Apart from cis-eQTLs that are typically easily identifiable using microarrays or RNA-sequencing, DeepSAGE also revealed many cis-eQTLs for antisense and other non-coding transcripts, often in genomic regions containing retrotransposon-derived elements. We also identified and confirmed SNPs that affect the usage of alternative polyadenylation sites, thereby potentially influencing the stability of messenger RNAs (mRNA). We then combined the power of RNA-sequencing with DeepSAGE by performing a meta-analysis of three datasets, leading to the identification of many more cis-eQTLs. Our results indicate that DeepSAGE data is useful for eQTL mapping of known and unknown transcripts, and for identifying SNPs that affect alternative polyadenylation. Because of the inherent differences between DeepSAGE and RNA-sequencing, our complementary, integrative approach leads to greater insight into the molecular consequences of many disease-associated variants.Many disease-associated variants affect gene expression levels  and expression profiling using next generation sequencing (NGS) technology is a powerful way to detect these eQTLs. We analyzed 94 total blood samples from healthy volunteers with DeepSAGE to gain specific insight into how genetic variants affect the expression of genes and lengths of 3′-untranslated regions (3′-UTRs). We detected previously unknown  Many genetic variants that are associated with diseases also affect gene expression levels. We used a next generation sequencing approach targeting 3′ transcript ends (DeepSAGE) to gain specific insight into how genetic variants affect the expression of genes and the usage and length of 3′-untranslated regions. We detected many associations for antisense and other non-coding transcripts, often in genomic regions containing retrotransposon-derived elements. Some of these variants are also associated with disease. We also identified and confirmed variants that affect the usage of alternative polyadenylation sites, thereby potentially influencing the stability of mRNAs. We conclude that DeepSAGE is useful for detecting eQTL effects on both known and unknown transcripts, and for identifying variants that affect alternative polyadenylation. Genome-wide association studies (GWAS) have associated genetic variants, such as single nucleotide polymorphisms (SNPs) and copy number variants (CNVs), with numerous diseases and complex traits. However, the mechanisms through which genetic variants affect disease phenotypes or physical traits often remain unclear. To gain insight into these mechanisms, we have combined genotype data with gene expression data by conducting expression quantitative trait locus (eQTL) mapping. Previously, the level of gene expression was primarily assessed using oligonucleotide microarrays, which was a powerful method to profile the transcriptome 3′-CATG sequence of each transcript  from total blood samples from 94 healthy, unrelated individuals from the Netherlands Twin Register (NTR) and the Netherlands Study of Depression and Anxiety (NESDA) HBA1, HBA2, HBB) genes, we were left with sufficient sequencing depth to detect a total of 9,562 genes at a threshold of at least two tags per million.For cis-eQTL mapping, evaluating only those combinations of SNPs and tags that were located within a genomic distance of 250 kb, while using a Spearman rank correlation test  controlled at 0.05). We identified 540 unique cis-regulated tags.Once reads had been mapped, we quantified the expression levels of sequenced tags and performed cis-eQTLs . The list of detected eQTLs is given in To subsequently increase the statistical power of eQTL detection, we used principal component analysis (PCA) to correct for technical and known and unknown biological confounders. The first principal components (PC) generally capture a high percentage of the expression variation, and these PCs mostly reflect technical, physiological and environmental variability. Removing this variation allows for the detection of more eQTLs cis-eQTLs with cis-eQTLs that we had identified using the Affymetrix HG-U219 expression microarrays on the same 94 samples. In that analysis we detected cis-eQTLs for only 274 genes (FDR<0.05), only a third of what we identified using DeepSAGE.We then compared the DeepSAGE cis-eQTLs detected using Affymetrix microarrays nearly always reflected genes that are highly expressed in blood, whereas for DeepSAGE the detected cis-eQTL genes had expression levels that could be much lower  .It was therefore not a surprise that only 39% of the identified DeepSAGE rection) . Indeed,vice versa, we could significantly replicate 75% of the detected Affymetrix cis-eQTLs with the same allelic direction in the DeepSAGE data  to get the same number of significant cis-regulated genes as identified in the microarray analysis of the 94 samples. As such, these results clearly indicate that DeepSAGE has higher statistical power than microarrays.We estimated the reduction that could be made in the sample size of the sequencing-based dataset to get the same number of While most microarray platforms interrogate mainly the protein-coding part of the transcriptome, NGS-based expression profiling will detect the majority of all expressed transcripts. Indeed, we detected eQTLs for known, but non-protein coding, genes: 8 antisense genes and 31 lincRNAs .cis-eQTL effects on previously unknown transcripts. Of the 1,011 tags with a significant cis-eQTL effect, 230 did not map to known transcripts. Many of these tags map to retrotransposon-derived elements in the genome, which are known to be a source of novel exons cis-eQTLs that did not map to annotated genes mapped to 72 unique LINE, SINE and LTR elements in the genome , as published in the Catalog of Published Genome-Wide Association Studies. 104 of the 6,446 unique trait-associated SNPs were significant cis-eQTLs in our data , two tags antisense to the known transcripts, and two tags outside the annotated transcripts. These results indicate that several trait-associated SNPs affect the expression of previously unknown transcripts, adding functional relevance to SNPs and transcripts that are so far without annotation.We identified 13 trait-associated SNPs that did show a significant  dataset . This inPRSS3, we now saw that it also affects the expression of UBE2R2. There are many links between the ubiquitin system and bipolar disorder reported in the literature  contained tags that were regulated in opposite directions  structure and structure in the expression data) and performed the same analysis as above . Using the differential correlation significance threshold of 102≥0.8). We did not find any strong evidence for SNPs influencing the cleavage site and focused on cis-regulating SNPs located within polyadenylation signals. Considering the length and the motif of canonical and non-canonical polyadenylation signals cis-regulating SNP. We identified five SNPs that likely affect polyadenylation because there was a change in the polyadenylation signal  on the LD between the detected cis-eQTLs and the putative causative SNPs; by imputing to the 1000 genomes dataset we may have missed causative SNPs unique to the Dutch population; and the list of experimentally validated polyadenylation sites is not exhaustive, because their detection depends on the expression level and cell type analyzed.Since the eQTL SNPs are usually in strong LD with multiple SNPs, it is difficult to conclude whether a SNP is causal or which SNP is the likely causal variant. To identify the likely causal variant, we assessed whether any of these SNPs caused changes in polyadenylation site usage. A direct effect on alternative polyadenylation can be explained by a change in the polyadenylation site (corresponding to the cleavage site) or in the polyadenylation signal (a six-nucleotide motif located between 10–30 bases upstream of the cleavage site). We searched for likely causative SNPs in linkage disequilibrium with the polyA-QTL SNP .We have described the results from cis-eQTLs was much smaller than in the DeepSAGE data, indicating the higher power of DeepSAGE in eQTL mapping. Moreover, for 220 of the cis-eQTLs SNPs detected by DeepSAGE we did not detect a significant cis-eQTL in a much larger microarray-based study in 1,469 whole peripheral blood samples We performed eQTL mapping on the microarray expression data of the same samples and the number of detected cis-eQTLs detected in microarray data was higher in highly expressed genes, whereas for DeepSAGE the detected cis-eQTL genes had expression levels that could be much lower , detection of such an RNA molecule is direct proof that it is being expressed.We observed that the number of ch lower . This mecis-eQTLs in the microarray data with a consistent allelic direction  while the DeepSAGE study was on total blood, and some cis-eQTLs may be tissue-specific We subsequently conducted a cis-eQTLs, as it is able to quantify the expression of novel transcripts, and to detect alternative polyadenylation effects and alternative 3′-UTR selection. It is complementary to other sequencing-based approaches, as they each reveal slightly different regulatory effects of genetic variants. Different sequencing-based eQTL analyses generally deliver consistent results, allowing for meta-analyses across different technologies. Future eQTL mapping studies based on DeepSAGE and other next generation sequencing strategies, using larger cohorts and different techniques, will likely reveal a more comprehensive picture of how far genetic variation affects the expression of protein-coding genes and non-coding RNAs.We conclude that DeepSAGE technology is useful to determine The medical ethical committee of the VUMC, Amsterdam, the Netherlands, approved the collection and analysis of material blood, DNA and RNA from the 94 participants from the Netherlands Twin Registry (NTR) and the Netherlands Study of Depression and Anxiety (NESDA).We analyzed 21 bp DeepSAGE data from total blood RNA of 94 unrelated individuals who participated in NTR or NESDA. RNA was isolated using PaxGene tubes The NTR-NESDA data was imputed using Beagle v3.1.0, with HapMap2 release 24 as a reference.Tags from DeepSAGE sequencing were aligned to the NCBI build 37 reference genome using Bowtie v. 0.12.7 allowing for a maximum of 1 mismatch and a maximum of 2 possible alignments (-n 1 -k 1 -m 2 –best –strata options).The expression values were both quantified on an individual tag and transcript level. For the tag-wise analysis, the total number of occurrences of each unique tag in each sample was counted. We only included tags that were present in >90% of samples. Tags with SNPs in the CATG recognition sequence (according to dbSNPv135) and the transcripts containing those tags were removed before eQTL analysis, since these SNPs can affect the position of the SAGE tag in the transcript. For the transcript-wise analysis, the tag counts for tags overlapping the exons of a transcript by at least half of the tag length were summed.Coordinates of LINE, SINE, LTR elements were derived from UCSC's RepeatMasker track (update: 2009-04-24).To calculate the GC content per individual for DeepSAGE data, GC frequencies for all mapped tags were summed after excluding the twenty most abundant tags, since their high abundance would give biased estimates.Before eQTL mapping, transcript and tag expression values were quantile normalized. To perform cis-eQTL mapping, association of SNPs with the expression levels of tags or transcripts within a distance of 250 kb (as this is the average size of linkage regions) of the midpoint of the transcript or tag were tested with a non-parametric Spearman's rank correlation. Multiple testing correction was performed, controlling the false discovery rate (FDR) at 0.05. To determine the FDR, we created a null distribution by repeating the cis-eQTL analysis after permuting the sample labels 10 times cis-eQTL effects. To mitigate the effects of non-genetic sources of variability, we first log2 transformed the data and centered and scaled each tag, and subsequently applied PCA on the sample correlation matrix. We then used the first PCs as covariates, and re-did the non-parametric cis-eQTL mapping on the residual expression data  according to the position of the DeepSAGE tags with opposite directions, the position