based on user-defined options and takes sorted/indexed bam files or vcf files as input. Filtering options are highly flexible and include filtering based on sequence depth, alternate allele ratio, and SNP quality on top of the SWEEP filtering procedure. Using real and simulated data we show that SWEEP outperforms current SNP filtering methods for polyploids. SWEEP can be used for high-quality SNP discovery in polyploid crops. A. hypogaea genetic map (Single nucleotide polymorphism (SNP) markers are powerful genomic tools because they are ubiquitous in the genome, codominant, and can be in coding regions and act as functional markers. In diploid organisms, including plants, SNPs have become the preferred marker type in genome-wide studies (Arachis duranensis (A genome) and Arachis ipaensis (B genome) that led to the origin of cultivated peanut is not known, these two diploids diverged from one another only 3–3.5 million years ago  that can be used as a tool to filter out false positives from a set of SNP calls. Our method uses the ubiquitous false-positive SNP calls and transforms them from a weakness to a strength by using their information to pull out the true SNPs that are polymorphic between genotypes of interest. SWEEP is implemented in a Perl script that is easy to use; the user only needs to supply sorted and indexed bam files and the reference genome used to map sequence reads. In addition, previously generated vcf files can be used. SWEEP will call SNPs using Samtools ; Florida-07 (F07); SPT06-06 (SPT); NC 3033 (NC); New Mexico Valencia A (NM); and C76-16 (C76). SPT06-06 has been released as GP-NC WS 16 (http://www.bioinformatics.babraham.ac.uk/projects/fastqc/) and reads were further trimmed for base quality (>30) and base usage bias. Due to nonrandom base usage in the first 10 bases of these libraries, 10 bases were trimmed from the 5′ end. Trimmed libraries were then filtered for rRNA contamination by mapping to a set of known rRNA sequences using Bowtie  using a custom python script. Then vcftools  (r-project.org) and the package ggplot2.All graphs were made using R statistical software . The default SWEEP program relies heavily on Samtools-calculated genotype probabilities. Manual inspection of alignments revealed that in some cases, reads map to the locus with the alternate base even when the genotype probability score of 0 for homozygous reference is calculated. These reads may have lower mapping quality or have been clipped. In the case of a SNP where only one genotype has been called homozygous reference, it is important to make sure no single read in the experiment from that genotype contains the alternate base, no matter the mapping quality of the read. These cases can cause false-positive SNP calls and lead to spurious downstream analysis. As an alternative to manually checking every individual SNP, the user can implement the –ultimate function, which, after SWEEP filtering, checks every base from a genotype covering the SNP of interest that was called homozygous reference. If one read covering the SNP contains the alternate allele, then the SNP is filtered out. Running –ultimate is recommended in all cases for optimal quality.SWEEP gives the user the option to include the ultimate filter. This option is more computationally intensive and will require longer run times . It is important to test for the efficacy of the filtering method. Without a known set of validated SNPs, we cannot fully verify the SNPs identified in silico, but we can evaluate them based on a set of assumptions. The assumption we make is that given a locus with a minimum number of reads mapping to it, if all the reads mapping to that locus have the reference base for a particular genotype, then that genotype does not have a SNP at that position. If, however, every genotype assayed contains at least one read that exhibits the alternate base at the position of interest, then we can reliably say that the base of interest is showing either a polymorphism in all genotypes relative to the reference or a polymorphism between subgenomes. In the latter case the SNP is not a true allelic SNP and can be determined to be a false positive. We compared the SWEEP filtering method with the traditional filtering method based on these assumptions and using different numbers of genotypes   . SWEEP fpe.html) .A. hypogaea and is a major improvement of current methods. We have validated SWEEP-filtered SNPs at an accuracy rate of 85%. SWEEP outperforms traditional filtering methods relative to the false-positive rate. The false-positive rate of traditionally filtered SNPs ranges from 80% to 96%, whereas the false-positive rate of SWEEP-filtered SNPs ranges from 0.01% to 0.02%. Using a simulation, we further validated that SWEEP greatly outperforms current filtering methods, retaining a high percentage of true SNPs relative to homeologous polymorphisms (65%–99% depending on coverage) compared to other methods (2%–8%). A similar method was developed and implemented in parallel to design high-quality probes for a 90k SNP array for allo-octoploid cultivated strawberry Fragaria × ananassa (We have shown that SWEEP is a reliable method for identifying true allelic SNPs among genotypes of


