Mycobacterium tuberculosis complex (MTBC), the causative agent of tuberculosis (TB), is characterized by low sequence diversity making this bacterium one of the classical examples of a genetically monomorphic pathogen. Because of this limited DNA sequence variation, routine genotyping of clinical MTBC isolates for epidemiological purposes relies on highly discriminatory DNA fingerprinting methods based on mobile and repetitive genetic elements. According to the standard view, isolates exhibiting the same fingerprinting pattern are considered direct progeny of the same bacterial clone, and most likely reflect ongoing transmission or disease relapse within individual patients.M. tuberculosis Beijing genotype clone from a high incidence region . Both isolates shared the same IS6110 RFLP pattern and the same allele at 23 out of 24 MIRU-VNTR loci.Here we further investigated this assumption and used massively parallel whole-genome sequencing to compare one drug-susceptible (K-1) and one multidrug resistant (MDR) isolate (K-2) of a rapidly spreading We generated 23.9 million (K-1) and 33.0 million (K-2) paired 50 bp purity filtered reads corresponding to a mean coverage of 483.5 fold and 656.1 fold respectively. Compared with the laboratory strain H37Rv both Beijing isolates shared 1,209 SNPs. The two Beijing isolates differed by 130 SNPs and one large deletion. The susceptible isolate had 55 specific SNPs, while the MDR variant had 75 specific SNPs, including the five known resistance-conferring mutations.M. tuberculosis isolates exhibiting identical DNA fingerprinting patterns can harbour substantial genomic diversity. Because this heterogeneity is not captured by traditional genotyping of MTBC, some aspects of the transmission dynamics of tuberculosis could be missed or misinterpreted. Furthermore, a valid differentiation between disease relapse and exogenous reinfection might be impossible using standard genotyping tools if the overall diversity of circulating clones is limited. These findings have important implications for clinical trials of new anti-tuberculosis drugs.Our results suggest that  Mycobacterium tuberculosis complex (Mtb) strains infect up to 22% of patients never treated and up to 60% of those previously treated Tuberculosis (TB) is primarily a symptom of poverty and inequality In addition to the socio-economic, environmental, and host genetic determinants M. tuberculosis and M. bovis, another species of the M. tuberculosis complex, revealed a maximum diversity of just 2,500 SNPs M. tuberculosis isolates of the same clone or strain (e.g. characterised by IS6110 genotyping) are generally considered to be identical and belong to the same outbreak or recent chain of transmission Even though genetic diversity appears to influence virulence and immunogenicity of strain of different phylogenetic lineages 6110 RFLP, which is the most discriminating test of all standard molecular TB typing tools in use today To address this question, we have applied massively paralleled whole-genome sequencing M. tuberculosis reference strain H37Rv (ATCC 27294) 6110 elements) whose sequence could not be uniquely aligned to the reference. This fraction increased to 4.8% for K-1 and 3.4% for K-2 to exclude any areas for which no valid base was called due to insertion or deletions. All the genetic changes we identified are correlated with the existing scientific literature and listed in detail in the Supplementary Information (We generated 23.9 million (K-1) and 33.0 million (K-2) paired 50 bp purity filtered reads corresponding to a mean coverage of 483.5 fold and 656.1 fold respectively which we compared to the genome of the ormation . In addiormation  a flexibormation -S6 to alRe-sequencing of H37Rv uncovered 80 SNPs with respect to the published reference genome. 74 SNPs were shared with the Beijing isolates sequenced in this study , all of Next, we compared K-1 and K-2 to H37Rv. Both Beijing isolates shared a common pool of 1,209 single-nucleotide-polymorphisms (SNPs)  and five6110 typing  revealed that more than half of the SNPs in K-1 were in fact unique to the Beijing genotype  are presumed to provide a high enough discriminatory power to accurately reconstruct transmission chains as part of epidemiological studies. Based on these standard criteria, our isolates K-1 and K-2 that exhibit the same molecular fingerprint can be considered as belonging to the same “outbreak clone” that is rapidly expanding in the study area by extensive transmission Early sequencing studies of By contrast, we observed a strikingly high genomic diversity among two isolates sharing the same DNA fingerprint. These findings challenge the “identical fingerprints – same strain” dogma and have important implications for the investigation of disease dynamics, especially in high incidence regions such as Eastern Europe where the Beijing genotype is dominant and the population diversity (e.g. among MDR strains) is significantly reduced . The fact that strains with identical genotyping patterns can accumulate significant amounts of genetic diversity indicates that epidemiological links between isolates with identical genotyping data can be more remote and are likely to represent older transmission events rather than cases of recent transmission among patients in one RFLP cluster. Under these circumstances, a valid differentiation of relapse and re-infection, that can occur in significant numbers in TB patients undergoing standard treatment in areas with high rates of MDR One of the key objectives of molecular epidemiology in TB is to distinguish treatment failure/relapse with the same strain from exogenous reinfection with a second strain. This is particularly important given the emergence of MDR and extensively-drug resistant (XDR) TB in many parts of the world A large genetic diversity among closely related isolates of Mtb has also implications for our understanding of the evolution of drug resistance. A recent report detected only few differences when comparing the genomes of MDR and XDR Mtb strains from KwaZulu-Natal, South Africa to an isogenic drug-susceptible isolate  Finally, the high number of novel SNPs that are unique to the Beijing lineage confirmed that previously sequenced genomes only sampled a small part of the global diversity of Mtb M. tuberculosis during spread. In clinical settings, such an approach could allow for rapid detection of drug-resistant TB and enhanced disease surveillance.In conclusion, our results showed that genomic diversity in closely related strains of Mtb may be greater than anticipated. Clonal variants can accumulate high levels of genomic diversity, which can lead to different pathogenic properties undetectable by standard genotyping 6110 RFLP fingerprints, spoligotype patterns and MIRU-VNTR profiles were determined as previously described K-1 was isolated from sputum in 11/2001 and K-2 in 09/2004. ISM. tuberculosis H37Rv genome (AL123456.2) and called at threshold ≥100 with the Illumina Genome Analyzer Pipeline. Where more than one base was called, only the majority call was accepted when the ratio between the two calls was ≥3. Only positions for which valid base calls were available in all genomes were considered for our assessment of the diversity. The validity of the SNP calls was confirmed by 40 kb of dideoxy chain terminator sequencing (ABI 3130xl) of each isolate. Deletions ≥250 bp were detected using combined paired read data and localised homology mapping across the deletion boundary.∼200 bp paired read fragment libraries were prepared from 1 µg of DNA, and sequenced using the Illumina Genome Analyzer as described previously Details of the annotation used for the SNP analysis and the graphical representation of the tabular results  with ArtFile S1Description of supplementary information Supplementary Tables and Figures as well as Supplementary Methods and Results(1.07 MB PDF)Click here for additional data file.File S2Supplementary information - Excel file with all SNPs detected(0.30 MB ZIP)Click here for additional data file.File S3Contains the H37Rv genome (AL123456.2) and TubercuList R11 annotation, necessary for the graphical representation of all SNPs.(1.96 MB ZIP)Click here for additional data file.File S4contains the graphical representation of SNPs in H37Rv(0.05 MB ZIP)Click here for additional data file.File S5Contains the graphical representation of SNPs in K-1 (Section 2.2).(0.05 MB ZIP)Click here for additional data file.File S6Contains the graphical representation of SNPs in K-2 (Section 2.2).(0.05 MB ZIP)Click here for additional data file.