of LongSAGE tags from CGAP, and the position of reported and predicted polyadenylation sites from polyA_DB database. To calculate the coverage in proximal and distal regions in RNA-seq datasets, we created a coverage histogram from each .bam alignment file using coverageBed tool from BEDTools package (version 2.17.0) HPS1 and IRF5 was quantified by qRT-PCR, which was performed on a subset of RNA samples used for the DeepSAGE sequencing. cDNA was synthesized from 400 ng of total RNA using BioScript MMLV Reverse Transcriptase (Bioline) with 40 ng of random hexamer and oligo(dT)18 primers following manufacturer's instructions  for IRF5. Each measurement was performed in duplicates. PCR efficiency was determined using the LinRegPCR program Expression of short and long variants of cis-regulating the choice of polyadenylation site. To do this we used bi-allelic SNPs that pass QC from the 1000G European panel (v3.20101123) and took all SNPs that were in linkage disequilibrium with the query SNPs .We obtained all the proxy SNPs for all SNPs identified as cis-regulating SNPs in linkage disequilibrium, we kept only SNPs, which were located in the cis-regulated genes. The filtering was performed by intersecting .bed files containing SNPs coordinates and coordinates of cis-regulated genes from RefSeq database, using table browser tool in UCSC genome browser and the overlap intervals tool in Galaxy (version 1.0.0).From this list of AATAAA, ATTAAA, TATAAA, AGTAAA, AAGAAA, AATATA, AATACA, CATAAA, GATAA, AATGAA, TTTAAA, ACTAAA, and AATAGA. For every SNP located in a putative polyadenylation signal motif, the distance to validated and predicted polyadenylation sites from PolyA-DB was calculated. Only motifs within a distance of 30 nucleotides from a polyadenylation site were considered true polyadenylation signals. Newly formed polyadenylation signals were detected by changing the reference allele of the SNP with the alternative allele, followed by the same polyadenylation signal motif search using custom Perl scripts.Intersection of SNPs with validated and predicted polyadenylation sites was performed using annotation in the PolyA-DB database (PolyA_DB 1 and PolyA_SVM) on UCSC (table browser tool). Detection of SNPs within polyadenylation signals was performed by extracting the strand specific sequence five nucleotide upstream and downstream each SNP (using table browser tool in UCSC) and performing a motif search using custom Perl script. Canonical and non-canonical polyA motifs searched were cis-regulated genes where the SNP is located within a true polyadenylation signal, we retrieved the coverage of every SAGE tag upstream and downstream the putative affected polyadenylation site and calculated the ratio between proximal and distal tags for the different genotypes to confirm the expected effects of polyadenylation site formation or disruption.For the For the meta-analysis we combined DeepSAGE data with two published RNA-seq datasets. The first dataset was 37 bp paired-end RNA-sequencing data from HapMap individuals  and Argonne (using 46 bp reads).The second dataset was single-end RNA-sequencing data from HapMap individuals et al. paper used genotype data for some individuals that were not in the HapMap3 panel , we imputed these individuals using Beagle v3.1.0, with HapMap2 release 24 as a reference.Since the Montgomery Reads from single- and paired-end RNA-sequencing were mapped to the human genome NCBI build 37 (reference annotation from Ensembl GRCh37.65) using Tophat v. 1.3.3 To estimate expression levels in RNA-seq data, reads that overlapped with exons from known transcripts (GRCh37.65) were quantified using the coverageBed method from BEDTools suite s for transcript tr was calculated as a sum of expression values over all exons contained in this transcript:For transcript level quantification the read count tr,e by not less than half of read's length,e (% of exon length covered by the reads mapping to that exon).In case a read mapped to multiple transcripts, the read was counted for all transcripts, since the short reads are difficult to assign to a specific transcript. Multiplication by breadth of coverage was performed to help in distinguishing between different isoforms by assigning higher weight to exons fully covered by reads in contrast to alternative exons covered only partly.Because different methods have different capacity to identify alternative splicing events, we subsequently summarized our eQTL results to unique genes.Meta-analysis was conducted by using a weighted Z-method, weighing each of the datasets by the square root of the number of samples per dataset http://www.rucdr.org). NTR and NESDA samples were randomly assigned to plates with seven plates containing subjects from both studies to better inform array QC and study comparability. Gene expression data were required to pass standard Affymetrix QC metrics (Affymetrix expression console) before further analysis. Probe sets were removed when their mapping location was ambiguous or if their location intersected a polymorphic SNP  and 60 HapMap CEU individuals provided by Montgomery et al. .For RNA-seq data we used corresponding microarray datasets that were available for most of the individuals present in RNA-seq datasets. We used Illumina expression data provided by Stranger The same normalization procedure was performed as for the sequencing-based datasets: quantile normalization, and subsequent probe set centering to zero, z-score transformation, and scaling to a standard deviation of one.The newly generated DeepSAGE data for NTR-NESDA dataset is available in ArrayExpress under accession number E-MTAB-1181 (ENA: ERP001544).Figure S1Correlation of GC content with principal component 1 (PC1) eigenvector coefficients for all the three datasets. Pearson correlation coefficient and corresponding p-value are shown in the plot.(EPS)Click here for additional data file.Figure S29/L. Pearson correlation coefficients and corresponding p-values are shown in the plot.Blood cell counts in DeepSAGE data captured by the eigenvector coefficients on principal components PC7 (left) and PC11 (right). Experimentally determined blood cell counts at the time of RNA isolation were available for 36/94 samples. Blood cell counts are expressed as (number of cells)×10(EPS)Click here for additional data file.Figure S3cis-regulated genes is given after each filtering step.Replication of Affymetrix eQTLs in DeepSAGE dataset and DeepSAGE eQTLs in Affymetrix data. The numbers of unique (EPS)Click here for additional data file.Table S1List of detected eQTLs in tag-wise eQTL mapping.(XLS)Click here for additional data file.Table S2Trait-associated SNPs affecting the expression of DeepSAGE tags of 94 peripheral blood samples.(XLS)Click here for additional data file.Table S3List of candidate genes with alternative polyadenylation event detected using a permissive strategy.(XLS)Click here for additional data file.Table S4Replications between RNA-seq and DeepSAGE eQTLs.(XLS)Click here for additional data file.Table S5Replication of RNA-seq eQTLs in microarray-based datasets.(XLS)Click here for additional data file.Table S6List of detected eQTLs in the meta-analysis.(XLS)Click here for additional data file.Table S7Primer sequences for qPCR validation.(XLS)Click here for additional data file.Text S1Additional details on principal component analysis of DeepSAGE expression data.(DOC)Click here for additional data file.


In order to supply new information on the antioxidant function of selected beverages for nutritionists and the general public, total phenolic contents of 51 kinds of herbal and tea infusions made in China were measured by the Folin-Ciocalteu method, and their antioxidant capacities were evaluated using ferric reducing antioxidant power (FRAP) and Trolox equivalent antioxidant capacity (TEAC) assays. A significant correlation between FRAP and TEAC values suggested that antioxidant components in these beverages were capable of reducing oxidants and scavenging free radicals. The high correlation between antioxidant capacities and total phenolic contents indicated that phenolic compounds could be one of the main components responsible for antioxidant activities of these beverages. Generally, these beverages had high antioxidant capacities and total phenolic contents, and could be important dietary sources of antioxidant phenolics for prevention of diseases caused by oxidative stress. DietaA special kind of herbal infusion is called cool tea (Liang cha in Chinese), which originated from South China. The cool tea is made from some kinds of herbs, and has been drunk as a beverage for hundreds of years. The vendition of cool tea has been from South China to the whole of China, and from China to about 20 countries around the world, such as the United States of America, Canada, United Kingdom, France and Germany. The cool tea has the efficacies of clearing away heat, detoxification, dewetting, moistening lung and stopping thirsty. Similarly, tea has been widely drunk in China for thousands of years. Tea consumption is associated with reduced risks of cardiovascular disease and cancer, and health effects of tea come from its high content of phytochemicals with antioxidant activity . TraditiThe aim of this study was to systematically evaluate total phenolic contents and antioxidant capacities of 51 kinds of herbal and tea infusions made in China, to investigate the relationship between antioxidant capacity and total phenolic content, and to supply new information on the antioxidant function of these beverages for nutritionists and the general public.2.2.1.Fifty-one kinds of commercial herbal and tea infusions were obtained from markets in Guangzhou, which represent main categories of the infusions made in China .The total phenolic contents of 51 infusions were estimated using the Folin–Ciocalteu method, which relies on the transfer of electrons from phenolic compounds to the Folin–Ciocalteu reagent in alkaline medium, and is a simple and rapid method –13. As sFor the herbal infusions, the total phenolic contents varied from 0.032 ±0.001 to 1.395 ±0.068 g GAE/L with the difference of 44-fold, and the mean value was 0.410 g GAE/L for the 28 herbal infusions . Ping AnFor the tea infusions, the total phenolic contents varied from 0.253 ± 0.005 to 0.867 ± 0.015 g GAE/L with the difference of 3-fold, and the mean value was 0.565 g GAE/L for the 23 tea infusions . Kang Shp > 0.05), which indicated that these infusions could contribute the same health benefit as those wines in terms of polyphenols. Taking the negative health effect of alcohol in those wines into account, these infusions would have better health benefits for people [When the total phenolic contents of these infusions were compared with those of Serbian white wines and Korean wines reported in the literature ,14, therr people .2.2.The ferric reducing antioxidant power (FRAP) assay was used to evaluate antioxidant capacities of the 51 infusions. The FRAP assay is based on the capacity of antioxidants to reduce ferric(III) ions to ferrous(II) ions ,17, whicFor the herbal infusions, the FRAP values varied from 0.392 ± 0.014 to 30.581 ± 1.285 mol Fe(II)/L with the difference of 78-fold, and the mean value was 6.897 mol Fe(II)/L for 28 herbal infusions . Ping AnFor the tea infusions, the FRAP values varied from 6.454 ±0.280 to 22.724 ±0.758 mol Fe(II)/L with the difference of about 4-fold, and the mean value was 11.979 mol Fe(II)/L for 23 tea infusions . Kang ShR2 = 0.7929), which indicated that phenolic compounds could be one of the main components responsible for antioxidant activities of these beverages.The correlation between antioxidant capacities and the total phenolic content of the 51 infusions is shown in 2.3.