Each of the human genes or transcriptional units is likely to contain single nucleotide polymorphisms that may give rise to sequence variation between individuals and tissues on the level of RNA. Based on recent studies, differential expression of the two alleles of heterozygous coding single nucleotide polymorphisms (SNPs) may be frequent for human genes. Methods with high accuracy to be used in a high throughput setting are needed for systematic surveys of expressed sequence variation. In this study we evaluated two formats of multiplexed, microarray based minisequencing for quantitative detection of imbalanced expression of SNP alleles. We used a panel of ten SNPs located in five genes known to be expressed in two endothelial cell lines as our model system.2 values > 0.95 for the majority of the regression lines. According to a two sample t-test, we were able to distinguish 1–9% of a minority SNP allele from a homozygous genotype, with larger variation between SNPs than between assay formats. Six of the SNPs, heterozygous in either of the two cell lines, were genotyped in RNA extracted from the endothelial cells. The coefficient of variation between the fluorescent signals from five parallel reactions was similar for cDNA and genomic DNA. The fluorescence signal intensity ratios measured in the cDNA samples were compared to those in genomic DNA to determine the relative expression levels of the two alleles of each SNP. Four of the six SNPs tested displayed a higher than 1.4-fold difference in allelic ratios between cDNA and genomic DNA. The results were verified by allele-specific oligonucleotide hybridisation and minisequencing in a microtiter plate format.The accuracy and sensitivity of quantitative detection of allelic imbalance was assessed for each SNP by constructing regression lines using a dilution series of mixed samples from individuals of different genotype. Accurate quantification of SNP alleles by both assay formats was evidenced for by RWe conclude that microarray based minisequencing is an accurate and accessible tool for multiplexed screening for imbalanced allelic expression in multiple samples and tissues in parallel. Single nucleotide polymorphisms (SNPs) are highly abundant in the human genome, appearing on average at 0.1% of the nucleotide positions -6. ImbalNon-synonymous SNPs in coding regions of genes may be functional by altering an amino acid, which in turn may affect the structure and function of the encoded protein, while synonymous SNPs may have functional consequences by affecting the stability or folding of mRNA transcripts. Intronic SNPs may give rise to alternatively spliced mRNAs, while SNPs in 5'- or 3'-untranslated mRNA regions may affect the stability or processing of the RNA. Moreover, SNPs in non-protein coding regions of genes that affect binding of regulatory factors may cause imbalanced expression of SNP alleles. This form of genetic variation has been suggested as a common cause of both normal and disease-related inter-individual variation in complex phenotypes . ClearlyOwing to the high sequence specificity of nucleotide incorporation by DNA-polymerases, single nucleotide primer extension has proven to allow quantitative determination of SNPs in genomic DNA in several studies and assay formats . A freqWe are currently using microarray based minisequencing for multiplex genotyping of SNPs. Our custom-made microarrays permit the genotyping of up to 100 SNPs in 80 samples per standard microscope slide, either using immobilised minisequencing primers ,18 or usWe used a panel of ten coding SNPs in five genes to choose the optimal microarray based minisequencing strategy for multiplex, quantitative genotyping of SNPs in DNA and RNA samples. The selected SNPs were located in genes shown by reverse transcriptase PCR analysis to be expressed in one or both of two endothelial cells lines, HUVEC  and HAEC  that served as our model cell lines in this study (data not shown). We evaluated two formats of microarray based minisequencing by performing five parallel assays with each method for each sample in the evaluation. The SNPs were analysed in both DNA polarities and the evaluation of the methods was based on the DNA polarity yielding the highest signal-to-noise ratio.In Method I, immobilised minisequencing primers are extended with fluorescently labelled ddNTPs in reactions performed on the microarray surface after annealing of the multiplex PCR products to the primers ,21. In M2), which describes how well the regression line fits the data points, was used to assess the accuracy of quantification of the SNP alleles by Methods I and II. As can be seen in Table 2 values are close to one for most of the SNPs analysed, demonstrating little scatter of the data points around the regression line. For Method I, six of the ten SNPs analysed have R2 values ≥ 0.95, while for Method II the R2 values are ≥ 0.95 for eight of the SNPs. Thus, accurate quantification of SNP alleles is possible by both methods. The slopes of the regression lines vary between the ten SNPs as well as between the two methods  and endothelin receptor type B (EDNRB rs5351) were heterozygous in the HUVEC cell line. These SNPs were genotyped in cDNA produced from total RNA extracted from the cells with the corresponding gDNA as reference samples using both methods. Table Next, the performance of the two methods in quantitative analysis on the RNA level was assessed. The ten SNPs were first genotyped in genomic DNA (gDNA) from the HUVEC and HAEC cells to identify those SNPs that were heterozygous in either or both cell lines. Three SNPs in the low