Genetic linkage maps are necessary for mapping of mendelian traits and quantitative trait loci (QTLs). To identify the actual genes, which control these traits, a map based on gene-associated single nucleotide polymorphism (SNP) markers is highly valuable. In this study, the SNPs were genotyped in a large family material comprising more than 5,000 piglets derived from 12 Duroc boars crossed with 236 Danish Landrace/Danish Large White sows. The SNPs were identified in sequence alignments of 4,600 different amplicons obtained from the 12 boars and containing coding regions of genes derived from expressed sequence tags (ESTs) and genomic shotgun sequences.Linkage maps of all 18 porcine autosomes were constructed based on 456 gene-associated and six porcine EST-based SNPs. The total length of the averaged-sex whole porcine autosome was estimated to 1,711.8 cM resulting in an average SNP spacing of 3.94 cM. The female and male maps were estimated to 2,336.1 and 1,441.5 cM, respectively. The gene order was validated through comparisons to the cytogenetic and/or physical location of 203 genes, linkage to evenly spaced microsatellite markers as well as previously reported conserved synteny. A total of 330 previously unmapped genes and ESTs were mapped to the porcine autosome while ten genes were mapped to unexpected locations.The linkage map presented here shows high accuracy in gene order. The pedigree family network as well as the large amount of meiotic events provide good reliability and make this map suitable for QTL and association studies. In addition, the linkage to the RH-map of microsatellites makes it suitable for comparison to other QTL studies. Genetic linkage maps are essential tools for locating genes and quantitative trait loci (QTLs) that control important traits. The first linkage map covering all 18 autosomes of the pig was published in 1995 . These mSNP-based genetic variation is found with high density throughout the genome. Efficient technologies have been developed, which allow for highly parallel and cost efficient genotyping, SNPs have therefore become the markers of choice for genetic mapping. This makes SNP maps highly suitable for association studies, fine mapping of QTLs as well as haplotype determination. Moreover, to identify the genes underlying monogenic and quantitative traits, it is an advantage if the maps are based on gene-associated markers, such as genic SNPs. Genic SNPs, whether they are located in coding or in 5' and 3' untranslated regions are more likely to cause a functional change than those that occur outside genes . The use7000 PARK2 [ENI [MUC13 , HCLS1 [3 [HCLS1 , BTG3 [9S1 [BTG3 , LPL [94L [HNRPF , MYPN [9PF [MYPN , MBL2 [9PN [MBL2 , MYOM2 [2 [MYOM2 , SLC25A4[SLC25A4 , SNX25 [25 [STAR , TTN [47TTN [FN1 , DES [99NT [RALY  and CHRMY [CHRM2 . The act7000 panel [After ordering the annotated SNP-containing genes, the homologous genes were used to analyse the known conserved synteny between human and pig of each porcine chromosome . Rearran00 panel ,103 Loca00 panel  against 00 panel  to ident7000 panel. Markers were mapped to the panel comprising 118 hybrid clones (90 clones plus 28 complementary clones) using the IMpRH database [Since this is the first porcine SNP-map, a total of 104 evenly distributed markers from the 18 porcine linkage groups were linked to microsatellites or genes using the whole-genome radiation hybrid IMpRHdatabase . In the database . This madatabase .CB conceived the research and coordinated the project. FP, HH and BZ contributed to annotation of exons, KL to primer design and XW, RKKV, AH and LBM to SNP selection. FP, HH and VRG developed the data pipeline. AH, RKKV and VRG performed the genotyping and VRG validated the data. RKKV and VRG made the linkage groups and RKKV constructed and outlined the linkage maps. BZ, VRG and RKKV annotated the sequences. VRG retrieved reference information for the gene position study, and VRG, RKKV and KKS drafted the manuscript. All authors have read and approved the final manuscript.Gene location. Genes linked to the microsatellites on the IMpRH7000, mapped previously by linkage and physically either by RH panels, somatic-cell hybrids or by Blast analysis of the reference sequence against the pig BAC assembly. The genes are listed in chromosomal (SSC) order. Rearrangements according to the microsatellite map or BAC assembly positions are indicated in bold.Click here for fileData about the SNPs. The data file contains the following information on each SNP used in the study: SSC number, SNP ID, SNP assays, gene name, sex-averaged distance, female distance, male distance, number of meioses, sow minor allele frequency, number of heterozygous sires, forward and reverse primer sequences, human cytogenetic position, human accession number, physical human location, SNP-containing sequences, SNP status, accession number and name on the PorcineSNP60.Click here for file


Left ventricular mass (LVM) is a strong, independent predictor of heart disease incidence and mortality. LVM is a complex, quantitative trait with genetic and environmental risk factors. This research characterizes the genetic architecture of LVM in an African-American population by examining the main and interactive effects of individual candidate gene single nucleotide polymorphisms (SNPs) and conventional risk factors for increased LVM.We used least-squares linear regression to investigate 1,878 SNPs from 234 candidate genes for SNP main effects, SNP-risk factor interactions, or SNP-SNP interactions associated with LVM in 1,328 African-Americans from the Genetic Epidemiology Network of Arteriopathy (GENOA) study. We reduced the probability of false positive results by implementing three analytic criteria: 1) the false discovery rate, 2) cross-validation, and 3) testing for internal replication of results.We identified 409 SNP-SNP interactions passing all three criteria, while no SNP main effects or SNP-risk factor interactions passed all three. A multivariable model including four SNP-SNP interactions explained 11.3% of the variation in LVM in the full GENOA sample and 5.6% of LVM variation in independent test sets.The results of this research underscore that context dependent effects, specifically SNP-SNP interactions, may dominate genetic contributions to variation in complex traits such as LVM. Heart disease, defined as myocardial infarction, hypertensive and ischemic heart disease, and heart failure, is the leading cause of mortality and morbidity in the United Sates . IncreasFamily and twin studies have demonstrated that genetic factors significantly contribute to the inter-individual variation in LVM in numerous racial/ethnic groups. Heritability estimates range between 0.2 - 0.6 depending on the population being studied and risk factors adjusted for -12. In aThe goal of this research was to explore the genetic architecture of LVM by identifying robust, replicated single SNP effects, SNP-environment interactions, and SNP-SNP interactions associated with LVM after adjusting for population stratification and relevant risk factors. In achieving this goal, we implemented a multi-stage approach that focuses on reducing the number of false-positive results and shows replication of effects within the study sample.The National Heart Lung and Blood Institute established the Family Blood Pressure Program (FBPP) in 1996, joining established research networks investigating hypertension and cardiac diseases. One of the four networks in FBPP is the Genetic Epidemiology Network of Arteriopathy (GENOA), which recruited hypertensive African-Americans and non-Hispanic white sibships for linkage and association studies to investigate genetic contributions to hypertension and hypertensive target organ damage. Subjects for this particular GENOA sub-study were African-Americans recruited from Jackson, Mississippi. GENOA recruited sibships containing at least two individuals with clinically diagnosed essential hypertension before age 60. Hypertension was defined by a previous clinical diagnosis of hypertension by a physician with current anti-hypertensive treatment, or an average systolic blood pressure (SBP) ≥140 mmHg or diastolic blood pressure (DBP) ≥90 mmHg on the second and third clinic visit . After i2). Diabetes was considered present if the subject was being treated with insulin or oral agents or had a fasting glucose level ≥126 mg/dL. Smoking status was defined as self-described smoker within the past year. Use of anti-hypertensive medication was based on self-report during the clinical exam.Data collection consisted of demographic information, medical history, clinical characteristics, lifestyle factors, and blood samples for genotyping and biomarker assays. Study visits were conducted in the morning after an overnight fast of at least eight hours. Blood pressure was measured with random zero sphygmomanometers and cuffs appropriate for arm size. Three readings were taken in the right arm after the participant rested in the sitting position for at least five minutes; the last two readings were averaged for the analysis. Height was measured by stadiometer, weight by electronic balance, and BMI was obtained by the standard calculation of weight (kg) divided by height squared  and wall thicknesses at, or just below, the tips of the anterior mitral leaflet in long- and short-axis views. Correct orientation of planes for imaging and Doppler recordings was verified using standardized protocols. Measurements were made using a computerized review station equipped with digitizing tablet and monitor screen overlay for calibration and performance of each measurement. LVID and interventricular septal and posterior wall thicknesses were measured using the two-dimensional view at end-diastole and end-systole according to the recommendations of the American Society of Echocardiography in up to three cardiac cycles . CalculaOne thousand nine hundred and fifty six SNPs from 268 genes known or hypothesized to be involved in blood pressure regulation, lipoprotein metabolism, inflammation, oxidative stress, vascular wall biology, obesity and diabetes were identified from the genetic association literature and positional candidate gene studies  to be ge® Sequence Detection System . Primers and probes are available from the authors upon request. Quality control measures for genotyping assays included robotic liquid handling, separate pre- and post-PCR areas, standard protocols and quality control analyses including 5% duplicates, positive and negative controls, computerized sample tracking, and data validity checks. After these quality control procedures and removal of monomorphic SNPs, 1,878 SNPs from 234 genes were available for analysis in the African-American cohort of GENOA.. Genotyping, based on polymerase chain reaction amplification techniques, was conducted at the University of Texas-Health Sciences Center at Houston using the TaqMan assay and ABI PrismThe presence of population substructure is a concern for genetic epidemiological studies because the distribution of admixture proportions within a study sample can be a source of confounding, resulting in spurious SNP-disease associations -26. BaseThe underlying admixture within the African-American GENOA sample can be accounted for through principal component analysis (PCA) . There wData analyses were conducted using the statistical language R (version 2.6) . LVM wasWe used a multi-stage approach in order to identify both main and interactive genetic effects associated with adjusted logLVM. The first stage was dedicated to conducting association analyses for SNP effects, SNP-covariate interactions, and SNP-SNP interactions. The second stage focused on reducing the possibility of false-positive association results and replication of results within our GENOA sample. Finally, we conducted multivariable SNP modeling with associations passing the second stage of analysis. This analysis approach has been previously described by Kardia et al  and SmitIn the first stage of analysis, we tested each of the 1,878 SNPs for association with adjusted logLVM using least-squares linear regression methods in the full sample ,35. The Based on the 1,878 SNPs and 15 chosen covariates, all possible SNP-covariate interactions were assessed for association with adjusted logLVM using least-squares linear regression. The covariates considered in the interactions included age, sex, SBP, DBP, height, weight, diabetes status (0/1), hypertension status (0/1), use of anti-hypertensive medication (0/1), duration of hypertension, smoking status (0/1), myocardial infarction (0/1), total cholesterol, low density lipoprotein