•+ radicals. It can measure antioxidant capacities of lipophilic and hydrophilic components in a sample, and is a method usually used for the evaluation of antioxidant capacity [The antioxidant capacities of samples may be influenced by lots of factors, such as test system, and cannot be fully described by one single method. Most natural antioxidants are multifunctional. A reliable antioxidant evaluation protocol requires different antioxidant activity assessments to be performed to take into account various mechanisms of antioxidant action . Therefocapacity . The TEAFor the herbal infusions, the TEAC values varied from 0.250 ± 0.006 to 19.296 ± 0.692 mol Trolox/L with the difference of 77-fold, and the mean value was 3.664 mol Trolox/L for the 28 herbal infusions . Ping AnFor the tea infusions, the TEAC values varied from 3.815 ±0.087 to 14.020 ±0.324 mol Fe(II)/L with the difference of about 4-fold, and the mean value was 6.791 mol Trolox/L for the 23 tea infusions . Kang ShR2 = 0.8043), which indicated that phenolic compounds could be one of the main components responsible for antioxidant activities of these infusions. In addition, the correlation between total antioxidant capacities obtained from FRAP and TEAC assays are shown in R2 = 0.865) between them, which suggested that antioxidant components in these infusions could reduce oxidants (such as ferric ions) and scavenge free radicals. This result is in agreement with those of medicinal plants and wild fruits [The correlation between antioxidant capacities and the total phenolic content of the 51 infusions is shown in d fruits ,24. Maybd fruits .Seven herbal infusions and six tea infusions had the strongest antioxidant activities among the 51 infusions based on a combinative consideration of the results obtained by FRAP and TEAC assays as well as the Folin-Ciocalteu method. They are Ping An Tang li yan cha, Ping An Tang shi gan cha, Qing Xin Tang jiang huo wang, Qing Xin Tang er shi si wei, Qing Xin Tang hou zheng tang, Qing Xin Tang zhi ke hua tan tang and Qing Xin Tang gan mao cha as well as Kang Shi Fu muo li mi cha, Kang Shi Fu muo li qing cha, Kang Shi Fu lü cha, Kang Shi Fu wu long ming cha, Kang Shi Fu tie guan ying cha and Tong Yi cha li wang. The main polyphenolic components in these infusions have been identified according to the method reported in the literature , and areFor total phenolic content, FRAP value and TEAC value, the differences between herbal infusions and tea infusions, between bitter herbal infusions and sweet herbal infusions as well as between green tea infusions and black tea infusions were significant, but the difference between herbal infusions produced by Ping An Tang and those by Qing Xin Tang was not significant . In addi3.3.1.Gallic acid, 6-hydroxy-2,5,7,8-tetramethylchromane-2-carboxylic acid (Trolox), Folin–Ciocalteu’s phenol reagent, 2,4,6-Tri(2-pyridyl)-s-triazine (TPTZ) and 2,2’-azinobis(3-ethylbenothiazoline-6-sulfonic acid) diammonium salt (ABTS) were obtained from Sigma–Aldrich . Sodium carbonate, potassium persulphate, Iron (III) chloride 6-hydrate, iron (II) sulfate 7-hydrate, acetic acid and sodium acetate were purchased from Tianjing Chemical Factory . Hydrochloric acid, ethanol and methanol were obtained from Kelong Chemical Factory . All chemicals used in the experiments were of analytical grade, and deionized water was used.3.2.Twenty-eight kinds of herbal infusions and twenty-three kinds of tea infusions were bought from local markets , which a3.3.Total phenolic content of the infusion was determined according to the literature ,28. Brie3.4.The FRAP assay of the infusion was carried out according to the procedure described in the literature ,17. Brie3.5.•+ stock solution was prepared from 7 mM ABTS and 2.45 mM potassium persulfate in a volume ratio of 1:1, and then incubated in the dark for 16 h at room temperature, which should be used within 2 days. The ABTS+ working solution was prepared by diluting the stock solution with ethanol to an absorbance of 0.70 ±0.05 at 734 nm. All infusions were aptly diluted to provide 20–80% inhibition of the blank absorbance. One hundred microliters of the diluted infusion was mixed with 3.8 mL ABTS+ working solution. After 6 min of incubation at room temperature, the absorbance of the mixture was measured at 734 nm, and the percent of inhibition of absorbance was calculated. Trolox solution was used as a reference standard, and the results were expressed as mol Trolox/L of infusion.The TEAC assay of the infusion was carried out according to the method established in the literature . 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.The total phenolic contents and antioxidant capacities of 51 kinds of herbal and tea infusions made in China were evaluated. A high correlation between antioxidant capacity and total phenolic content indicated that phenolic compounds could be one of the main components responsible for antioxidant activities of these beverages. A significant correlation between the FRAP value and the TEAC value suggested that antioxidant components in these beverages were capable of reducing oxidants and scavenging free radicals. Generally, these beverages had high total phenolic contents and antioxidant capacities. These beverages could be important dietary sources of antioxidant phenolics for prevention of diseases caused by oxidative stress. This study supplied new information on the antioxidant function of these beverages for consumers, nutritionists and food policy makers. In the future, health effects of these beverages for the consumers should be explored by the epidemiologic method.


Restriction Enzyme-based Reduced Representation Library (RRL) method represents a relatively feasible and flexible strategy used for Single Nucleotide Polymorphism (SNP) identification in different species. It has remarkable advantage of reducing the complexity of the genome by orders of magnitude. However, comprehensive evaluation for actual efficacy of SNP identification by this method is still unavailable.Tsp 45I enzyme which covers 266 Mb flanking region of the enzyme recognition site according to in silico simulation on human reference genome, then we sequenced YH RRL after Tsp 45I treatment and obtained reads of which 80.8% were mapped to target region with an 20-fold average coverage, about 96.8% of target region was covered by at least one read and 257 K SNPs were identified in the region using SOAPsnp software.In order to evaluate the efficacy of Restriction Enzyme-based RRL method, we selected Compared with whole genome resequencing data, we observed false discovery rate (FDR) of 13.95% and false negative rate (FNR) of 25.90%. The concordance rate of homozygote loci was over 99.8%, but that of heterozygote were only 92.56%. Repeat sequences and bases quality were proved to have a great effect on the accuracy of SNP calling, SNPs in recognition sites contributed evidently to the high FNR and the low concordance rate of heterozygote. Our results indicated that repeat masking and high stringent filter criteria could significantly decrease both FDR and FNR.This study demonstrates that Restriction Enzyme-based RRL method was effective for SNP identification. The results highlight the important role of bias and the method-derived defects represented in this method and emphasize the special attentions noteworthy. SNPs are the most abundant markers across the genome. Their relatively uniform distribution and high density make them ideal markers in genome wide association studies (GWAS), comparative or evolutionary genomics study and marker-assisted molecular breeding research. Due to the outcome of HapMap Project ,2 and thSeveral genome-wide genotyping technologies have been developed and commercialized, aiming at detecting common SNPs or tagSNPs in parallel   was performed with nine commercially available and methylation-insensitive restriction enzymes. These enzymes were selected upon following criterions: (1) predicted fragments length ranged from 200 to 700 base pairs (bp); (2) proportion of target region overlapped with the repetitive elements; (3) number of putative SNPs in dbSNP database v129 (ftp://ftp.ncbi.nih.gov/snp/organisms/human_9606/) covered by target region. The repetitive elements were determined using RepeatMasker software v3.2.7 (http://hgdownload.cse.ucsc.edu/) [Tsp 45I RRL was prepared following the main workflow shown in Figure Tsp 45I in 100 μL reaction mixer at 65°C for 1 hour (New England Bio Labs). The digested DNA was separated on a 2% agarose gel and the fragments in the range of 200 bp to 700 bp were excised from gel and purified using QIAquick Gel Extraction Kit (Qiagen). The sticky ends of fragments generated by Tsp 45I digestion were polished with T4 DNA polymerase and Klenow polymerase, after 'A' base tailing using Klenow exo-(3' to 5' exo minus). Then PCR-free adaptors with a single 'T' base overhang at the 3' end were ligated to the above products. The concentration of the libraries was determined by Q-PCR. The libraries were performed 90-cycles paired-end multiplex sequencing on Illumina HiSeq 2000 platform (Illumina).hg18) using SOAPaligner v2 (http://soap.genomics.org.cn/) with at most 4 mismatches and at least 40 perfectly matched bases.The raw sequencing data were masked from adapter using our own software application Mlinker. Parameters were set up for Mlinker as following: (1) overlapped length between read and adapter was at least 5 bp; (2) minimum of 90% sequence identity between read and adapter; (3) length of reads after adapter-masked was at least 35 bases. The clean reads were mapped to the human reference  to evaluate our SNPs calling results. Novel SNPs identified in our results were defined as false discovery loci, and missing SNPs in our results were defined as false negative. The FDR and FNR indicated the accuracy of SNP calling. To evaluate the genotyping accuracy, we calculated the concordance of these SNPs genotyped by RRL method and Illumina 1 M Duo_v3.B BeadArray.2 ≥ 0.8), MAF (mean/median), and spacing (mean/median) between markers. Allele frequency and linkage disequilibrium (LD) data for the four HapMap populations were obtained from HapMap database . The value of genomic coverage was calculated according to the method previously described [R. The sets of SNPs covered by Illumina genotyping Beadchip or RRL were represented by T. The sets of SNPs located in same LD blocks with T sets of SNPs with r2 ≥ 0.8 were represented by L. Then the value of genome coverage was calculated as (L+T)/R. Human 1 M Duo_v3.B Beadchip from Illumina which cover about 1.1 M SNPs was selected to perform the comparison.To compare with commercially available genotyping platform, we set some parameters including genomic coverage  with a degeneration base in the middle, in principle could call around 1 M putative SNPs. Since we planned to evaluate the SNP calling efficiency by comparing our RRL method and 1 M Duo_v3.B chip, we selected Tsp 45I to obtain RRL. Tsp 45I RRL produced approximately 1.5 million fragments ranging from 200 bp to 700 bp, paired end sequences from the sequenced fragments covered 266 Mb region (8.64% of the whole genome) flanking enzyme recognition sites.In this study we screened nine restriction enzymes with human genome Tsp 45I RRL construction was depicted in Figure Tsp 45I entirely and separated by agarose gel electrophoresis  were used. To evaluate the accuracy of SNP calling, we compared the identified SNPs to YH SNPs data generated by WGS  and dbSNTsp 45I RRL should be as low as 2.3% and 6.67%, respectively.To figure out the main causes, false discovery and false negative loci were selected and calculated for their identification or missing. From Table in silico digestion and SNP calling using hg18 as reference, some bases in the enzyme recognition sites may be candidate SNPs in YH genome. If these loci were homozygous which were different from the reference, the corresponding target region could be excluded from the final RRL and the corresponding loci were accordingly undetectable, so that it may contribute to FNR ; CHB: the HapMap data on individuals of Chinese ancestry ; JPT: the HapMap data on individuals of Japanese ancestry ; YRI: the HapMap data on individuals of Yoruba ancestry .Click here for fileTsp 45I RRL and Illumina 1 M BeadchipMAF distribution of putative SNPs in . The MAF distribution of putative SNPs in Tsp 45I RRL was coincided with the curve of HapMap release24 data, but the distribution of SNPs on Illumina 1 M Chip was distinctly biased toward common SNPs.Click here for file