Homo sapiens single variation (SNPs/Indels) in BRAF gene through coding/non-coding regions. Variants data was obtained from database of SNP even last update of November, 2015. Many bioinformatics tools were used to identify functional SNPs and indels in proteins functions, structures and expressions. Results shown, for coding polymorphisms, 111 SNPs predicted as highly damaging and six other were less. For UTRs, showed five SNPs and one indel were altered in micro RNAs binding sites (3′ UTR), furthermore nil SNP or indel have functional altered in transcription factor binding sites (5′ UTR). In addition for 5′/3′ splice sites, analysis showed that one SNP within 5′ splice site and one Indel in 3′ splice site showed potential alteration of splicing. In conclude these previous functional identified SNPs and indels could lead to gene alteration, which may be directly or indirectly contribute to the occurrence of many diseases.This study was carried out for BRAF coding gene, member of RAF family, located on chromosome seven (7q34), region from 140,715,951 to 140,924,764 base pairs which cover approximately 190 kb, is composed of 18 exons, and its translated protein name is “B-Raf proto-oncogene serine/threonine protein kinase.” This protein belongs to raf/mil family, which plays a role in regulating the MAP kinase/ERKs signaling pathway, which affects cell division, differentiation, and secretion [ BRAF gene through various cancers, including non-Hodgkin lymphoma, colorectal cancer, malignant melanoma, thyroid carcinoma, non-small-cell lung carcinoma, and adenocarcinoma of lung [Genetic alterations (mutations) in general can be divided into two categories, inheritable (germline mutations) with 2% to 4% occurrence and sporadic (somatic mutations) , 2. BRAF seven 7q4, region seven 7q4, regionSingle nucleotide polymorphisms (SNPs) markers are single-base changes in DNA sequence, with allele frequency of 1% or greater among population; it normally occurs throughout the genome with frequency of about one in every 1000 nucleotides, which is considered the simplest and common type of the genetic markers leading to DNA variation among individuals . Nonsyno Homo sapiens variants. From total number of variants, of which 144205811 were SNPs, 16064552 were Indels (single or multi-insertion/deletion). Database of SNP contains the results of HapMap and 1000 Genomes Projects (http://www.ncbi.nlm.nih.gov/snp/).1000 Genomes Project showed that most human genetic variation is represented by SNPs. Database of SNP (dbSNP) is one of the most databases serving as a central and public store for genetic variation since its initiation in September 1998 . Any lab BRAF gene.Through noncoding regions , polymorphisms such as SNPs in microRNAs (miRNAs/mRNA) binding sites which are called mirSNPs can affect miRNAs function and then gene expression, resulting in many human diseases such as cancers . Identifhttp://www.ncbi.nlm.nih.gov/snp/). The related protein sequences are obtained from UniProt database (http://www.uniprot.org/).SNPs located in target gene were obtained from the database of SNPs (dbSNP); it is a public-domain archive for a broad collection of simple genetic polymorphisms. This collection of polymorphisms includes single-base nucleotide substitutions (SNPs), small-scale multibase deletions or insertions , and retroposable element insertions and microsatellite repeat variations (short tandem repeats or STRs) ( Homo sapiens SNPs and Indels (single insertion or deletion) within coding (nonsynonymous), 3′/5′ UTR, and 3′/5′ splice sites had been selected and submitted to bioinformatics tools for further investigation. Distributions of single variants are shown in SNP database contains SNPs or Indels within 3′/5′ UTR, 3′/5′ splice sites, coding synonymous, intron, and nonsynonymous which represent missense, nonsense, stop gain, and frameshift. In this studyAbout the main diagram of SNPs analysis, for missense SNPs, analysis was done by using three tools  and SNPs predicted as functional or damaging by previous triple servers were arranged in http://sift.bii.a-star.edu.sg/index.html.SIFT server is an online bioinformatics server that is used to predict the damaging effect of nucleotide substitution and frame shift (insertion/deletion) on protein function based on the maintenance degree of the amino acid residues in sequence alignments derived from closely related sequences with the main assumption; that is, evolutionarily conserved regions tend to be less tolerant to mutations, and so mutations in these regions mainly affect its function . SIFT sehttp://genetics.bwh.harvard.edu/pph2/index.shtml.An online bioinformatics server automatically predicts the nsSNPs that affect with amino acid substitution structure and function of protein, using a comparative method. PolyPhen searches for protein 3D structures and make multiple alignments of homologous sequences and amino acid contact in several protein databases and calculate position-specific independent count scores (PSIC) for each of two variants and then computes the PSIC scores difference between two variants, where the higher PSIC score difference indicates that the functional impact of amino acid substitution is likely to occur . PolyPhehttps://rostlab.org/services/snap/).SNAP2 is a trained classifier that is based on a machine learning device called “neural network.” It distinguishes between effect and neutral variants/nonsynonymous SNPs by taking a variety of sequence and variant features into account. The most important input signal for the prediction is the evolutionary information taken from an automatically generated multiple sequence alignment. Also structural features such as predicted secondary structure and solvent accessibility are considered. If available, also annotation  of the sequence or close homologs are pulled