cholesterol (LDL), and triglycerides. Age, sex, SBP, height, and weight were left out of the adjustment model in order to include this main effect in the respective test for interaction. We determined significance of the SNP-covariate interaction with a likelihood ratio test statistic comparing a full model (including interaction terms and main effects of the variables in the interaction term) to a reduced model that contains the main effects of the covariate and SNP being tested.All possible pairwise SNP-SNP interactions were tested with SNPs coded as two dummy variables to allow testing for all possible statistical epistatic effects . The staThe second stage of analysis was focused on reducing the possibility of false-positive association results and replication of results within our GENOA sample. We did this by implementing three analytic approaches: 1) False Discovery Rate (FDR) , 2) fourThe first step for reducing the probability of false positive results was to calculate the FDR q-value for all association tests . FDR is 2, this procedure was repeated ten times and the CV R2 values were averaged [2) was greater than 0.5% and interactions were considered cross-validated if the difference in average percent variation predicted in independent test samples between the full model containing the interaction terms and the reduced model containing only main effect terms was greater than 0.5%. This threshold of 0.5% was chosen because permutation tests on the models investigated in this paper, we found that the probability of observing a CV R2 × 100 greater than 0.5% by chance alone was less than 5% (results not shown). That is, Pr(CV R2 × 100 > 0.5%) <0.05 under the null hypothesis of no association.The second approach for minimizing false positive results was to use four-fold CV, a method that reduces false positive results by eliminating associations that lack predictive ability in independent test samples. We performed CV by dividing the full sample into four equally sized groups. Three of the four groups were combined into a training dataset, and the modeling strategy outlined above was carried out to estimate model coefficients. These coefficients were then applied to the fourth group, the testing dataset, to make predictions about the value of the outcome variable of each individual in the independent test sample. This process was repeated for each of the four testing sets. Because random variations in the sampling of the four mutually exclusive test groups can potentially impact the estimates of CV Raveraged . Single The third and final step to reduce false positive results was to demonstrate replication of effects within our GENOA sample. Considering the entire sample of African-Americans and randomly sampling one sibling from each sibship, without replacement, the first replication subset sample was created. From the remaining people, we randomly sampled a second sibling from each sibship to establish the second sample. Association analyses were then conducted in both of the subset samples. If a SNP, SNP-covariate, or SNP-SNP association replicated across these two samples (α = 0.10), passed FDR and CV criteria in the full sample, it was tested for homogeneity of direction and magnitude of effect across the two samples.2 > 0.005), we built a multivariable linear regression model using forward selection in the full sample of GENOA African-Americans. Residuals from the age, sex, SBP, height, weight, and admixture adjusted logLVM were used as the dependent variable for this multivariable model. The increase in percent variation of adjusted LVM explained was then calculated, as was the increased predictive ability of the model based on the full model CV R2 with the addition of each term. Because the full sample of individuals contains siblings, the associations that were included in the final multivariable model were also tested using a linear mixed effects model to account for the familial correlation and ensure that the results were not dependent upon the underlying familial correlation in the data.Based on the association tests that passed all three of the above criteria  hypertensive cohort (79% hypertensive) with an average BMI of 31 and 29% diabetic . None of these single SNP associations had an FDR q-value < 0.30 and only one had a CV R2 > 0.005 (SNP: rs2182833). Table 1,878 SNPs were tested for association with adjusted logLVM. Of these, 221 had a p-value < 0.10 in the full sample, the minimum p-value was 9.24 × 10-6 to 9.59 × 10-5), 303 replicated across sample subsets, and 112 had a CV R2 > 0.005. However, none of the SNP-covariate interactions passed all three criteria. There were a total of 28,075 SNP-covariate interactions tested. Ten of those had an FDR q-value < 0.30 .Based on the 1,878 SNPs, all possible SNP-SNP interactions were tested for a total of 1,740,614 associations. 409 of these associations passed all three criteria with an FDR q-value < 0.30, replicating in both subsets of the data, and had a CV R2. CV R2 was 5.56% when the full model included all four SNP-SNP interactions. Detailed mathematical description of the final model which includes main and interactive effects is included as an additional file.  to 0.415 (rs12971616) . Recognizing that replication in an independent cohort might not be possible because of various sources of heterogeneity; we sought to find genetic associations that replicated within our study sample and were robust across numerous multiple testing adjustment methods. The relative low level of agreement between results filtered through FDR, internal replication, and CV supports the conservative nature of our strategy for determining which results are robust and significant. Furthermore, a similar analysis approach applied to two different phenotypes, ankle brachial index  and leukA natural question arising from our study results is how these SNPs interact biologically? As these SNPs were selected from "candidate genes", biological plausibility can be argued for any individual SNP. Table There is much yet to be understood about LVM and why it is so highly predictive of heart disease and all-cause mortality, independent of other cardiovascular risk factors . The resThe authors declare that they have no competing interests.SLRK and KJM conceived of and designed the study. KJM and JC conducted statistical analysis. THM was responsible for recruiting and data collection at the Jackson field center of GENOA. KJM prepared and wrote the manuscript. All authors were involved in critical revisions of the manuscript and have agreed upon its final content.The pre-publication history for this paper can be accessed here:http://www.biomedcentral.com/1471-2350/11/160/prepubContains a list of all SNPs (and their respective gene) investigated.Click here for fileContains the R output for the multivariable model including 4 SNP-SNP interactions.Click here for file


To facilitate whole-genome association studies (WGAS), several high-density SNP genotyping arrays have been developed. Genetic coverage and statistical power are the primary benchmark metrics in evaluating the performance of SNP arrays. Ideally, such evaluations would be done on a SNP set and a cohort of individuals that are both independently sampled from the original SNPs and individuals used in developing the arrays. Without utilization of an independent test set, previous estimates of genetic coverage and statistical power may be subject to an overfitting bias. Additionally, the SNP arrays' statistical power in WGAS has not been systematically assessed on real traits. One robust setting for doing so is to evaluate statistical power on thousands of traits measured from a single set of individuals. In this study, 359 newly sampled Americans of European descent were genotyped using both Affymetrix 500K (Affx500K) and Illumina 650Y (Ilmn650K) SNP arrays. From these data, we were able to obtain estimates of genetic coverage, which are robust to overfitting, by constructing an independent test set from among these genotypes and individuals. Furthermore, we collected liver tissue RNA from the participants and profiled these samples on a comprehensive gene expression microarray. The RNA levels were used as a large-scale set of quantitative traits to calibrate the relative statistical power of the commercial arrays. Our genetic coverage estimates are lower than previous reports, providing evidence that previous estimates may be inflated due to overfitting. The Ilmn650K platform showed reasonable power (50% or greater) to detect SNPs associated with quantitative traits when the signal-to-noise ratio (SNR) is greater than or equal to 0.5 and the causal SNP's minor allele frequency (MAF) is greater than or equal to 20% (N = 359). In testing each of the more than 40,000 gene expression traits for association to each of the SNPs on the Ilmn650K and Affx500K arrays, we found that the Ilmn650K yielded 15% times more discoveries than the Affx500K at the same false discovery rate (FDR) level. Advances in SNP genotyping array technologies have made whole-genome association studies (WGAS) a readily available approach. Genetic coverage and the statistical power are two key properties to evaluate on the arrays. In this study, 359 newly sampled individuals were genotyped using Affymetrix 500K and Illumina 650Y SNP arrays. From these data, we obtained new estimates of genetic coverage by constructing a test set from among these genotypes and individuals that is independent from the SNPs and individuals used to construct the arrays. These estimates are notably smaller than previous ones, which we argue is due to an overfitting bias in previous studies. We also collected liver tissue RNA from the participants and profiled these samples on a comprehensive gene expression microarray. The RNA levels were used as a large-scale set of quantitative traits to calibrate the relative statistical power of the commercial arrays. Through this dataset and simulations, we find that the SNP arrays provide adequate power to detect quantitative trait loci when the causal SNP's minor allele frequency is greater than 20%, but low power is less than 10%. Importantly, we provide evidence that sample size has a greater impact on the power of WGAS than SNP density or genetic coverage. It has been estimated that the human genome contains more than 5 million common single nucleotide polymorphisms (SNPs) with minor allele frequencies (MAF) ≥10% There are two strategies to building whole-genome SNP arrays. One is to randomly select SNPs that are relatively evenly spaced across the genome, not taking into account the inter-SNP linkage disequilibrium (LD) patterns, such as the Affymetrix 100K and 500K SNP arrays  The identification of tag SNPs is essentially a feature selection problem. It has been well established in the machine learning field that using an independently sampled test dataset is necessary to guarantee an unbiased assessment of the selected features' operating characteristics. It has also been shown that if the evaluation takes place on the training dataset itself, then the quality of the features' performance is often anti-conservatively over-estimated, commonly referred to as the overfitting problem The key operating characteristics of several whole-genome SNP arrays have been evaluated recently on HapMap data We utilized two commercially available high-density SNP arrays on an American Caucasian cohort to obtain estimates of genetic coverage for these different SNP panels that are robust to overfitting. The estimates we obtain in this cohort are lower than previous reports. In addition, liver RNAs were extracted and profiled on a comprehensive gene expression microarray. By simultaneously utilizing these thousands of gene expression traits scored on a fixed set of genetic backgrounds, we obtain estimates of the relative power of the different SNP genotyping arrays to detect quantitative trait SNPs of varying effect sizes Human liver tissue samples were collected as described in a companion article −4. In total, 286K and 545K genotyped autosomal SNPs on Affx500K and Ilmn650K, respectively, were used in the analysis. Genotypes from Ilmn300K and Ilmn550K were also derived as subsets of the Ilmn650 data, resulting in 296K and 514K genotyped SNPs, respectively.DNA specimens were extracted and sent to Perlegen Sciences Inc. and Illumina Inc. for genotyping services using Affx500K and Ilmn650K, respectively. There was an overlap of >80,000 SNPs tiled on both SNP arrays. We found the two platforms gave highly consistent (98.7%) genotypes on their shared SNPs, suggesting both genotyping arrays are fairly accurate. We filtered out