P < 0.001) on LD decay. There was significant interaction between breeds and chromosomes (P < 0.001). Correlations of LD phase were high between C and AN (0.84), C and CH (0.81), as well as between AN and CH (0.77) for distances less than or equal to 70 kb. These dropped when the distance increased. Estimated effective population sizes for AN and CH were 207 and 285, respectively, for 10 generations ago. Given a useful LD of at least 0.3 between pairs of SNPs, the LD phase between any pair of the three breed groups was highly persistent. The current SNP density would allow the capture of approximately 49% of useful LD between SNP and marker QTL in AN, and 38% in CH. A higher density SNP panel or redesign of the current panel is needed to achieve more of useful LD for the purpose of genomic selection beef cattle.Linkage disequilibrium (LD) and the persistence of its phase across populations are important for genomic selection as well as fine scale mapping of quantitative trait loci (QTL). However, knowledge of LD in beef cattle, as well as the persistence of LD phase between crossbreds (C) and purebreds, is limited. The objective of this study was to understand the patterns of LD in Angus (AN), Charolais (CH), and C beef cattle based on 31,073, 32,088, and 33,286 SNP in each population, respectively. Amount of LD decreased rapidly from 0.29 to 0.23 to 0.19 in AN, 0.22 to 0.16 to 0.12 in CH, 0.21 to 0.15 to 0.11 in C, when the distance range between markers changed from 0–30 kb to 30–70 kb and then to 70–100 kb, respectively. Breeds and chromosomes had significant effects ( Linkage disequilibrium (LD) refers to non-random association of alleles at two or more loci, and is important in fine scale mapping of quantitative trait loci (QTL) , which is defined by Wright  of selection candidates based on their marker genotypes  being the main focus  was supplied by primarily AI companies or local breeders. These test animals comprised 11 animals of 50% AN and 50% (Piedmontese) PI, 231 of 50–75% AN, 41 of 75–87.5%AN, 141 of 50–75% SM, 7 of at least 75% SM, 28 of at least 50% CH, and 157 of other breed combinations. Average breed composition of the crossbred animals genotyped for this study is presented in Figure P < 0.01), and/or had low call rate (<95%) were removed from further analysis. The work of Du et al.  less than 10% and came from a small sample size. Since the numbers of animals in the three populations used in this study are considered larger sample sizes, SNP with 5% MAF or above were included in analyses. After screening, 31,073, 32,088, and 33,286 SNP on 29 autosomal chromosomes were used for AN, CH, and C, respectively. SNP position from the UMD 3.1 bovine assembly was used in this study.The AN and CH animals were genotyped for 54,609 single nucleotide polymorphisms (SNP), using the Illumina Bovine SNP50_v2 Beadchip; and the crossbreds were genotyped for 51,620 SNP across the bovine genome, using the Illumina Bovine SNP50_v1 Beadchip. The genotyping was accomplished on blood samples at the University of Alberta. SNPs that were out of Hardy–Weinberg equilibrium  between the observed and the expected haplotype frequencies. D = f(AB) – f(A)f(B), where f(AB) is the estimated frequency of haplotype AB using the observed genotype frequency  and f(B) being the frequencies of alleles A and B, respectively. However, D is highly dependent on allele frequencies and therefore undesirable for comparing LD among multiple pairs of loci. Hill and Roberson (1968) developed r2 as a measure of LD, f(A), f(a), f(B) and f(b) are observed frequencies of alleles A, a, B, and b, respectively. The metric r2 varies between 0 and 1, where zero means that the SNPs are completely uncorrelated while 1 means the two SNPs are perfectly correlated. The r2 metric represents the correlation of determination for alleles at 2 loci, and is proven less dependent on allele frequencies in finite population sizes as compared to other LD measures  was estimated on each chromosome. More details of this technique are described by Sargolzaei et al. . The age of Ne for any distance is estimated by A linear model was set out to determine the effects of marker intervals, chromosomes, and breed groups on the decay of LD, LDBos taurus autosomal chromosome (BTA) being 42.72 Mb (BTA25) and the longest 158.09Mb (BTA1). The distribution of SNPs varied among the chromosomes, with BTA1 having the highest number of SNPs (2026–2176) and BTA28 having the fewest (580–607); the longest SNP interval was identified on BTA20 (38.77 Mb). However, average SNP intervals were relatively consistent among the chromosomes, and the overall average distance between two adjacent SNPs was 70 kb.Table r2 was computed for each interval. Figure Table r2 for SNPs separated by intervals ≤30 kb were 0.29, 0.22, and 0.21 for AN, CH and C, respectively  with LD at least 0.3 were 0.23 (27.78%), 0.16 (7.82%), 0.15 (6.71%) for AN, CH, and C respectively. As the distance between SNPs increased, r2 decreased rapidly. The same linear model as performed earlier was used in each distance range, and showed that breed groups had significant influence on the decay of LD.The average The decay of LD was found to be significantly different among chromosomes as well. The rate of LD decay was slower on BTA5 and 13, but more rapid on BTA29 than the average of the whole genome. This association in AN is presented in Table r2 on BTA5 of AN was higher than that of CH while the average LD on BTA29 and its decay were similar in both breeds, especially at genomic distance greater than 1 Mb.In terms of interaction between breeds and chromosomes, the rate of LD decay was delayed the most on BTA5 as compared to other chromosomes. Yet this was observed in AN only and is presented in Figure Pearson correlation coefficient for LD phase between same pairs of SNP, for pairs of breeds was obtained and presented in Table Ne in both AN and CH. At almost all times the effective population size of CH was higher than that of AN and the reduction rate in Ne was consistent until approximately 250 generations ago, when it began to accelerate and became even more rapid in the past 100 generations. This may suggest a bottleneck has occurred in both breeds, plus the use of AI in the more recent past leading to the Ne of approximately 207 and 285 for AN and CH, respectively, 10 generations ago. Compared to the estimates by Villa-Angulo et al.  of SNP pairs separated by 70 kb or less showed r2 of at least 0.15. Subsequently if a QTL is located halfway between two SNPs of a pair then the LD amount between that QTL and each of the two SNP would be twice the correlation between the SNPs, and thus might be at least 0.3. If the r2 threshold of 0.3 is applicable to useful LD in beef cattle, and required for genomic selection to achieve a high accuracy for genomic BV . This accuracy could be improved by redesigning the SNP chip based on LD blocks, or increasing marker density in regions of low LD, or regions of interest. To illustrate this, further analysis  showed that the amount of useful LD between SNP and QTL in AN, CH, and C could be increased to 54 and 43%, respectively, given a panel of 84,500 usable SNPs. This improvement would be substantially enhanced with the availability of bovine half million SNP panels as SNP intervals could be narrowed down to approximately 215 b for any two adjacent SNPs. Two such panels, Illumina BovineHD BeadChip  for any pairwise comparison of the three breed groups, given that LD was at least 0.3. This is interesting because if a QTL is in linkage with its surrounding SNPs, and its effect is estimated in a training population then used to predict trait performance in a different population, the direction of the effect is highly preserved if the correlation of r for the two populations is high as discovered in this study. Since LD over a short genomic distance is considered historical LD, the current data reveals a close genetic relationship between AN and CH over short genomic distance, and indicates these two breeds came from the same population thousands of generations ago. To illustrate this, the correlation of r for the two breeds was plotted over past generations in Figure r suggests that these two breeds were genetically close to each other thousands of generations ago. This is supported by a common belief that Bos primigenius was the last common ancestor of domestic cattle , where N is the total number of animals alive at anytime, L being the generation length, σ2n the variance of family size. Using this relationship the shrinkage of Ne depends on the number of sires and the variance of progeny number per sire. Mukai et al. (Ne 10 generations ago for both AN and CH in the current study were well above the recommended numbers. This could be attributed to a sufficiently large number of sires being used to produce animals in the current dataset, and thus a small variance of family size. However, the slope of the Ne in Figure Ne.Hill  proposedi et al.  and Nomui et al.  found thi et al.  suggestei et al.  recommenr indicates AN and CH came from one common population thousands of generations ago; their genetic divergence started approximately 1500 generations ago and accelerated over the past 250 generations. The estimated Ne for AN and CH 10 generations ago were 207 and 285, respectively, and sufficiently large to maintain fitness and maximize responses to selection for economic traits. The study also reveals a redesign of the current SNP chip or an increase in SNP density is necessary to exploit more useful LD for genome-wide selection in a population consisting of these breeds.The amount of LD decayed rapidly as SNP pair distance increased within 200 kb, but the LD over longer distances remained consistently low. For a given genomic distance, populations ranked as AN, CH, C animals for level of LD. The phase of LD was more persistent between AN and the C animals than between AN and CH, as well as CH and the C. This persistence was very high between any pair of the three breed groups for SNP pairs with LD as large or larger than 0.3. The behavior of the correlation of The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


As described in other articles in this Special Section, microarrays are widely used to evaluate gene expression at the genome scale. However, all too often the importance of data analysis at the level of the individual probe is overlooked. This is a particular problem when trying to detect differences in gene expression levels among genetically unique animals, across inbred animal strains, or among genetically modified animals. Of particular concern is the presence of small modifications in the DNA  that occur naturally and differentiate one individual from the next. This article describes the potential impact of SNPs on analyses of gene expression differences and introduces an approach called SNP masking, which implements removal of SNP-affected probes. SNP masking is a valuable and feasible approach that can ameliorate these hybridization problems.Recent research projects to determine the exact sequence of the mouse genome and other research efforts have identified millions of SNPs in the mouse genome. In all, more than 10 million SNPs now are known to exist among mouse strains. Even for the two strains most commonly used for genetic analyses , approximately 4 million known SNPs exist. Atp1a2, 61 SNPs were identified that affected many probes within multiple probe sets for the gene, causing multiple false-positive results (see Using two different analytic methods, the researchers estimated that these experiments could yield false-positive results in 36 percent and 22 percent of cases, respectively, and false-negative results in 13 percent and 12 percent of cases, respectively . These fults see .Researchers over the years have identified millions of SNPs in the mouse genome alone, and there are many more as-yet-unidentified SNPs that also could affect gene expression probes in hybridization-based assays. Even for the well-studied B6 versus D2 strains, it is estimated that there are many more SNPs than have been described so far. Therefore, it is of utmost importance that investigators performing microarray analyses identify false-positives and false-negatives before carrying out costly followup confirmation experiments on their samples. With the promise of being able to sequence an entire genome for only $1,000 on the horizon, determination of the complete genome sequence of many mouse strains in the near future will provide additional momentum to the field, allowing for complete SNP masking of genetic differences among strains.