in. Predicting a score , analysis suggests that the prediction score is to some extent correlated to the severity of effect  , the higher 15 functional nsSNPs (got higher predicted score) were selected for next analysis.http://gpcr2.biocomp.unibo.it/cgi/predictors/I-Mutant3.0/I-Mutant3.0.cgi.I-Mutant version 3.0 is a suite of support vector machine, based predictors integrated in a unique web server. It offers the opportunity to predict the protein stability changes upon single-site variations from the protein structure or sequence. I-Mutant result is designed as follows: DDG < 0: decrease stability, DDG > 0: increase stability, or DDG = 0: neutral . I-Mutanhttp://www.cbs.dtu.dk/services/CPHmodels/).A protein homology modeling prediction server, used to predict the 3D structure of proteins with an unknown 3D structure model, in CPH models the template recognition based on profile-profile alignment guided by secondary structure and exposure predictions . Proteinhttp://www.cgl.ucsf.edu/chimera/).Chimera is a high-quality extensible molecular graphics program designed to maximize interactive visualization, analysis system, and related data . This sohttp://www.cmbi.ru.nl/hope/method) medical researchers  (http:///method) .http://compbio.uthsc.edu/miRSNP/  within microRNAs binding sites . Single /miRSNP/ .http://alggen.lsi.upc.es/cgi-bin/promo_v3/promo/promoinit.cgi?dirDB=TF_8.3).PROMO is a virtual laboratory for the identification of putative transcription factor binding sites (TFBS) in DNA sequences from a species or groups of species of interest. TFBS defined in the TRANSFAC database are used to construct specific binding site weight matrices for TFBS prediction. The user can inspect the result of the search through a graphical interface and downloadable text files . Input dβ serine-arginine proteins and the hnRNP A1 ribonucleoprotein. It also developed new position weight matrices to assess the strength of 5′ and 3′ splice sites and branch points [http://www.umd.be/HSF3/index.html).Human Splicing Finder (HSF) is a tool to predict the effects of mutations on splicing signals or to identify splicing motifs in any human sequence. It contains all available matrices for auxiliary sequence prediction as well as new ones for binding sites of the 9G8 and Tra2-h points . In thisSome information about total single variants and functional nsSNPs predicted with triple or double tools is obtained from many databases  Tables . In addi BRAF gene, 111 variants were predicted to be damaging or effect by triple  servers , results showed that two SNPs were found in transcription factor binding sites with none being altered, and the remaining three were not located within any TF binding sites, meaning that none of five SNPs showed an effect on TF binding sites . In addi BRAF modify the normal programs of cell proliferation, differentiation, and death, they are believed to play an important role in disease predisposition. Therefore, efforts were made to identify SNPs that can modify the structure, function, and expression of the BRAF gene.To date the complete mechanisms by which a nucleotide variant may result in a phenotypic change are for the most part unknown. In silico analysis using powerful software tools can facilitate predicting the phenotypic effect of nonsynonymous coding SNPs on the physicochemical properties of the concerned proteins. Such information is critical for genotype-phenotype correlations and also to understand disease biology. Given the fact that nsSNPs in critical cellular genes such as BRAF mutations, when thymine is substituted with adenine at nucleotide 1799, it results in an amino acid substitution at position 600 from valine (V) to a glutamic acid (E), which is called V600E, located in the activation segment that has been found in many human cancers. For example, it was reported as the most common genetic mutation related to papillary thyroid cancer and occurs in approximately 45% of patients [Through one of the most significantpatients , 25. In  BRAF gene. These polymorphisms could directly or indirectly influence the intermolecular and intramolecular interactions of amino acid residues and protein expression and can culminate into disease risks. By analyzing the conformational changes and interactions of amino acid residues within BRAF proteins, we have identified significant structural and functional changes that can explain the activity deviations, caused by several mutations. Furthermore significant pathology or likely pathology showed association of many detected SNPs with many diseases through clinical variation database (http://www.ncbi.nlm.nih.gov/clinvar/). They include the following diseases: cardiofaciocutaneous syndrome, Noonan syndrome, LEOPARD syndrome, RASopathy, non-small-cell lung cancer, carcinoma of colon, adenocarcinoma of lung, thyroid cancer, malignant lymphoma, non-Hodgkin lymphoma. Screening for BRAF variants may be useful for molecular diagnosis and development of vital molecular inhibitors of genes pathways. This study demonstrates the significance of different bioinformatics tools to figure out the phenotypic changes and protein function, associated with the structure-function relationship of BRAF gene. More evidence is required for the involvement of deregulated miRNA networks in cancer development. Resultant SNPs can be applied for further investigation and diagnosis of many associated diseases.The current study shows the in silico analysis of genetic single variants within the coding region, 3′/5′ UTR and 3′/5′ splice sites of