SNPs with MAF <5%, call rate <90%, or Hardy-Weinberg Test p-value <10Additionally, we purified RNA from the tissue samples and measured the 39,280 gene transcription levels using the Agilent platform. We adjusted the expression values for gender, age, and medical center by using a standard linear model. See The HapMap data are comprised of 270 individuals from four ethnic groups: (i) 30 trios from the Yoruba group in Ibadan, Nigeria (YRI); (ii) 30 trios from the CEPH collection, which are Utah residents with Northern and Western Europe ancestry (CEU); (iii) 45 unrelated Han Chinese individuals from Beijing, China (CHB); and (iv) 45 unrelated individuals from Tokyo, Japan (JPT). The CHB and JPT samples are often considered as a single East Asian sample Recall that the Affx500K platform harbors 90K common SNPs that were not utilized in the HapMap project (referred to here as Affx NonHapMap SNPs). The genotypes from the Affx500K platform measured on the 359 Liver Study subjects therefore provide two key sources of independent data: (i) genotypes of SNPs identified independently from the HapMap project (the Affx NonHapMap SNPs) and (ii) individuals sampled independently from the HapMap project. This allowed us to study SNP selection overfitting and sample overfitting, respectively, in calculating genetic coverage.Given that SNPs on the Affx500K were randomly chosen, the 90K Affx NonHapMap SNPs can be considered as a random sample of the entire 5.3M NonHapMap SNPs. We therefore utilized these Affx NonHapMap SNPs as a set of SNPs identified independently from the HapMap data. Also note that the complementary Affx HapMap SNPs (SNPs on Affx500K that are included in HapMap data) represent a random subsample of the 2.2M HapMap SNPs. We utilized these two classes of SNPs genotyped in our independently sampled human cohort scored for thousands of gene expression traits to estimate the true genetic coverage of the different SNP panels as well as assess the power these panels afford to detect associations between SNPs and traits (details below).2 cutoff (r2cutoff) with at least one SNP typed by the microarray 2cutoff, 0.8 and 0.9. Results were qualitatively equivalent in the 0.7 to 0.9 range for r2cutoff.Genetic coverage was calculated as the fraction of SNPs exceeding a pre-defined rOverfitting of genetic coverage estimates was assessed separately for SNP overfitting and sample overfitting.To systematically evaluate whether there has been SNP overfitting in estimated genetic coverage, we compared genetic coverage rates of Affx NonHapMap SNPs to Affx HapMap SNPs on the Illm650K platform. A higher genetic coverage of Affx HapMap SNPs is evidence for SNP overfitting. The SNPs represented on Ilmn300K and Ilmn550K are subsets of Ilmn650K, and we computed their genetic coverage in a similar manner.To determine whether there has been sample overfitting in previous estimates, we compared genetic coverage of the CEU individuals versus the Liver Study individuals. A higher level of genetic coverage in the former is evidence of overfitting. We made this comparison among Affx HapMap SNPs, among Affx HapMap SNPs, and among all Affx SNPs combined. Evidence for sample overfitting was present for all three sets of SNPs. Finally, for both types of overfitting, we stratified the AffxSNPs into MAF bins, and computed coverage for each bin to investigate whether the overfitting bias is also a function of MAF.2), where σ2 was constantly set to 1 and μ varied among genotypes. We set  = , , or  to investigate a range of signal strengths. Second, we conducted single-marker tests, which examined association between each SNP and each simulated trait. We surveyed three choices of α level  that are reasonable for WGAS. Kruskal-Wallis and Spearman rank correlation tests were employed because these non-parametric methods were robust to the underlying genetic model and trait distribution, thereby allowing our simulation to be useful for non-normal traits and non-additive models. We defined a “true discovery” to be any association detected within 200 kb of the causal SNP. We calculated statistical power  and the average number of true discoveries (NTD) over the set of simulated datasets. Two million simulation runs were conducted for each parameter setting.The statistical power of the SNP array platforms for WGAS were first estimated from simulation studies. First, we randomly selected a SNP from Affx500K and used its genotype to simulate trait value. Over the range of simulations, SNP genotypes from both Affx HapMap SNPs and Affx NonHapMap SNPs were utilized. We assumed the trait followed a Normal distribution N for each of the ∼40,000 gene expression traits measured. Furthermore, we repeated the tests on three permuted gene expression datasets and calculated the false discovery rate (FDR). In each permutation run, we first randomized the patient IDs in the expression file, breaking any association between expression traits and genotypes while leaving the respective correlation structures among gene expression traits and SNP genotypes intact. Second, we repeated the association tests for every expression trait and genotype pair, resulting a set of null statistics for each permutation. A standard FDR estimator was then applied to the resulting association statistics, as previously carried out on observed and permutation null statistics Based on the Liver Study subjects, we obtained new estimates of genetic coverage for the Illumina and Affymetrix SNP genotyping arrays , which aBy comparing estimates of genetic coverage derived from HapMap CEU to those from Liver Study subjects , we alsow = 2.2/7.5) and that on NonHapMap SNPs (w = 5.3/7.5). Among the Liver Study Caucasian subjects, the Ilmn300K and Ilmn650K's weighted genetic coverage was 64% and 76% at r2cutoff = 0.8, which is lower than previous reports . Furthermore, we found that the tagSNP arrays cover low MAF SNPs (e.g. MAF<15%) worse than the high MAF ones , and importantly, the overfitting bias appears to be more severe for the low MAF range  was proportional to the number of SNPs employed in the WGAS, with NFDcreening .Finally, we conducted association tests on actual traits, namely RNA expression levels or “expression traits.” It has been shown that comparing the ND from many related traits, all conditional on the same set of individuals , can lead to meaningful empirical power comparisons, where simple models often used for simulations do not have to be assumed The number of discoveries (ND) obtained on both observed data and permuted data followed the same pattern: Ilmn650K>Ilmn550K>Ilmn300K>Affx500K. Because the true and false discoveries were no longer distinguishable, we could not directly infer the SNP arrays' statistical power using ND. Instead, we compared the relative power using ND conditioning on FDR  and S3. After filtering SNPs based on MAF, call rate, and HWE p-values , a similcis-associations. In brief, for a given expression trait, only SNPs within ±1 Mb of the corresponding gene are tested. By taking these steps, the number of tests is substantially reduced and the statistical power increased, illustrated in more cis-association discoveries in cis-association genes also reflect the relative power. Consistent with cis-association. Surprisingly, Affx500K+Ilmn650K (relative power = 115%) only slightly outperforms Ilmn650K, indicating the limited return of adding additional SNPs on top of Ilmn650K.As a novel feature of our study, we were also able to investigate the power of combining Affx500K and Ilmn650K arrays for a single analysis . Since tcis-association discoveries , implying a respective 31% and 33% boost in relative power. In contrast, conditioning on the same sample size, Ilmn650K yields about 10% more cis-associations than Affy500K. This is potentially an important observation that sample size has a more profound impact on statistical power than the difference in genetic coverage among current SNP arrays. Since arrays vary greatly in price, and argument has been raised whether to choose high genetic coverage arrays or cheaper ones and run more subjects.We collected 68 additional liver samples from Caucasian donors. We performed RNA expression profiling as before and obtained SNP genotypes using the Affy500K array only. We then we pooled the sample  and reran the association tests on the Affx500K SNPs. Surprisingly, this increase in sample size (19%) results in 31% and 33% more Whole-genome association studies using high-density SNP arrays are viewed as a powerful approach to elucidating the genetic bases of common human diseases. We provided a novel investigation of two key properties for determining the performance of SNP array genotyping platforms in WGAS: genetic coverage and statistical power. The availability of (i) 90K genotyped SNPs which were identified independently from the HapMap, (ii) a new, independently sampled cohort of subjects, and (iii) thousands of related “expression traits” measured simultaneously on these subjects, yielded the opportunity to provide new insights into genetic coverage and statistical power, and make comparisons to previous results.Two strategies are usually taken in selecting SNPs and constructing genome wide arrays: random SNPs and tag SNPs. These strategies might result in different levels of performance in terms of genetic coverage and statistical power 2r≥0.8) among the “pseudo” HapMap I SNPs Since there are a limited number of common SNPs in the human genome, tag SNPs selected from the complete set  would be robust to SNP overfitting in assessing genetic coverage. At the current stage, tag SNPs are usually selected from an incomplete initial SNP set , and the remaining SNPs  would be “hidden” from the training procedure. Previous simulation studies showed that 26% of the common ENCODE SNPs in CEU had no good proxies . Certainly, larger sample size is necessary to detect weaker effects . Illumina tag SNP arrays were designed to capture common HapMap SNPs. Therefore, most of these tag SNPs have MAF ≥5%. In contrast, Affx500K harbors over 100K rare SNPs (MAF ≤5%). Because we used common SNPs to simulate quantitative traits, the Affx500K rare SNPs provided little statistical power and were therefore excluded from the analysis. Rare SNPs might be useful when the disease-causing polymorphism was also rare. However, Our SNP filtering resulted in similar number of SNPs on Affx500K and Ilmn300K, which enabled a fair comparison between random SNPs and tag SNPs. We found random SNPs actually achieved larger ND , but man2r≥0.8) among the simulated HapMap II datasets, and those SNPs could only be adequately captured by searching beyond the HapMap data. In another words, the HapMap-derived tag SNP struggles to reach the 90% genetic coverage level. Through simulations, we were able to directly test the causal SNP, allowing us to calibrate the upper bound of a SNP array's performance in WGAS. At SNR = 0.5 Click here for additional data file.Figure S2The null distribution of p-values for the Kruskal-Wallis test and Spearman Rank Correlation test. The p-value of Spearman test (p.spearman) follows the uniform distribution under the null, whereas the p-values of the Kruskal-Wallis test shows a lower density in the  range, indicating this test is conservative.(0.33 MB TIF)Click here for additional data file.Figure S3Using the expression traits, we surveyed a wide range of p-value cutoffs and the corresponding FDR values for simulated WGAS.(1.23 MB TIF)Click here for additional data file.Figure S4(A) and (B): the number of discovery (ND) observed in the expressiongenotype association screening. Please note that one expression trait sometimes showed significant association with multiple mutually proximal SNPs, because these SNPs were in strong LD. (C) and (D): the number of expression traits that showed at least one significant association with SNP(s).(1.00 MB TIF)Click here for additional data file.Figure S5The statistical power of the Affymetrix array, Illumina arrays, and “direct genotyping.” (A) SNR = 1/4 and Kruskal-Wallis test; (B) SNR = 1/4 and Spearman rank correlation test; (C) SNR = 1 and Kruskal-Wallis test; (D) SNR = 1 and Spearman rank correlation test.(0.63 MB TIF)Click here for additional data file.Table S1Genetic Coverage of tagSNP Arrays.(0.11 MB DOC)Click here for additional data file.Table S2Power and Number of Discoveries (ND) of Whole Genome SNP Genotyping Products.(0.21 MB DOC)Click here for additional data file.Table S3Number of False Discoveries in Mapping Quantitative Trait.(0.04 MB DOC)Click here for additional data file.Text S1Supplementary Materials.(0.03 MB DOC)Click here for additional data file.