Apostichopus japonicus, a sea cucumber species with economic significance for the aquaculture industry in East Asian countries. HRM analysis revealed that all the loci showed polymorphisms when evaluated using 40 A. japonicus individuals collected from a natural population. The minor allele frequency ranged from 0.035 to 0.489. The observed and expected heterozygosities ranged from 0.050 to 0.833 and 0.073 to 0.907, respectively. Thirteen loci were found to depart significantly from Hardy–Weinberg equilibrium (HWE) after Bonferroni corrections. Significant linkage disequilibrium (LD) was detected in one pair of markers. These SNP markers are expected to be useful for future quantitative trait loci (QTL) analysis, and to facilitate marker-assisted selection (MAS) in A. japonicus.Single nucleotide polymorphisms (SNPs) are currently the marker of choice in a variety of genetic studies. Using the high resolution melting (HRM) genotyping approach, 101 gene-based SNP markers were developed for  Apostichopus japonicus (Selenka 1867), naturally distributes along the coasts of China, Japan, Korea and Russia [A. japonicus has become widespread along the coasts of China, due to increasing market demand and over-exploitation of wild sea cucumbers [A. japonicus aquaculture. Recently, marker-assisted selection (MAS) has become a valuable tool for selecting individuals with traits of interest [The sea cucumber  Selenka 867, natuucumbers . Howeverucumbers . In ordeinterest . To perfA. japonicus [Single nucleotide polymorphisms (SNPs) have been shown to be the most abundant type of genetic variations in eukaryotic genomes , and areaponicus –7. MoreoA. japonicus transcriptome [A. japonicus individuals collected from four natural populations. Genetic parameters of the validated SNP markers were evaluated using 40 A. japonicus individuals from a single natural population. These SNP markers will be useful for future QTL analysis in order to facilitate MAS in A. japonicus.Previously, our group has released a large amount of EST data by 454 sequencing of the criptome . By miniA. japonicus transcriptome generated by 454-FLX sequencing [Transcriptomic sequences represent an important resource for rapid and cost-effective development of gene-based SNPs. For the high resolution melting (HRM)-based SNP marker development, we designed PCR primers for 200 candidate SNPs , which wquencing . After PA. japonicus individuals from a single natural population. As expected, all 101 SNP loci were polymorphic. The minor allele frequency ranged from 0.035 to 0.489  from Hardy–Weinberg equilibrium (HWE) after Bonferroni correction, suggesting that these loci may be under ongoing natural selection. Significant linkage disequilibrium (LD) was detected in one pair of SNP markers (ApjSNP092_CT and ApjSNP098_CT).Genetic parameters of the validated SNP markers were further evaluated using 40 to 0.489 . The Ho As the gene-derived SNPs reside in or are immediately next to protein-coding sequences, they stand a better chance for identifying functional genes that are responsible for complex traits as well as simply inherited traits ,9. In ouA. japonicus individuals used for SNP marker validation were collected from four natural populations  in China. Genetic parameters of the validated SNP markers were further evaluated using 40 A. japonicus individuals from the Rongcheng  population. Genomic DNA was extracted from the muscles of sea cucumbers by following the protocol developed by Zhan et al. [A total of 48 n et al. . The quaA. japonicus collected from four natural populations were used for SNP marker validation. PCR amplifications were carried out in a 10 μL reaction mixture containing 20 ng of genomic DNA, 1× PCR buffer, 0.2 mM dNTPs, 1.5 mM MgCl2, 0.5 U Taq DNA polymerase , 0.1 μM forward primer, 0.5 μM reverse primer and 1× LCGreen Plus . The amplification was programmed as: an initial denaturation at 95 °C for 5 min, followed by 55 cycles of 95 °C for 40 s, 60 °C for 40 s and 72 °C for 40 s, finishing with a final elongation at 72 °C for 5 min. The PCR products were checked by gel electrophoresis, and those with correct PCR product sizes were then subjected to probe testing. An aliquot of the appropriate probe was added in each reaction to a final concentration of 5 μM. The PCR product and probe mixture were denatured at 95 °C for 15 min and then slowly cooled to 4 °C. HRM genotyping was immediately performed on a Light Scanner instrument  with continuous melting curve acquisition (10 acquisitions per °C) during a 0.1 °C/s ramp from 40 to 95 °C.Our group has recently released a large amount of transcriptomic data by 454 sequencing of eight cDNA libraries constructed using more than 200 sea cucumber individuals. Potential SNPs were detected from the assembled contigs using the program GS Reference Mapper  with default parameters (cDNA mode). More than 54,000 putative SNPs were identified from the dataset, 200 of which were selected in this study for marker development with the selection criteria of at least 3× occurrence of the minority allele and at least 6× contigs coverage (number of reads forming the contig). SNP genotyping was performed using a recently developed cost-effective HRM method . For eacHe) and observed (Ho) heterozygosities, and tests for deviation from Hardy-Weinberg equilibrium (HWE) and linkage disequilibrium (LD).Data were retrieved and analyzed using the Light Scanner software followed by manual curation of the obtained genotype calls. POPGENE  was usedA. japonicus. These developed markers are expected to be useful for future QTL analysis, and to facilitate MAS in A. japonicus.In summary, 101 gene-based SNPs were successfully developed from the transcriptome sequences of


Drosophila melanogaster, using ∼2.5 million SNPs determined by sequencing the Drosophila Genetic Reference Panel population of inbred lines. We constructed a genomic relationship matrix from the SNP data and used it in a genomic best linear unbiased prediction (GBLUP) model. We assessed predictive ability as the correlation between predicted genetic values and observed phenotypes by cross-validation, and found a predictive ability of 0.239±0.008 (0.230±0.012) for starvation resistance (startle response). The predictive ability of BayesB, a Bayesian method with internal SNP selection, was not greater than GBLUP. Selection of the 5% SNPs with either the highest absolute effect or variance explained did not improve predictive ability. Predictive ability decreased only when fewer than 150,000 SNPs were used to construct the genomic relationship matrix. We hypothesize that predictive power in this population stems from the SNP–based modeling of the subtle relationship structure caused by long-range linkage disequilibrium and not from population structure or SNPs in linkage disequilibrium with causal variants. We discuss the implications of these results for genomic prediction in other organisms.Predicting organismal phenotypes from genotype data is important for plant and animal breeding, medicine, and evolutionary biology. Genomic-based phenotype prediction has been applied for single-nucleotide polymorphism (SNP) genotyping platforms, but not using complete genome sequences. Here, we report genomic prediction for starvation stress resistance and startle response in  The ability to accurately predict values of complex phenotypes from genotype data will revolutionize plant and animal breeding, personalized medicine, and evolutionary biology. To date, genomic prediction has utilized high-density single-nucleotide polymorphism (SNP) genotyping arrays, but the availability of sequence data opens new frontiers for genomic prediction methods. This article is the first application of genomic phenotype prediction using whole-genome sequence data in a substantial sample of a higher eukaryote. We use ∼2.5 million SNPs with minor allele frequency greater than 2.5% derived from genomic sequences of the “Drosophila Genetic Reference Panel” to predict phenotypes for two traits, starvation resistance and startle-induced locomotor behavior. We systematically address prediction within versus across sexes, genomic best linear unbiased prediction (GBLUP) versus a Bayesian approach, and the effect of SNP density. We find that (i) genomic prediction can be efficiently implemented using sequence data via GBLUP, (ii) there is little gain in predictive ability if the number of SNPs is increased above 150,000, and (iii) neither implicit nor explicit marker selection substantially improves the predictive ability. Although the findings must be seen against the background of small sample sizes, the results illustrate both the potential of the approach and the challenges ahead. Most efforts to understand the genetic architecture of quantitative traits have focused on mapping the variants causing phenotypic variation in quantitative trait locus (QTL) mapping populations derived from crosses between lines genetically divergent for the trait, or in association mapping populations, with the goal of understanding the biological underpinnings of trait variation i.e. the proportion of shared alleles of identical ancestral origin) given the pedigree. The advent of high-throughput genotyping platforms for many agronomic species In classical animal and plant breeding, the genetic quality of individuals or lines is predicted from phenotypic values of selection candidates and their relatives. The widely used Best Linear Unbiased Prediction BLUP,  method mGenome based-prediction follows a different paradigm than genome wide association studies (GWAS). GWAS identify single molecular variants associated with phenotypic variability using individual statistical tests for significance of each variant. Genome-based prediction uses the entire genomic variability captured by the available marker set to explain the observed phenotypic variation, and does not rely on selection of single loci based on significance tests. Standard prediction methods are thought to work for traits with a highly polygenic or even infinitesimal With the advent of next generation sequencing technologies, it is now feasible to implement genomic prediction based on complete genome sequences of higher organisms. While these techniques have only been applied to individuals or cohorts of limited size The accuracy of prediction methods based on marker data depends on the heritability of the trait, its genetic architecture  which are assumed to be drawn from the same normal distribution and uncorrelated. The model primarily provides estimates of SNP effects, but estimated genetic values of individuals can be derived as linear combinations of the estimated SNP effects, yielding the same predictions of individual genotypic or phenotypic values as GBLUP. The BayesB method It has been suggested Drosophila melanogaster inbred lines. The inbred lines have been sequenced, and constitute the Drosophila Genetics Reference Panel (DGRP), a new community resource for genetic studies of complex traits Implementing genomic prediction with full genome sequence data raises a number of questions. What is the most efficient way to incorporate the complete genomic information in prediction? How much predictive ability is gained by using whole genome sequence data compared to high density SNP panels? Is it possible to increase predictive ability by a pre-selection of SNPs or models with an internal feature selection? How comparable are the results of genomic prediction and genome wide association? Here, we address these questions empirically based on full genomic sequences of a population of vs. across sexes, various SNP densities, and training set sizes. We assessed whether BayesB is superior over GBLUP given full genome sequence data We report the results of a full sequence based genomic prediction for two quantitative traits, starvation stress resistance and locomotor startle response, both of which display considerable genetic variation in natural populations and respond rapidly to artificial selection To our knowledge, this is the first application of genomic prediction on empirical whole genome sequence in a substantial sample of a higher organism. However, this study, as well as all previous association studies, only assesses the effects of common SNPs, since the effects of rare alleles cannot be estimated due to the small sample of sequenced lines. The results illustrate both the potential of the approach and challenges to be addressed in the future.We constructed a genomic relationship matrix analyses  and a coanalyses  show thaanalyses , as indis  does not affect the predictive ability for startle response, but substantially reduces the predictive ability for starvation resistance, reflecting a higher degree of genotype by sex interaction in this trait (D. melanogaster genome. The average LD between SNPs (after imputation) whose distance lay in the interval X-chromosome. Long-range LD between pairs of loci at the opposite ends of chromosome arms or across different chromosome arms was on average X-chromosome.A series of p matrix . The prerandomly chose For starvation resistance, the influence of the minor allele frequency of the SNPs used on the predictive ability was assessed with a series of 5-fold CVs using SNP sets with different average minor allele frequency. We find that the variability of the predictive ability increases when the average minor allele frequency of the SNPs used to construct the genomic relationship matrix is decreased . In  reps used , . RunningTo analyze whether the predictive ability is due to lines which are more highly related, we ran an additional 5-fold CV with atedness  were excThe accuracy of genomic prediction is a function of a number of quantities, including the size of the training set and the effective population size obtained .We estimated The effective population size in the Raleigh population (from which the DGRP-lines were drawn) was estimated to be ∼19,000 in We can use the curves fitted through the empirical accuracies , to pred2L . These values correspond to an estimated effective population size of We also estimated the effective population size based on LD directly. For a distance bin of Genomic prediction might be improved if we only fit SNPs which are associated with variance in a trait, because we then concentrate on the biologically relevant genomic regions, and excluding SNPs which are not associated with the trait reduces statistical noise. We tested this hypothesis using the starvation resistance data. We identified the We also compared our GBLUP results to those from a method which does not assume that all SNP effects are drawn from the same normal distribution and carries out an internal feature selection. We ran nd GBLUP . The avee.g. the genome-wide largest SNP effects on chromosome 3L for starvation resistance and 2L for startle response), while concordance is poor in other regions, especially on the X chromosome.Although genomic prediction follows a different paradigm than genome-wide association studies, it is informative to compare significant SNP positions from the GWAS to areas of large estimated SNP effects resulting from the GBLUP model. Previously AS study . There iWe further investigated whether the most significant SNPs detected in the GWAS are reflected by large SNP effects in the GBLUP study using a different approach. For each significant SNP position from the GWAS we took the sistance .significant GWAS positions, The density resulting from the sliding window approach reflects the overall distribution of the suggested statistic in the sample. For starvation resistance (startle response) a threshold value of among DGRP lines is the total genetic variance, and is possibly inflated by additive by additive epistatic variance individual flies to determine the nature of the total genetic variance, especially to what extent the presence of non-additive genetic variance might have affected predictive abilities. We fitted three different models to the individual phenotype data: Model 1 contained a random line effect, and lines were assumed to be unrelated. In Model 2, a random additive line effect In addition to the line means we also analyzed individual records  application of genomic prediction to a real set of full genomic sequencing data in a eukaryotic organism. Although predictive abilities obtained with starvation resistance and startle behavior are only moderate to low, and although we limited our analysis to SNPs that are common due to the small sample size of lines, this study can be seen as a proof of concept for this approach. There are several reasons for the limited predictive ability obtained in this study. First, the training set is small, with a maximum of The second important factor affecting accuracy of prediction is the number of independently segregating chromosome segments, D. melanogaster genome, few SNPs are in useful LD with any causal polymorphism. Even if we define “useful LD” very conservatively as X chromosome). This means that on average X-linked) polymorphism, as the average distance between neighboring SNPs was X chromosome). If predictive ability was mainly driven by SNPs in LD with causal polymorphisms, reducing the SNP density should lead to a massive decay of predictive ability of the models, which was not observed. Little decrease in accuracy was seen, even if every Accuracy of genomic prediction is thought to come from two sources: (i) SNPs in useful LD with causal loci; and (ii) SNPs reflecting the relationship structure between the training set and the set to be predicted The number of SNPs for maximal accuracy of genomic prediction with unrelated individuals has been estimated as For starvation resistance, we find that the empirical accuracy levels off when approximately every D. melanogaster genome and the robustness of the accuracy of genomic prediction with reduced marker density, we conclude that the observed accuracy of prediction for starvation resistance and startle response is primarily due to the long-range LD in the population, or equivalently, the subtle relationship structure as reflected by the genomic relationship matrix.Since pedigree information for the founders of the inbred lines was not available, our estimates of heritability and genomic prediction are based on the actual degree of identity-by-descent sharing between relatives We restricted our analyses to SNPs for which the minor allele was present in at least four DGRP lines  genetic background The finding that BayesB did not outperform GBLUP in the present study is consistent with a quasi-infinitesimal genetic architecture; and results indicate that starvation resistance and startle response are complex traits with a highly polygenic genetic architecture rather than being driven by a few major causal genes. This is in agreement with previous studies stating that starvation resistance and startle response can be considered to be model traits with a complex  regression models were used to predict starvation resistance and startle response phenotypes from genotypic data The imperfect concordance of the positions of the most significant SNPs from the GWAS and the largest estimates of SNP effects from RRBLUP is a consequence of the different objectives of the two approaches. A sequence-based GWAS is conducted to identify causal polymorphisms and provide estimates of allelic effects and frequencies. Also, the GWAS suffers from estimating one effect at a time and so does not necessarily position the QTL accurately. The goal of RRBLUP is to predict the phenotype using all available SNP information simultaneously. Here, estimated SNP effects are a by-product and mapping causal variants is not the primary objective. Given that the number of SNP effects to estimate is much larger than the number of observations, effects are estimated using penalized multiple regression approaches, shrinking estimated effect sizes towards zero. In addition, the magnitude of estimated SNP effects from RRBLUP is a function of the marker density. The higher the marker density, the more SNPs will be in LD with a causal mutation; therefore, the true allele substitution effect of a causal polymorphism will be split up and assigned in parts to a series of SNPs in the respective haplotype block. This can mask both the effect size, because one large effect may come in many small pieces; and the mapping position, because any SNP in LD with the causal polymorphism may have a substantial estimated effect. Nevertheless, some of the largest SNP effects from RRBLUP are in the proximity of prominent SNPs identified in the GWAS, so that to some extent positional information can still be retrieved from the RRBLUP results.A methodology combining the strengths of both approaches – unbiased effect estimates and high positional resolution of GWAS with the simultaneous analysis of all SNPs, high predictive power and quality control via CV of genomic approaches – still needs to be developed. Results obtained in our study cannot be directly compared to predictive abilities in human studies due to the extremely small training set size  2L, 2R, 3L, 3R and X chromosome. We did not consider the few SNPs on the very short chromosome 4. In total there were SNPs were called from the raw sequence data as described previously i.e.Phenotypic measurements for starvation resistance were available for all or males . MeasureWe used different cross-validation (CV) procedures We used (4∶1)-, (3∶2)-, (2∶3)- and (1∶4)-CVs to analyze the effect of decreasing training set size. The CVs also differed in the constellations of phenotypic records used for the training and validation set. For example, the notation “(4∶1) male – female” indicates that only the medians of male records were used in the training set, and that the predicted genetic values were correlated with the medians of female records of the validation set to obtain the predictive ability in a (4∶1)-CV. CVs were also run for different marker densities, using every atedness  from theWe also analyzed the influence of minor allele frequency on the predictive ability by another series of 5-fold CV. For this, we sorted all SNPs by their minor allele frequency and divided the sorted vector into Predictive ability was measured in terms of correlation between predicted genetic values and observed phenotypic values. The corresponding accuracy i.e. the average of the medians of the phenotypic measurements for males and females for this line. Moreover, The underlying statistical model ishttp://CRAN.R-project.org/package=RandomFields), and its function “fitvario”. The BLUP approach to obtain the vector of genetic values is equivalent to solving the so-called Mixed Model Equations (MME):Variance components were estimated via maximum likelihood (ML) using the R-package “RandomFields”, Version 2.0.46 (i.e.The GBLUP model (1) is equivalent to the following linear model in which all SNPs are assumed to have an effect drawn from the same normal distribution We used D. melanogaster):We modified the formula 2L , We ran CVs with different numbers of lines , which is implemented in C++. BayesB is computationally very intensive. The analyses were run on a Mac Pro 2× 2.93 GHz 6-Core Intel Xeon with 64 GB RAM running Mac OS X Server 10.6.7. One fold of a In our studies, we used A genome-wide association study (GWAS) revealed We compared genomic regions for which GBLUP estimated large SNP effects to these significant SNP positions of the GWAS. To avoid an effect of different sample sizes, we chose the individual trait records. The first model included a fixed sex effect, a random line effect, a random line-sex-interaction term and a random term accounting for the different replicates in which measurements of the traits were taken:For each trait, we fitted three different models using The broad-sense heritability for Model 1 was calculated ashttp://CRAN.R-project.org/package=ff), was used to handle the large amount of SNP data efficiently in terms of memory capacity.Unless stated otherwise, all statistical analyses were performed using R software Figure S1i.e. each bin contained Predictive ability of 5-fold CV with GBLUP for starvation resistance using different set of SNPs with different average minor allele frequencies. Each boxplot shows the average predictive abilities for (PDF)Click here for additional data file.Figure S2Manhattan plot of the estimated SNP effects for starvation resistance for different chromosomes. The SNP effects were estimated using the GBLUP approach and sex-averaged phenotypic values of (PDF)Click here for additional data file.Figure S3Manhattan plot of the estimated SNP effects for startle response for different chromosomes. The SNP effects were estimated using the GBLUP approach and sex-averaged phenotypic values of (PDF)Click here for additional data file.Table S1Mean and standard deviation of phenotypic values and of the number of individual records per line.(PDF)Click here for additional data file.Table S2Variance components and heritabilities estimated from GBLUP using all lines. Variance components were estimated by maximum likelihood using the R-package “RandomFields” and its function “fitvario.”(PDF)Click here for additional data file.Table S3Results of variance component estimation using ASReml for starvation resistance. Different linear models for individual trait records were investigated.(PDF)Click here for additional data file.Table S4Results of variance component estimation using ASReml for startle response. Different linear models for individual trait records were investigated.(PDF)Click here for additional data file.Text S1D. melanogaster. We also derive the expected value of the genomic relationship matrix We give more details on the formula of (PDF)Click here for additional data file.