Iterative DNA "resequencing" on oligonucleotide microarrays offers a high-throughput method to measure intraspecific biodiversity, one that is especially suited to SNP-dense gene regions such as vertebrate mitochondrial (mtDNA) genomes. However, costs of single-species design and microarray fabrication are prohibitive. A cost-effective, multi-species strategy is to hybridize experimental DNAs from diverse species to a common microarray that is tiled with oligonucleotide sets from multiple, homologous reference genomes. Such a strategy requires that cross-hybridization between the experimental DNAs and reference oligos from the different species not interfere with the accurate recovery of species-specific data. To determine the pattern and limits of such interspecific hybridization, we compared the efficiency of sequence recovery and accuracy of SNP identification by a 15,452-base human-specific microarray challenged with human, chimpanzee, gorilla, and codfish mtDNA genomes.In the human genome, 99.67% of the sequence was recovered with 100.0% accuracy. Accuracy of SNP identification declines log-linearly with sequence divergence from the reference, from 0.067 to 0.247 errors per SNP in the chimpanzee and gorilla genomes, respectively. Efficiency of sequence recovery declines with the increase of the number of interspecific SNPs in the 25b interval tiled by the reference oligonucleotides. In the gorilla genome, which differs from the human reference by 10%, and in which 46% of these 25b regions contain 3 or more SNP differences from the reference, only 88% of the sequence is recoverable. In the codfish genome, which differs from the reference by > 30%, less than 4% of the sequence is recoverable, in short islands ≥ 12b that are conserved between primates and fish.Experimental DNAs bind inefficiently to homologous reference oligonucleotide sets on a re-sequencing microarray when their sequences differ by more than a few percent. The data suggest that interspecific cross-hybridization will not interfere with the accurate recovery of species-specific data from multispecies microarrays, provided that the species' DNA sequences differ by > 20% (mean of 5b differences per 25b oligo). Recovery of DNA sequence data from multiple, distantly-related species on a single multiplex gene chip should be a practical, highly-parallel method for investigating genomic biodiversity. Drosophila melanogaster  oligo quartets tiling the intermediate positions ("Flynn' s Rule"). In the ape data, we typically observe low probe intensity at all positions between two SNPs that occur within 25b. Precise patterns for any given oligo will be influenced by other factors, such as [G+C] content and distribution.Where two SNPs occur within the 25 bp region covered by a particular SNP-specific oligo quartet, probe binding is affected at intermediate invariant positions. The pattern is specific and predictable. Consider two SNPs at an interval of 25 b, where one oligo quartet tiles one invariant position exactly 12 bp from either SNP. All four oligos in this set will have mismatches at their terminal  of three mammal species in different orders, three ray-finned fish species in different subclasses, and one bird species. Minimum interspecific divergence for these comparisons is > 23% (. ExperimPongo: ~14% mtDNA sequence difference) and gibbons (Hylobates: ~17% difference) .Distribution of SNP density between the ape dideoxy sequences and the tiled human sequence was calculated as a sliding window of 25 bp, starting at Position 13 of the tiled sequence. The numbers of interspecific SNPs versus intraspecific miscalls  within each of the two primate sequences were compared in a sliding window of 25 bp extending 12b on either side of each position, starting at Position 13 of the tiled reference. The SNP versus mismatch densities were averaged over all calls in each of the eight classes of Table Gorilla as the outgroup to Pan and Homo.To compare intra- and interspecific differentiation among the mtDNA genomes, we performed a phylogenetic analysis with the program PAUP (36). We aligned the 15,452b of the three primate dideoxy sequences in this paper, together with the homologous portions of five additional sequences from GenBank listed in Figure The author(s) declares that there are no competing interests.The experiments described were part of the BSc (hons) thesis research of SMCF, including dideoxy sequencing and preparation and execution of the microarray experiments. SMC supervised the research in his lab, developed the algorithms for the numerical analysis, and drafted the manuscript. Both authors read and approved the final version of the manuscript.


Using the Problem 1 data set made available for Genetic Analysis Workshop 15 (GAW15), we identified many expression phenotypes that have significant evidence of association and linkage to one or more chromosomal regions. In particular, six of ten phenotypes that we found to be regulated by cis- and trans-acting loci were also mapped by a previous analysis of these data in which a total of 27 phenotypes were identified with expression levels regulated by cis-acting determinants. However, in general, the p-values associated with these regulators identified in our study were larger than in their studies, since we had also identified other factors regulating expression. In fact, we found that most of the gene expression phenotypes are influenced by at least one trans-acting locus. Our study also shows that much of the observable heritability in the phenotypes could be explained by simple single-nucleotide polymorphism associations; residual heritability was reduced and the remaining heritability may represent complex regulation systems with interactions or noise.Assuming multiple loci play a role in regulating the expression level of a single phenotype, we propose a new approach to identify  In an linkage  and asso linkage . For the linkage  examined linkage . Here we, so we focused on the remaining 3462 gene expression traits. The genotypes for 2819 autosomal SNPs for the same individuals were generated by The SNP Consortium .Expression levels for 3554 genes, taken from the lymphoblastoid cell lines of 194 members of 14 CEPH Utah families, were made available for GAW15 [cis-Regulatory variants were defined as SNPs either within a gene, up to 1 Mb proximal to the start of the gene, or up to 1 Mb distal to the end of the gene. trans-Regulatory polymorphisms are defined as all SNPs elsewhere in the genome. Physical locations of probe sets were obtained from the Affymetrix annotation table . The Rutgers map was used to establish a correspondence between the megabase locations on the physical map and the genetic map . Markers that could not be mapped using Rutgers map, but that were located between physically anchored markers, were placed on the genetic map by linear interpolation.cis- and trans-regulatory polymorphisms , and then we fit a linear regression model containing gender and the cis-SNPs as covariates. If no cis-SNPs were identified for the probe set, we used only gender as a covariate; if more than one cis-SNP was identified, a stepwise algorithm based on the Akaike information criterion (AIC) was applied to choose a predictive set of cis-SNPs; if no SNPs were kept after running the stepwise algorithm, we forced in the SNP with the smallest p-value. The SNPs were coded 0, 1, and 2, representing homozygous rare, heterozygous, and homozygous common genotypes, respectively. Within-family dependence was not modelled, although we did some sensitivity analyses examining the effect of this assumption (see Discussion). We report the nominal, parametric p-values for the test of no association for each SNP (β = 0). 2) Residuals were obtained from the previous linear models containing gender and cis-SNPs (if any). Genome-wide multipoint linkage analysis was then performed on the residuals using the MERLIN-REGRESS command in the statistical genetics software MERLIN [trans-SNPs because the linkage models used residuals that had already been adjusted for cis-SNPs. We evaluated which of these SNPs significantly and independently predicted gene expression phenotype by using the stepwise procedure with AIC to choose the optimal set of SNPs in the model.Three steps were used to identify e MERLIN . 3) For cis-SNPs (if any) and gender (H_cis); 3) residuals to a stepwise regression analysis containing SNPs (if any) that have LOD of at least 2.0 and gender (H_lod).The variance components analysis in MERLIN was used to estimate heritability based on: 1) raw gene expression profiles (H_exp); 2) residuals to a stepwise regression analysis containing cis-SNP. Table cis-SNPs nearby, and the number that were associated with the phenotypes. There were 1286 expression phenotypes without an associated cis-SNP. Of the 2176 phenotypes with cis-SNPs, the stepwise model chose only one SNP as associated for 78% (1697 out of 2176) of the phenotypes, although 81% (1763 out of 2176) of the phenotypes had more than one nearby SNP. Using a definition of significance of p = 0.01, 288 phenotypes were associated with at least one significant cis-regulator, and of these, 43 phenotypes were associated with two significant cis-regulators. If a more stringent significant level, say p = 0.001, is used, the number of significant cis-SNP drops dramatically , and p = 4.3 × 10-7 (LOD ~ 5.3). Using the same thresholds, we identified 1556 and 337 expression phenotypes with at least one marker showing evidence for linkage beyond these thresholds, respectively. In comparison, Morley et al. [We then performed linkage analysis using residuals for the 3462 expression phenotypes derived from the fitted association models using the associated y et al.  defined p-value threshold of 0.01, we found 930 of the 3034 phenotypes were significantly associated with one marker and another 893 phenotypes were significantly associated with more than one marker. The remaining 1639 phenotypes showed no significant association with any marker. Again, if a more stringent significance level is used, the number of identified significant trans-SNPs will be decreased, but not by as much as in Step 1. Focusing on the most significant SNP for each phenotype, we found that 1514 (83.0%) expression phenotypes are most strongly influenced by a trans-acting transcriptional regulator.We then performed a second set of stepwise linear regression analyses for the expression phenotypes, including gender and SNPs that showed LOD scores ≥ 2.0. There were 3034 of these 3462 phenotypes with at least one linkage peak. Given a cis-acting as well as at least 1 trans-acting regulator  of the phenotypes show associations with more than one trans-regulator. As can be seen from Table cis-regulators and trans-regulators, some of the most strongly linked cis-SNPs also show evidence of association in Step 1. For these 10 phenotypes, we also observed that the variation explained in the second association analysis (Step 3) is greatly increased compared with that in the first association analysis (Step 1). According to Step 3, between 2 and 8 polymorphisms can the majority of the phenotypic variance; for gene VAMP8 (Table Of the 337 phenotypes with at least one LOD score over Morley's second threshold LOD ≥ 5.3), 10 (3.0%) were found to have a or Table , and 6 o.3, 10 . It would also be interesting to include age to examine more complex genetic relationships. We also performed linkage analysis on residuals to models containing gender and cis-controlled phenotypes as Morley et al. [cis-SNPs in the regression analysis to explore joint effects. Despite performing linkage on residuals, we sometimes found linkage to regions near the cis-SNPs, probably due to multi-marker linkage patterns, incompletely explained by allelic variability.We allowed multiple SNPs to be considered for each linear regression and hence we identified phenotypes that are associated with several different SNPs in different parts of the genome. For some genes, a very large proportion of the variability was explained by a combination of several SNPs  while the GEE model identified only 10 with one significant cis-SNP (p ≤ 0.01). Five of these phenotypes were identified by both methods. By ignoring familial clustering, we may have p-values that are too small. It would be worth fitting random effect models or GEE models to all the data, as well as models that are robust to non-normal distributions. However, the number of families here is quite small and any general conclusions would be better drawn from a larger sample.Although we used simple linear regression to explore SNP associations and did not correct for additional familial dependence in this analysis, we compared our first stage results with generalized estimating equation (GEE) models using only the 413 phenotypes where there was exactly one Conceptually, we showed that a sizeable proportion of the observable heritability could be explained by simple SNP associations for these lymphoblastoid expression phenotypes. The 5.5% of the phenotypes where residual heritability remained over 0.4 may be influenced by complex regulation systems.The author(s) declare that they have no competing interests.CMTG proposed the methods. PH and HL analyzed the data. PH drafted the manuscript. WX and JB participated in the data analysis. JB directed GEE analysis. All authors revised the manuscript and read and approved the final manuscript.