Ribes nigrum L.) using transcriptome-based 2GS 454 sequencing on the parental genotypes of a reference mapping population, to generate large numbers of novel markers for the construction of a high-density linkage map.Deep-level second generation sequencing (2GS) technologies are now being applied to non-model species as a viable and favourable alternative to Sanger sequencing. Large-scale SNP discovery was undertaken in blackcurrant (Ribes germplasm, including mapping populations and other selected Ribes species.Over 700,000 reads were produced, from which a total of 7,000 SNPs were found. A subset of polymorphic SNPs was selected to develop a 384-SNP OPA assay using the Illumina BeadXpress platform. Additionally, the data enabled identification of 3,000 novel EST-SSRs. The selected SNPs and SSRs were validated across diverse SNP-based maps were developed from two blackcurrant mapping populations, incorporating 48% and 27% of assayed SNPs respectively. A relatively high proportion of visually monomorphic SNPs were investigated further by quantitative trait mapping of theta score outputs from BeadStudio analysis, and this enabled additional SNPs to be placed on the two maps.The use of 2GS technology for the development of markers is superior to previously described methods, in both numbers of markers and biological informativeness of those markers. Whilst the numbers of reads and assembled contigs were comparable to similar sized studies of other non-model species, here a high proportion of novel genes were discovered across a wide range of putative function and localisation. The potential utility of markers developed using the 2GS approach in downstream breeding applications is discussed. In many species the main limitation to understanding and characterising important traits is the lack of sufficient genetic markers for the development of high-density genetic maps and association studies. Large numbers of markers, such as Simple Sequence Repeats (SSRs) and Single Nucleotide Polymorphisms (SNPs), are required to assist in identifying genes that underlie genetic variation. For many crop and horticultural species, genetic linkage maps have now been developed and Quantitative Trait Loci (QTL) have been assigned to large chromosomal regions, but so far candidate genes have been identified for only a few of these . The neeDeep-level 2GS technologies are now being applied to non-model species as a viable and favourable alternative to Sanger sequencing, despite the absence of a reference genomic sequence on which to map the short reads. Expressed Sequence Tags (ESTs), derived from the RNA-based transcriptome, have been extremely useful resources to assist marker development  and, by Ribes nigrum L.) is taxonomically isolated within the Saxifragaceae and current genomics resources are extremely limited. As with many economically important woody perennial species, breeding of Ribes is a long-term process due to the highly heterozygous germplasm available and the long generation time, so there is an obvious incentive to develop marker-assisted breeding strategies to reduce the timescale for selection of superior genotypes. Previously, we have constructed cDNA libraries from developing fruit and buds, and Sanger-sequenced several thousand ESTs [Blackcurrant  and S36  were screened for adaptor sequence contamination, leaving 225,334 reads (S10) and 482,959 reads (S36), followed by removal of ribosomal matches, leaving 212,104 reads (S10) and 314,189 reads (S36). We found significantly higher levels of rRNA-derived contamination in S36 (35%) compared to S10 (6%), which was believed to be due to processing-related factors, therefore a further run of S36 was necessary to boost filtered read levels from this parent. The mean read length of the final sets were 214 nt (S10) and 230 nt (S36) respectively. These were subsequently assembled de novo, resulting in 33,518 contiguous sequences (contigs) and 12,893 singletons, with a mean contig length of 407 nt . These contigs and singleton sequences were annotated with descriptors of their closest homologues by running BLASTX searches against the non-redundant protein sequences from NCBI and the peptide models for Arabidopsis thaliana from TAIR [-10) was 52% and 64% respectively, reflecting the high level of novel gene identification for Ribes in this study. The BLAST hits resulting from the search against the Arabidopsis peptides were also processed further by extracting Gene Ontology (GO) terms for each hit using the GO annotation provided by TAIR  or both parents  were heterozygous, and only a small proportion (202) was found where both parents were homozygous. There were only 120 cases where all the reads in the contig originated from the same parent, and these were not considered for further use in this study. As well as SNPs, many of the EST sequences contained repeat motifs. Using Sputnik software [A set of 7,245 high-confidence (p > 0.9) software , 3,179 Stechsupport@illumina.com). As described in the Methods section, the Illumina SNP selection was based on an absence of neighbouring polymorphisms, repetitive elements or palindromes, which are known to have an adverse effect on success of assays.The 384 SNP assay was designed using Illumina technical support . Of these, 75 were heterozygous in the seed parent only, 63 were heterozygous in the pollen parent only and 51 were heterozygous in both parents. Inspection of segregation ratios of the individual markers showed four lines in the population with unexpected genotypes for many SNPs, and these were excluded from subsequent analysis. A cluster analysis of the remaining progeny based on the markers that were heterozygous for the seed parent only showed no particular groupings, but a cluster analysis based on the markers heterozygous for the pollen parent showed a distinct cluster of 46 offspring, none of which had inherited any of the alleles specific to the pollen parent. A chi-squared test was used to compare the segregation ratio of these 46 offspring with the remaining 261 offspring for the markers heterozygous for the seed parent. This found that the segregation ratios were significantly different (p < 0.001) for 72 of the 75 markers, with a segregation ratio close to 1:2:1 for these 46 offspring, but 1:1 for the remaining offspring. These results are consistent with these 46 offspring being selfs and these were excluded from the linkage analysis.In the MP7 population, 118 of the 384 SNPs were found to segregate using the BeadStudio software. Of these, 50 were heterozygous in cv. Ben Finlay (seed parent) only, 35 were heterozygous in cv. Hedda (pollen parent) only and 33 were heterozygous in both parents. A cluster analysis of the MP7 population showed three lines in the population with unexpected genotypes for many SNPs and these were excluded from subsequent analysis. Cluster analysis showed no evidence for any selfing or other grouping of individuals within this population.Both SNP and SSR markers were used in the linkage analysis. No markers were isolated from this population: all were linked with a lod of at least 11 to one or more other markers. Two linkage groups formed at a lod score of three, but the remaining markers only separated at a higher lod, between 7 and 16. This gave ten linkage groups, of which two were small, while the remaining groups had 14-46 markers. The markers within each linkage group were ordered together, rather than separating the markers from the two parents as is sometimes necessary for this type of cross. The fit of the linkage map was, in the authors' experience, unusually good for an outbreeding species. Only five markers were omitted as causing problems with the fit, and JoinMap's mean chi-squared criterion for the resulting maps was below 2.5 for each of the eight large linkage groups. Figure In this population, six SNP markers were excluded as having highly distorted ratios p < 0.001). Five markers were isolated at a lod of 4. The remaining markers formed 9 linkage groups using a lod threshold between 5 and 7. There were two small groups, of two and three markers, and seven larger ones of 8-21 markers. Two markers were excluded as causing problems with the fit. The remaining fits were good, again with all mean chi-squared criteria below 2.5. Figure . Five maWhere there are pairs of SNPs in common between the corresponding linkage groups, the recombination frequencies can be tested for heterogeneity using a chi-squared test implemented in JoinMap 3. A total of 360 pairs of SNPs were examined. Of these, there was no significant heterogeneity (p > 0.05) for 339 pairs, while 15 pairs had significance between 0.05 and 0.01, i.e. a similar number to that expected by chance. Six pairs showed more significant heterogeneity, two pairs on LG7 both involving CL113Contig1_641 were significant with p < 0.005, while four pairs on LG5, all involving CL754Contig1_758, were significant with p < 0.001. Heterogeneity of recombination frequencies is therefore not a widespread problem between these two crosses.Inspection of the 384 SNP theta scores for the SCRI 9328 population showed that 15 SNPs had more than 100 missing values. These were excluded from further analysis, leaving 369 SNPs with at most 15 missing values. The range was also examined: the ideal SNP will have a range of one, i.e. a theta score of one for the BB genotype and zero for the AA genotype. SNPs with a range less than 0.05 were excluded from the QTL analysis, leaving a total of 310 SNPs for which the theta scores were mapped. These consisted of 184 SNPs that were mapped as clear bi-allelic markers, five SNPs that segregated as bi-allelic markers but were excluded from the linkage map and 121 SNPs that were considered as non-segregating by BeadStudio.All 184 SNPs that could be mapped as markers mapped to the same location when their theta scores were used for QTL mapping. Regression of the theta values on the most significant marker explained 71-99% of the variance in the theta values, with a lower quartile of 97%. The five SNP markers that were dropped from the linkage analysis due to their poor fits to the linkage group all mapped to the same groups when the theta scores were analysed as QTL, with regression on the closest marker explaining 90-99% of the variance of the theta score. Two of these markers were heterozygous in both parents, and mapped to a region on LG2 with some segregation distortion. The other three were heterozygous in one parent but, when mapped as QTL, showed associations to the alleles from the other parent.The 121 remaining SNPs, when mapped as QTL, showed marker associations with the maximum percentage variance explained ranging from 0.7% (i.e. no significant association) to 99%. Thirty-one of the SNPs had a maximum percentage variance of at least 70%, comparable to the SNPs that were also mapped as markers. Significance thresholds for the presence of QTL were established by means of a permutation test , using 1In this population, 251 SNPs had theta scores with a range greater than or equal to 0.05 and at most 10 missing values. One hundred and eighteen of these were scored as markers, with 105 placed on the linkage map. Of the 133 remaining SNPs, 36 mapped as QTL with more than 50% of the variance explained and these are shown in Figure Ribes nigrum cultivars and 5 related