Linkage disequilibrium (LD) mapping is commonly used to evaluate markers for genome-wide association studies. Most types of LD software focus strictly on LD analysis and visualization, but lack supporting services for genotyping.2SNPing, which provides a complete package of mining tools for genotyping and LD analysis environments. The software provides SNP ID- and gene-centric online retrievals for SNP information and tag SNP selection from dbSNP/NCBI and HapMap, respectively. Restriction fragment length polymorphism (RFLP) enzyme information for SNP genotype is available to all SNP IDs and tag SNPs. Single and multiple SNP inputs are possible in order to perform LD analysis by online retrieval from HapMap and NCBI. An LD statistics section provides D, D', r2, δQ, ρ, and the P values of the Hardy-Weinberg Equilibrium for each SNP marker, and Chi-square and likelihood-ratio tests for the pair-wise association of two SNPs in LD calculation. Finally, 2D and 3D plots, as well as plain-text output of the results, can be selected.We developed a freeware called LD2SNPing thus provides a novel visualization environment for multiple SNP input, which facilitates SNP association studies. The software, user manual, and tutorial are freely available at .LD Single nucleotide polymorphisms (SNPs) are very important markers for disease  and cancLinkage disequilibrium (LD) is one of the most commonly used methods when choosing informative SNPs that represent the original SNP distribution in a genome for genome-wide association studies. LD mappings are commonly used to evaluate markers across large data sets. Given the vast amount of data in association studies, visualization of the LD results in graphical form rather than text form facilitates the interpretation of the results considerably . and  and [16]SNP b126  and SNP SNP b126 . The defNP b126 [ version NP b126 [, which i2SNPing software are available as a free download from . Many animations explaining how to use the LD2SNPing software are provided on the homepage and embedded in the user manual , Word (.doc) and NotePad (.txt) formats. The first and second rows for each file are reserved for the user-defined SNP name and the distance between SNPs , respectively. Individual genotypes accept the following formats: NN, N_N, and N/N (N is one of four possible nucleotides). If the input file is missing a genotype, it is automatically bypassed in LD2SNPing processing without interference. Some example files for testing are available in the example file folder of the LD2SNPing software package.LD2SNPing provides the rsID# input for online retrieval of individual SNP information from the dbSNP of the NCBI  input to provide tag SNPs through online retrieval from HapMap . HapMap-CEU, HCB, JPT and YRI are acceptable for selection.In Figure Before performing LD analysis, it is necessary to collect SNP genotype data for genes of interest, such as the SNP ID input Figure  and tag 2SNPing executes RFLP restriction enzyme mining upon clicking of the RFLP box indicated by arrow 6 of Figure LD2SNPing provides for file input and sample file input to perform LD analysis and visualization  value and HWE-P values for each analyzed SNP when LD analysis is performed (not shown). A more detailed description is given in the user manual.The distance between SNPs supplied in the input file can be optionally displayed or hidden  of paired SNPs are provided. These values are matched to the LDA software [LDw Figure  will ope2 Figure  is clicksoftware   and graphic files (.jpg) for convenience. The LD parameters are exported to a single file. Figure δQ and ρ values.Many kinds of software for LD visualisation are freely available. LDA , Haplovi2SNPing  rs9534342, rs9943888, rs11571662, rs206120, rs206342, rs542551, rs9567552, rs206079, rs9562605, and rs14448 and 2) rs9534275, rs9943888, rs11571579, rs206146, rs206077, rs573014, rs9567552, rs9534174, rs144848, and rs9562605.2SNPing provides the SNP ID searching to online retrieval to dbSNP in NCBI for RFLP analysis. However, the RFLP analysis for SNP ID input may be unable to provide the restriction enzyme information due to the nature of SNP itself. For example, the sequence information for rs9943888 and rs11571579 are retrieved successfully in LD2SNPing but only rs11571579 has the suitable restriction enzymes to mine (not shown). This is the nature for the SNP itself but not the RFLP analysis system error. For the wet experiment of PCR-RFLP, the users need the primer design software such as the "Prim-SNPing" [The LD-SNPing"  for prim-SNPing"  for the 2SNPing has the following characteristics: 1) it provides a search function for online retrieval of SNP information from dbSNP of NCBI; 2) it provides gene-centric tag SNP selection through online retrieval from HapMap; 3) all the SNP IDs and tag SNPs are processed to mine RFLP restriction enzymes for SNP genotype; 4) it provides LD measurements for D, D', r2, δQ, and ρ, along with the P value of the Hardy-Weinberg Equilibrium for each SNP marker and the P values of the Chi-square and likelihood-ratio tests for the pair-wise association of two SNPs in LD calculation; 5) it accepts multiple SNP inputs to perform LD analysis by online retrieval from HapMap and NCBI; 6) it presents both 2D and 3D visualization with LD-related measurements shown on the graphs; 7) it provides both graphic and plain-text outputs for LD analysis. In conclusion, LD2SNPing is a novel and integrated visualisation software designed to provide the user with the tools necessary for genotyping and LD analysis. It provides a simple and user-friendly interface with integrated functions for retrieval of SNP information, LD statistical calculation, analysis and visualization.LDProject name: LD2SNPing: Linkage disequilibrium plotter and RFLP enzyme mining for tag SNPsProject home page:  with software and user manual for download.Operating system(s): Platform-independentProgramming language: JavaOther requirements: Java 1.5.0 or higherLicense: Free for non-commercial useAny restrictions to use by non-academics: Please contact corresponding author.SNP: single nucleotide polymorphism; LD: linkage disequilibrium; RFLP: restriction fragment length polymorphism; HWE: Hardy-Weinberg Equilibrium; EM: expectation-maximisation algorithm; HUGO: Human Genome Organization; MAF: minor allele frequency.HWC and LYC wrote the manuscript. LYC provides the genomics information and LD-related statistics. YJC designed and developed the Java implementation of the underlying algorithms and GUI. YHC improved the RFLP performance and online retrieval for SNP information. HWC instructed HCH and HCC regarding software testing, improvement, and animation preparation. CHY coordinated and oversaw this study. All authors read and approved the final manuscript.2SNPingUser manual for LD. User manual for LD2SNPing.Click here for file


Penicilium chrysogenum. Although these SNPs represent only 0.1 percent of the nucleotides in the genome, they collectively altered 1.8 percent of predicted gene models between these strains. To determine which SNPs or combinations of SNPs were responsible for this variation, we predicted protein-coding genes in 500 intermediate genomes, each identical except for randomly chosen alleles at each SNP position. Of 30,468 gene models in the genome, 557 varied across these 500 genomes. 226 of these polymorphic gene models (40%) were perfectly correlated with individual SNPs, all of which were within or immediately proximal to the affected gene. The genetic architectures of the other 321 were more complex, with several examples of SNP epistasis that would have been difficult to predict a priori. We expect that many of the SNPs that affect computational gene structure reflect a biologically unrealistic sensitivity of the gene prediction algorithm to sequence changes, and we propose that genome annotation algorithms could be improved by minimizing their sensitivity to natural polymorphisms. However, many of the SNPs we identified are likely to affect transcript structure in vivo, and the synthetic association study approach can be easily generalized to any computed genome annotation to uncover relationships between genotype and important molecular phenotypes.Identifying DNA polymorphisms that affect molecular processes like transcription, splicing, or translation typically requires genotyping and experimentally characterizing tissue from large numbers of individuals, which remains expensive and time consuming. Here we introduce an alternative strategy: a “synthetic association study” in which we computationally predict molecular phenotypes on artificial genomes containing randomly sampled combinations of polymorphic alleles, and perform a classical association study to identify genotypes underlying variation in these computationally predicted annotations. We applied this method to characterize the effects on gene structure of 32,792 single-nucleotide polymorphisms between two strains of the antibiotic producing fungus  As the cost of DNA sequencing plummets it is increasingly possible to sequence multiple genomes from a single species to identify naturally occurring polymorphisms. However, characterizing the phenotypic consequences of specific variants remains a significant challenge.A crucial first step in this process is to understand how polymorphisms alter molecular phenotypes, such as protein-coding gene location, structure, and expression pattern. While the effects of some sequence changes, such as the gain or loss of a stop codon, are predictable, the overall relationship between sequence changes and gene structure remains unresolved.Genome-wide association studies (GWAS), which have been highly effective at identifying polymorphisms that have an effect on disease and other organism level phenotypes Here we present a strategy that associates naturally occurring polymorphisms with variation in inferred molecular phenotypes – in this case computationally predicted protein-coding genes. This approach has several clear advantages. Most significantly, it does not require experimental analysis of hundreds or thousands of samples. Furthermore, the use of a computed phenotype allows us to circumvent the need for a large population of genotyped or sequenced individuals. Instead, we computationally phenotype a large population of “intermediate” genomes, each containing a random sample of alleles from a set of polymorphisms identified from a natural population. This design allows us to get strong statistical power to detect associations by arbitrarily increasing the number of intermediate genomes analyzed.in vivo. However, gene prediction programs and other tools for computational annotation are becoming increasingly accurate, and thus our synthetic association study is likely to identify many polymorphisms that are good candidates for experimental investigation. We also believe that probing the sensitivity of gene prediction programs to naturally occurring polymorphism will help improve these and other computational annotation algorithms that are becoming increasingly important in biology.Because collections of predicted transcripts only partially capture the complexity of real transcriptomes, we expect many of the variable gene models and associated polymorphisms not to be relevant Penicillium chrysogenum, the fungus originally identified by Alexander Fleming as the source of penicillin, remains an important commercial source of this antibiotic. During the past 60 years, industrial strains of P. chrysogenum have undergone many rounds of mutagenesis and selection in order to improve drug production P. chrysogenum have been sequenced: one from an industrial strain, here termed “vdB” While assemblies were available for both strains, we wanted to compare gene predictions between the two genomes without anomalies arising from chromosomal rearrangements or assembly errors. We therefore identified 32,792 well-supported SNPs between the two strains by aligning reads from UCB against the vdB assembly. We used these SNPs to generate a new genome sequence, vdB*, which was identical to vdB except that all of the SNP positions were mutated to the UCB allele.To determine the effects of these SNPs on gene predictions, we ran the gene prediction program SNAP −38 by two-tailed t-test). This suggests that exons that are less confidently determined by SNAP may be more prone to vary in response to SNPs. However, given the high degree of overlap between the SNAP score distributions of variable and non-variable exons, it is not possible to predict a priori whether an individual SNAP gene model is sensitive to sequence polymorphisms.Variable exons had lower SNAP scores (23±33) than non-variable exons (44±60)  vary between vdB and vdB*. To determine how this compared to what might be expected by chance, we generated 100 versions of the vdB genome with randomly positioned mutations. The same number and types of base pair changes were made in these randomized genomes as between the vdB and vdB* genomes.There were over twice as many gene model changes between vdB and the randomized genomes  as between vdB and vdB* (1032) . Thus, aBetween the vdB and vdB* genomes, we were unable to distinguish between an exon gain and an exon loss (or intron gain versus intron loss) without an outgroup to determine the ancestral SNP allele. However, for the defined mutations of the randomized genomes, we found that there were more exon losses than exon gains (259+/−19 exon losses versus 154+/−16 exon gains) and more intron gains than intron losses (309+/−16 intron gains versus 37+/−6 intron losses). Therefore, at least with random mutations, it is more common to lose exons and gain introns.We next wanted to investigate the mechanism by which SNPs were affecting gene predictions. To do this, we first needed to identify which SNPs were affecting which SNAP predictions. This proved difficult because: 1) it was unknown if a gene prediction was affected by a single SNP or multiple SNPs; 2) there were often many SNPs close to and within the altered gene model; and 3) many of the altered gene predictions did not have SNPs in obviously functional sites (e.g. a stop codon).in silico genetics” approach, in which we computationally mutated genomes and examined the resulting SNAP phenotypes. One possible approach would be to generate all 32,792 possible single-mutant genomes, each identical to vdB except at a single SNP, to see if one of the single mutants phenocopied vdB* at a specific locus. However, running SNAP on this many genomes would take an inordinate amount of computational time. Moreover, in order to find cases where 2, 3, or more SNPs together influenced a single gene, one would additionally need to generate double, triple, etc. mutant genomes.To overcome these difficulties, we set out to use an “As a more effective and tractable alternative, we devised a strategy that we refer to as a synthetic association study. A typical association study examines the relationship between genotype and phenotype within a population of individuals whose genomes have been shuffled by an extended period of meiotic recombination. In our synthetic association study, we computationally shuffled the SNP genotypes of vdB and vdB*, simulating 500 genomes that were intermediate in genotype to vdB and vdB*. Specifically, at every SNP position, each intermediate genome had a 50% chance of incorporating either the vdB or UCB allele. For each of these 500 intermediate genomes, we ran SNAP to generate the gene prediction phenotype and then looked for associations between specific SNP genotypes and gene model phenotypes .