species. The number of polymorphic SNPs was similar to that observed in the original mapping population (207 SNPs cf. 190 SNPs). Diversity values for each SNP, measured using Nei's unbiased expected heterozygosity, ranged from 0.030 to the maximum value of 0.500, with an overall mean value of 0.307  and redcurrant (R. rubrum L). Of the 40 SSR primers designed, 36 amplified in all genotypes tested and of the 10 SSRs which were subsequently fluorescently labeled and visualised using the ABI 3730, 6 were mapped in the segregating population  for all loci, suggesting the presence of null alleles in the wild germplasm.A subsample of 40 SSRs representing different motif types and repeat numbers were tested using the SCRI 9328 mapping parents and a range of blackcurrant germplasm and related species, gooseberry  [Pinus contorta (32%) [In terms of read numbers and assembled contigs and singletons, our results were similar to those generated in other 454 transcriptome studies of non-model species ,8,15,24.ta (32%)  reflect de novo assembled blackcurrant EST data. As well as the development of this approach for SNP discovery, we addressed the question of validation and whether de novo SNP discovery based upon 2GS data alone can translate into SNP detection assays and, more importantly, useful markers. We designed a multiplex high-throughput SNP detection assay based on the Illumina BeadXpress platform and examined polymorphism across 384 SNPs using two segregating populations and a diverse set of germplasm. Although all SNPs were chosen to be polymorphic from read alignments, we were unable to confirm almost half of putative SNPs from the current assembly by a linkage mapping approach as they did not segregate clearly in the mapping populations. There may be technical reasons why some SNPs do not perform as well as others: Close et al. [de novo assembly and SNP identification pipelines have recently been developed [Second generation sequencing has been used to identify SNPs in a range of plant species . In thise et al.  describee et al. ,12,26-28eveloped ,30.Using the available analysis software (Illumina BeadStudio v3.1), we were able to map 184 SNPs (48% of assayed SNPs) and 105 SNPs (27% of assayed SNPs) from two blackcurrant mapping populations, SCRI 9328 and MP7 respectively. Although these levels appear relatively low, considering both parents of 9328 were used in the SNP discovery pipeline, other studies which have used mapping parents in the same manner  found similar numbers of SNPs placed on the genetic maps in maize (63%) . There wRibes species. Over half of the SNPs were polymorphic with a mean MAF of 0.253, similar to that observed in chicken (0.280) and pigs (0.274) using SNPs from reduced representation libraries [et al. [E of 0.292 for Ribes compared to HE of 0.350 for chicken [The 384 SNP assay was also used to genotype a set of diverse blackcurrant accessions, including breeding lines, and related cultivated and wild ibraries ,11. Mammd pigs 0.4 using S chicken ) and, asE 0.152 to 0.825) were comparable with others  [2 = 0.777, data not shown), corroborating the robustness of these markers for a range of applications.As well as SNPs, several studies have used similar approaches to mine for SSRs, for a range of applications including mapping, systematics, population and conservation genetics ,17,32-35o 0.900) , althougo 0.900) . HoweverRibes are restricted to a single-gene pest resistance trait [We have found the use of 2GS technologies for marker development far superior to any previously described methods (supported in ), both ice trait  but, usiLeaf buds were sampled from four-year old blackcurrant plants grown in the field at Invergowrie, Dundee  of both parents of the reference mapping population SCRI 9328 in February 2008, immediately prior to dormancy break, i.e. as the buds began to visibly swell. Buds were flash frozen in liquid nitrogen and stored at -80°C.1 full-sib progeny from a pseudo-testcross [1 full-sib mapping population with 95 progeny, designated MP7, from a cross between blackcurrant cvs. Ben Finlay and Hedda, was used in the downstream validation of markers.The SCRI 9328 population consists of 311 Festcross  made by estcross . In addiRibes germplasm, including 33 breeding lines, 15 commercially available cultivars (Bens) and 5 related wild species  with the addition of RNA isolation aid (Ambion). RNA quality was checked by spectrophotometry and integrity assessed using a Bioanalyzer (Agilent Technologies).Ribes germplasm accessions. Total genomic DNA was extracted using either the method described by Milligan [Young leaf material was harvested from field grown plants of two mapping populations (SCRI 9328 and MP7) and 71 Milligan  or the DRibes parents S10 and S36 were submitted separately to the GenePool Service Facility  for standard transcriptome 454 FLX (Roche) RNA-seq sequencing. cDNA was generated using either SMART (Clontech) or MINT (Evrogen) kits as recommended by the manufacturer. Fragmentation and library preparation were performed as recommended (Roche) prior to running samples. All sequence reads have been submitted to EMBL European Nucleotide Archive (ENA: http://www.ebi.ac.uk/ena/). The reads for each parent were screened for the presence of adapter sequences originating from both the cDNA preparation and the 454 experimental procedures. Adapter contamination was masked using CROSS_MATCH (http://www.phrap.org/phredphrapconsed.html), and then trimmed from the reads using custom perl scripts. The matching quality scores for the reads were also removed. Any reads that had adapter contamination in the middle were discarded as possible chimeric sequences. Following adapter trimming, the sequences were screened for the presence of contaminating ribosomal RNA. A small BLAST database containing ribosomal RNA sequences from a variety of plants was constructed from entries using a keyword search of Genbank. The reads were then searched against this database and any that had a match to a ribosomal RNA sequence with an e-value greater than 1e-10 were discarded.Total RNA from developing buds of http://compbio.dfci.harvard.edu/tgi/software) running on a single CentOS Linux machine with four processors. The assembly parameters used were the same as those 'relaxed' parameters used in the HarvEST assemblies (http://harvest.ucr.edu), namely the CAP3 parameters -p 75 -d 200 -f 250 -h 90. These were sufficiently relaxed so that SNPs would not be separated into different contigs, thereby allowing SNP discovery. During assembly, 19 reads caused slippage error messages from CAP3 and were therefore removed.After adapter and ribosomal sequence trimming, the identifiers of each of the sequences were prefixed with the parental name (S10 or S36), and then all 526,293 sequences were assembled using the tgicl suite  terms for each hit using the annotation file provided by TAIR (ftp://ftp.arabidopsis.org/home/tair/Ontologies/Gene_Ontology/ATH_GO_GOSLIM.txt). The number of occurrences of each GO ID was then recorded, and the GO ID was resolved against the highest order GO categories that were to be visualised (ftp://ftp.arabidopsis.org/home/tair/Ontologies/Gene_Ontology/TAIR_GO_slim_categories.txt).Contigs were annotated with descriptors of their closest homologues using BLAST  to search them against the non-redundant protein sequences from NCBI and against the peptide models for thaliana . The BLAhttp://bioinformatics.bc.edu/marthlab/GigaBayes). GigaBayes detects SNPs and indels in assembly files (ace file format) and, depending on parameter settings, can also output parental genotypes. Both the SNP itself and the parental genotypes are associated with a Bayesian probability value which indicates the degree of confidence in the feature. The parameter settings "--CRL 6 --CAL1 3 --CAL2 3 --PSL 0.9 --QRL 0 --QAL 0 --ploidy diploid --sample multiple" were used to find locations at which both the minor and major alleles are present at least three times per assembled sequence. The minimum read base quality value (--QRL) and minimum aggregate allele quality value (--QAL) flags had to be set to a zero threshold because the assembly software used assigns low base quality scores to the consensus sequence at positions where there is a high degree of variability, such as at SNPs [techsupport@illumina.com) for design of Illumina GoldenGate SNP assays. The Illumina SNP selection is based on an absence of neighbouring polymorphisms (60 bp flanking sequence on each side between SNPs), repetitive elements or palindromes, since these are known to affect the conversion rate of SNPs into working assays [Single nucleotide polymorphisms (SNPs) were discovered in the final assembly using the GigaBayes tool from the laboratory of Gabor Marth at Boston College . Diversity statistics were calculated according to [IS, was calculated using the FSTAT v. 2.9.3 software [SSRs were identified from the assembly using the Sputnik program  and oligrding to  using thrding to . The unbsoftware .The entire genotyping procedure was performed as recommended in the Goldengate Genotyping Assay for VeraCode Manual (Illumina VC-901-1001). All reagents, unless stated otherwise were provided by Illumina. The sample VBP was scanned immediately using default settings in the VeraScan software on the BeadXpress Reader System.Genotypes were scored visually using Illumina BeadStudio data analysis software (v 3.1) package. Each SNP was scored separately and clusters determined automatically or manually into the three expected groups .et al. [Brennan et al.  detectedet al. .Linkage maps of the segregating SNPs and SSRs were estimated for both the reference mapping population SCRI 9328 and also for the second MP7 population separately, using the JoinMap 3 software  and the The Illumina data consists of two intensity values  for each SNP, measuring the intensities of the fluorescent dyes associated with the two alleles of the SNP. After normalisation, the intensities are transformed to a combined SNP intensity R = (X+Y) and an intensity ratio theta = (2/π)*arctan(Y/X) . IndividAll of the 384 SNPs were expected to segregate in population SCRI 9328, but as reported, about half were not identified as segregating by the BeadStudio software. Another approach was to analyse the theta scores as quantitative traits, regarding them as being comprised of genetic information plus measurement error. Each trait was thus analysed by QTL interval mapping using the software MapQTL 5.0 . GenstatJR helped conceive the study and coordinated the molecular work and mapping analysis. PH helped conceive the study, provided advice on the experimental design and molecular biology, and facilitated the 2GS procedures. MB and LC provided bioinformatics support for the 2GS data. CH analysed the mapping data. CB and JAM provided sequencing and genotyping support. RB helped conceive the study and provided appropriate plant material. SG collected plant samples for analysis. LJ performed the molecular work. JR, PH and RB drafted the manuscript, which all authors read and approved.Arabidopsis predicted peptide sequencesFigure S1 - Distribution of GO annotation categories (blue bars) of blackcurrant ESTs based upon closest derived homologies to . These are compared to distribution of GO annotations from the whole Arabidopsis genome (red bars).Click here for file