−103). Encouragingly, all of the perfectly correlating SNPs were local to their associated events. Most SNPs were located within the boundaries of the event (89%), while the remaining SNPs were external to the event but nearby (median of 11 bp away). As hidden markov model (HMM) gene predictions are expected to be sensitive primarily to local perturbations, these results lend confidence to the synthetic association study method.As multiple exons often changed in a coordinated manner, we grouped 2 or more polymorphic exons into an “event” when they co-occurred 100% of the time in the intermediate genomes. For example, in To better understand how these perfectly correlating SNPs affected gene models, we examined the positions of the SNPs relative to their associated events. The SNPs were often located in “influential sites” in a gene, defined here as the start codons, stop codons, splice donor, and splice acceptor sequences . 92 out In addition to the 226 perfect correlations between events and SNPs, there were also many cases of SNPs and events that were highly, but not perfectly, correlated. We interpret these correlations to mean that there are modifier SNPs that affect but are not completely responsible for the phenotype of that event.To determine which of these SNP-event correlations were significant, we compared the distribution of correlations to a theoretical model in which all SNPs and events were unassociated. Unassociated SNPs and events are predicted to have a distribution of observed correlations that peaks at zero and extends as a binomial distribution. The distribution of event-SNP correlations in the association study fit this binomial curve for correlations between 0 and 0.2, but diverged for higher correlation values . After wWe next asked if the exon model variation was due to a small number of genotypic changes (some with a large effect) or many changes . 226/557 (41%) events were completely controlled by a single SNP . 243 eveOf the events associated with multiple SNPs, we observed several cases in which an event could be “completely explained” by 2, 3, or 4 SNPs, meaning that the genotype at only the 2, 3, or 4 SNPs was relevant for predicting the gene model phenotype . We perfOverall, we found all of the SNPs that control an event for only 50% (n = 280) of the events . Thus, it is common for events to have one (or more) small, modifier SNPs that are below the significance cutoff of 0.244, where we are unable to detect them .Traditional association studies have trouble identifying causative alleles when the phenotype of interest is examined in a small number of individuals, or when the alleles that influence the phenotype are of small effect Computational methods play an important role in genome annotation. To the extent that these methods accurately capture biological reality, they provide a powerful means to understand the molecular consequences of sequence variation within and between species. However, as these methods have grown more accurate, they have also grown more complex, to the point where it is no longer possible to predict their behavior.To study the unpredictable behaviors of these complex models – and hopefully the biology they represent – we have co-opted techniques developed to study complex organisms. Association studies analyze a population of individuals to understand the ways in which genes influence phenotypes. Here, we present the concept of a synthetic association study for analyzing the behaviors of complex genomic programs. Since the phenotype we examined was strictly computational, we could easily phenotype an arbitrarily large synthetic population of intermediate genomes to understand how sequence polymorphisms produce variable gene predictions. The synthetic association study is not limited to gene prediction programs, and could be extended to understand any complex biological phenomenon that is modeled by a complex program. We discovered at least one SNP that was significantly associated with 85% of variable gene predictions. Additionally, we identified many cases in which 2, 3, 4, or more SNPs were responsible for the gene model phenotype, demonstrating that SNPs can affect gene predictions in a complex and combinatorial manner.in vivo.Of the 226 SNPs that were perfectly correlated with a variable gene model, 92 (41%) were located in obviously influential sites within a gene . To the extent that the affected genes exist, which we believe most do, these SNPs are likely to affect their structure The remaining 134 SNPs were found in the middle of exons, introns, or intergenic regions. While sequence motifs inside exons and introns are known to influence gene boundaries through the action of splicing regulators that bind these sites, these effects were not explicitly modeled in the HMM used in SNAP Any improper sensitivity of SNAP to polymorphisms should be of serious concern to those who wish to use gene predictions for subsequent experiments or genome analyses, especially given that SNAP was additionally sensitive to random polymorphisms that could result from random mutation or sequencing error. We therefore suggest that inappropriate sensitivity to polymorphism should be a key criterion of gene model program assessment. Currently, analyses of the accuracy of gene prediction programs rely heavily on comparisons between gene predictions and experimentally validated gene sets in model organisms SNPs were identified using MosaikAligner C. elegans SNAP parameters, we ran SNAP on the vdB FASTA file to create a .zff training data file. We took the output of this run to use as the input parameters for another SNAP training run on the vdB FASTA file. This cycle was repeated for 25 training rounds to optimize the SNAP parameters for P. chrysogenum.Beginning with the Beginning with the vdB FASTA file, we altered every SNP position to contain the UCB instead of the vdB allele. Thus, the vdB* genome has the vdB genome as a backbone, and is different at only 32,792 bases (0.1% of the genome).Intermediate genomes also have the vdB genome as a backbone. At every SNP position, we randomly incorporated (with a 50% chance) either the UCB allele or the vdB allele. Thus, SNP alleles are independent and there is no linkage.Subsequently, SNAP was run on all intermediate genomes using the previously described parameters (see ‘SNAP parameter training’). We then determined the presence or absence of every exon model that was predicted in any of the SNAP runs. This allowed for the possibility of observing exons in intermediate genomes that were not predicted in either the vdB or vdB* genomes. Exon models were defined by their contig, type , start position, stop position, and strand.Exon changes that were completely correlated or completely anti-correlated in their patterns of presence and absence in the vdB, vdB*, and the 500 intermediate genomes were grouped together into an “event.”As we were interested in understanding the variation between vdB and vdB*, we only considered events which were polymorphic between these two genomes. 99.8% of these events contained grouped exons that were all on the same contig (557/558); the single event that grouped exons from multiple contigs was removed from the analysis.In cases where multiple exons in the same genome were grouped into a single event, 90% of the grouped exons were in the same gene, and 10% were in neighboring genes, demonstrating that grouped exons tended to be local.We searched for correlations between the patterns of event presence/absence and the occurrence patterns of every SNP. Some SNPs-event pairs (n = 226) perfectly co-occured, while many were more weakly correlated, as seen in We converted the number of times a SNP allele co-occurred with an event into a “correlation”, defined as:This formula is a linear conversion that converts a SNP-event agreement frequency of 500 into a correlation of 1, and a SNP-event agreement frequency of 250 into a correlation of 0.Using the start and stop positions of each exon, we determined the overlap between pairs of exons in vdB and vdB*. Exon gain/loss changes were defined as single exons that overlapped no exons in the other genome. Intron gain/loss changes were defined as single exons that overlapped 2 or more exons in the other genome. Shift exon changes were defined as a single pair of overlapping exons that had different start and/or stop sites. Finally, “flip” exon changes were defined as any case where exons in the same event were positioned on opposite strands. More extreme changes were considered to be hierarchically more important than other changes; for example flip events were not further analyzed for the other categories of change, and an exon change that could be analyzed as both a join/split event and a shift was considered to be a join/split change, because this was the more extreme change. Individual events could consist of multiple changes, for example, a single event could have both a “shift” change and an “exon gain/loss” change involving different exons.We began by assessing the number of SNP polymorphisms of each type (e.g. number of A→T transitions) and by counting the number of As, Ts, Cs, and Gs in the vdB genome that had at least 5× coverage by the UCB reads. We then mutated the vdB genome such that at every base there was a small probability of mutation, based on how many mutations we had already made and how much of the genome remained un-mutated. For example, at every A in the vdB genome, the probability of mutating to a G was:To categorize gene model changes in a randomized genome, we identified all gene model predictions that were different between the vdB genome and the randomized genome. We next determined which exons in one genome overlapped which other exons in the second genome. If an exon in one genome overlapped no exons in the other genome, we classified this situation as an “exon gain/loss.” If an exon from each genome overlapped each other and only each other, we classified this situation as a “shifted exon.” Finally, if two exons in one genome overlapped a single exon in the other genome, we classified this situation as an “intron gain/loss.” This analysis was a similar classification system as that used for events, but used a distinct method. This method did not rely on any grouping of exons into events.If a SNP was within the first or last 3 base pairs of a gene, it was classified as being in the start or stop sites, respectively. If a SNP was located in an intron but within 5 base pairs of the beginning or the end of the intron, is was classified as being in a splice donor or splice acceptor site, respectively. Otherwise, a SNP was classified as intronic, exonic, or intergenic, depending on its location.A SNP was defined relative to a pair of genomes based on a hierarchy of functional locations. For example, a SNP that was located in a start codon in vdB genome and in an intron in vdB* was classified as a start codon SNP, because its position in the start codon was considered more likely to reflect a functional role. The hierarchy of SNP positions was defined as follows: start, stop>splice donor, splice acceptor>exon>intron>intergenic.An event was considered “completely explained” if each combination of SNP alleles was always associated with a single event phenotype. For example, in The event phenotype frequency was defined as the percentage of the intermediate genomes that contained the less common version of the event. Thus, events that had each version occur 250/500 times had a frequency of 0.50 and events that had one version occur either 0 or 500 times had a frequency of 0.00.


Dioscorea bulbifera, Eriobotrya japonica, Tussilago farfara and Ephedra sinica could be potential rich sources of natural antioxidants.Antioxidant capacities of 56 selected Chinese medicinal plants were evaluated using the Trolox equivalent antioxidant capacity (TEAC) and ferric reducing antioxidant power (FRAP) assays, and their total phenolic content was measured by the Folin-Ciocalteu method. The strong correlation between TEAC value and FRAP value suggested that the antioxidants in these plants possess free radical scavenging activity and oxidant reducing power, and the high positive correlation between antioxidant capacities and total phenolic content implied that phenolic compounds are a major contributor to the antioxidant activity of these plants. The results showed that  They are extremely reactive, highly unstable and potentially damaging transient chemical species. Under physiological conditions, the cellular redox state is tightly controlled by antioxidant enzymatic systems and chemical scavengers such as endogenous enzymes, dietary antioxidants as well as some hormones . HoweverThe best health and nutrition results can be achieved not only from the consumption of fruits and vegetables with high antioxidant capacities, but also from medicinal herbs and plants . SeveralThe aims of this study were to find new sources of safe and inexpensive antioxidants from 56 selected Chinese medicinal plants using the ferric reducing antioxidant power (FRAP) and Trolox equivalent antioxidant capacity (TEAC) assays, to determine their total phenolic contents using the Folin-Ciocalteu method, and to investigate the relationship between antioxidant capacity and total phenolic content.2.2.1.•+ radicals, and is widely used to screen antioxidant activity of fruits, vegetables, foods and plants, and is applicable to both lipophilic and hydrophilic antioxidants [Dioscorea bulbifera showed the highest antioxidant capacity (708.73 μmol Trolox/g), followed by Eriobotrya japonica (326.87 μmol Trolox/g), Tussilago farfara (217.62 μmol Trolox/g), Ephedra sinica (197.69 μmol Trolox/g) and Ardisia japonica (164.17 μmol Trolox/g), whereas Pinellia ternata exhibited the lowest antioxidant capacity (0.61 μmol Trolox/g).Trolox equivalent antioxidant capacity (TEAC) assay is one of the most commonly employed methods for determining antioxidant capacity. The TEAC assay measures the ability of a compound to scavenge ABTSoxidants . In part2.2.2+/g to 856.92 μmol Fe2+/g, with large differencse in antioxidant capacities of up to 5713-fold. Dioscorea bulbifera possessed the highest antioxidant capacity (856.92 μmol Fe2+/g), followed by Tussilago farfara (455.64 μmol Fe2+/g), Eriobotrya japonica (437.40 μmol Fe2+/g), Ephedra sinica (388.68 μmol Fe2+/g) and Arctium lappa (223.68 μmol Fe2+/g). Sargassum fusiforme showed the lowest antioxidant capacity (0.15 μmol Fe2+/g) among these plants.The antioxidant capacity of the plant extract largely depends on both the composition of the extract and the test system. It can be influenced by a large number of factors, and can not be fully evaluated by one single method. It is necessary to perform more than one type of antioxidant capacity measurement to take into account the various mechanisms of antioxidant action . TherefoDioscorea bulbifera, Eriobotrya japonica, Tussilago farfara and Ephedra sinica had the highest antioxidant capacities among the 56 plants based on a combinative consideration of the results obtained by FRAP and TEAC assays. They are potential sources of natural antioxidants for preparation of crude extracts or further isolation and purification of antioxidant components. Especially, if the crude extract is nontoxic after the toxicological assessment, further isolation and purification of antioxidant components is not necessary because health benefits of the extract might be from additive and synergistic effects of phytochemicals in the extract [As shown in  extract . At thisDioscorea bulbifera is used to prevent and treat several diseases, such as sore throat, Struma, tumors, diabetes and leprosy [O-galactopyranoside, diosbulbin B, myricetin-3-O-galactopyranoside and myricetin-3-O-glucopyranoside were identified from this plant [ leprosy ,16. To ois plant , and somEriobotrya japonica is traditionally used for the prevention and treatment of cough and asthma, which contained some phenolics and triterpenes that exhibited beneficial effects of anticancer, anti-inflammation, hypoglycemia and hypolipidemia [Eriobotrya japonica was assessed for the first time. It showed very high antioxidant capacity, and is a potential source of natural antioxidant. Ursolic acid, oleanolic acid, maslinic acid, malic acid, amygdalin, saponins, hyperin and catechin were identified from this plant [ipidemia ,18. In tis plant ,18, and Tussilago farfara is used for the treatment of cough, bronchitis and asthmatic disorders, and has antimicrobial activity [activity . The antactivity . Faradioactivity ,19, and Ephedra sinica, which is used for treatment of multiple symptoms of cold and allergy. It is also useful to control body weight, treat fulminant hepatic failure, and to alleviate inflammatory responses [In the present study, another new candidate for a natural antioxidant was esponses ,20,21. ER2 = 0.9348), which implies that the antioxidants in these plants were able to scavenge free radicals (ABTS•+) and reduce oxidants (ferric ions). However, some plants, such as Fverticillata Willd and Arctium lappa, possessed relatively high radical scavenging activities, but low oxidant reducing activities. These differences might result from the different antioxidant mechanisms of the antioxidants they contain, which is worth to be studied further.As shown in 2.3.Dioscorea bulbifera showed the highest phenolic content (59.43 mg GAE/g), followed by Eriobotrya japonica (31.47 mg GAE/g), Tussilago farfara (30.03 mg GAE/g), Ephedra sinica (27.70 mg GAE/g), and Pueraria lobata (24.01 mg GAE/g), whereas Pinellia ternata showed the lowest phenolic content (0.12 mg GAE/g) of these plants. Phenolic compounds are plant metabolites characterized by the presence of several phenol groups. Some of them are very reactive in neutralizing free radicals by donating a hydrogen atom or an electron, chelating metal ions in aqueous solutions [Total phenolic content of the 56 selected plants were measured using the Folin-Ciocalteu method, and the results are shown in olutions . Besidesolutions .2.4.Despite the presence of a wide range of the total antioxidant capacities and total phenolic contents among the selected plants, linear positive relationships could be found between the TEAC value and total phenolic content, as well as between the FRAP value and the total phenolic content, as shown in Perilla frutescens, which exhibited relatively high antioxidant capacity, but did not contain comparable phenolic content. This suggests the presence of other antioxidant compounds in some of the medicinal plants, which meets the general agreement that the extracts of Chinese medicinal plants often contain complex mixtures of different kinds of active compounds, and the contribution from compounds other than phenolics should not be neglected.The strong correlations between the results using the two methods of measuring antioxidant capacity and the total phenolic content showed that phenol compounds largely contribute to the antioxidant activities of these plants, and therefore could play an important role in the beneficial effects of these plants. The results were in accordance with other researches ,24. Howe3.3.1.s-triazine (TPTZ) and gallic acid were purchased from Sigma-Aldrich . Potassium persulfate, iron (III) chloride 6-hydrate, iron (II) sulfate 7-hydrate, acetic acid, sodium carbonate, acetic acid, sodium acetate, hydrochloric acid and methanol were obtained from Tianjing Chemical Factory . All the chemicals and reagents used in the experiments were of analytical grade.Trolox, 2,2′-azinobis(3-ethylbenothiazoline-6-sulfonic acid) diammonium salt (ABTS), Folin–Ciocalteu’s phenol reagent, 2,4,6-tri(2-pyridyl)-Fifty-six selected Chinese medicinal plants used in this investigation were obtained from a famous vendor of traditional Chinese medicines, Beijing Tong-Ren-Tang drug retail outlet in Guangzhou of China.3.2.The dry plant sample was ground to fine powder in a mill, and 0.5 g of powder of each sample was treated with 10 mL of methanol-water  in a shaking water bath at 35 °C for 24 h as described . The mix3.3.et al. [•+, 7 mmol/L ABTS salt and 2.45 mmol/L potassium persulfate were mixed in a volume ratio of 1:1, the reaction mixture was allowed to stand in the dark for 16 h at room temperature and was used within two days of preparation. The ABTS•+ radical solution was diluted with ethanol to an absorbance of 0.7 ± 0.05 at 734 nm. All samples were diluted approximately to provide 20–80% inhibition of the blank absorbance. One hundred microliters of the diluted sample was mixed with 3.8 mL ABTS•+ working solution, and the reaction mixture was left at room temperature to react for 6 min, and then the absorbance at 734 nm was taken using the ultraviolet spectrophotometer [The TEAC assay was carried out according to the method of Re et al. . Firstlyotometer . Trolox 3.4.4 solution, and the results were expressed as μmol Fe(II)/g dry weight of herbal material.Measurement of ferric reducing antioxidant power of the herbal extract was carried out based on the procedure of Benzie and Strain . Firstly3.5.Total phenolic content was determined with the Folin-Ciocalteu reagent according to a procedure described by Singleton and Rossi . Briefly3.6.p value less than 0.05 was considered to be statistically significant.All the experiments were carried out in triplicate, and the results were expressed as mean ± SD (standard deviation). Statistical analysis was performed using SPSS 13.0 and Excel 2003. The 4.Dioscorea bulbifera, Eriobotrya japonica, Tussilago farfara and Ephedra sinica are the most potent, since they exhibited the highest scavenging activity against ABTS•+ radical, outstanding reducing power and plenteous phenolic contents among 56 studied plants. Besides their strong antioxidant activities, their low toxicities, wide distributions and medicinal functions [The antioxidant capacities and total phenolic contents of 56 selected medicinal plants were evaluated, and the potential antioxidant activities of many plants were assessed for the first time. Positive correlations between antioxidant capacity and total phenolic content suggest that the antioxidant activities of the medicinal plants can be mainly ascribed to their phenol compounds. A strong correlation between TEAC value and FRAP value implies that antioxidants in these plants possess free radical scavenging activity and oxidant reducing power. The results indicate that unctions  all make