density lipoprotein receptor gene  were heterozygous in the HAEC cell line, and one SNP in each of the genes encoding angiotensin I converting enzyme (ACE rs4331), βTo test that the results on imbalanced allelic expression detected by the multiplexed microarray based methods represents the true biological situation in the cells, we analysed the heterozygous SNPs in five replicate RNA samples prepared from HUVEC or HAEC harvested at different time points from different cell culture flasks. We also analysed the three LDLR SNPs in five replicate reverse transcription reactions from the same RNA sample prepared from HAEC cells. For this analysis we used our first generation solid-phase minisequencing assay for individual SNPs in a microtiter plate format. The concordant cDNA/gDNA ratios from these control experiments from independent cell and RNA samples presented in Table The purpose of our study was to evaluate microarray based minisequencing for multiplexed detection and quantification of imbalanced expression of SNP alleles, as a prelude to further large scale screening for allelic imbalance. We found no significant differences in the performance of our two "in house" methods, minisequencing with primers directly immobilised on the microarrays (Method I) and the It is notable that the largest differences in accuracy and sensitivity were observed between SNPs. Some of the SNP-to-SNP differences are likely due to differences is the accuracy and efficiency of incorporation of the four different fluorescently labelled nucleotide analogues by the DNA polymerase ,26 as weComparison of the relative amounts of the alleles of six SNPs on the RNA (cDNA) level to heterozygote SNPs in genomic DNA revealed four SNPs with imbalanced expression of the two alleles. A three-fold increase in the expression of the T-allele for the SNP rs4331 ACE was the most pronounced difference observed. In our study, 1.4–1.5-fold differences in allelic expression levels were detectable. The sensitivity of detecting a minority allele in our system would allow the distinction between 10-fold reduction in the expression of an allele and monoallelic expression, for example as a result of imprinting. Owing to its potential for high throughput screening of large numbers of samples, we have also performed a preliminary evaluation of the commercial SNPstream genotyping system  that also utilises the "tag-array" primer extension strategy in a semi-automated 384-well microtiter plate format for detection of imbalanced allelic expression . The samIt is also reassuring for future large scale detection of imbalanced allelic expression that the accuracy of our methods seemed to be similar for cDNA and genomic DNA. Analysis of replicate RNA samples from different batches of both cell lines using a microtiter plate format of the minisequencing method evidenced for the biological authenticity of the allelic imbalance detected using minisequencing in the microarray format. The data obtained from independent cell samples also indicate an acceptable reproducibility of RNA extraction, RNA storage and cDNA synthesis. Another important factor besides sample to sample variation that may affect the accuracy of the relative allele quantification is the amount of mRNA subjected to the analysis. At a low copy number of mRNA, the stochastic distribution of the RNA templates may be a major source of variation . The reaA similar minisequencing strategy as the one used for determination of imbalanced expression between SNP alleles can also be used for determination of the relative expression levels of highly homologous genes  and for Here we demonstrated the applicability of two formats of microarray based minisequencing for detecting imbalanced expression of SNP alleles. The accuracy and sensitivity of both systems allow detection of 1.4- to 10-fold differences in the expression levels of the two alleles of heterozygous SNPs. The microarray-based minisequencing systems utilise widely available reagents and equipment, and can thus easily be established "in-house". Moreover, the system is flexible with respect to number of SNPs and samples to be analyzed. Systematic quantitative screening of genetic diversity on the RNA level in multiple individuals and tissues will be a future approach in the elucidation of the molecular mechanisms that regulate gene expression.DNA samples from 30 volunteer donors were genotyped by Methods I and II to identify individuals of different genotypes for the panel of ten SNPs analysed. The SNPs are described in the section "SNPs and primers" below. DNA 10 ng/μl) from one individual was serially diluted 2:1 into DNA (10 ng/μl) from a second individual, to yield a series of DNA samples with different ratios between the SNP alleles. These mixed DNA samples were used for construction of quantification standard curves. Depending on the genotype of each SNP in the two individuals whose DNA was mixed, dilution series of samples with different allelic ranges were obtained for the ten SNPs, as specified in Table 0 ng/μl f2. Cells from the cultures were harvested at 80% confluence according to the manufacturer's instructions. Total RNA was isolated from the cells using the TRIZOL®Reagent  and the RNA samples were stored at -70°C until use. High quality RNA with A260/A280 ratio over 1.9 and intact ribosomal 28S and 18S RNA were used for cDNA synthesis. The RNA samples were treated with 1 U RQ1 RNase-free DNase  per μg RNA. Two to 2.5 μg total RNA was subjected to first strand cDNA synthesis using SuperScript™ II  reagents in a 20 μl volume. DNA was extracted from the cells using GenElute™ Mammalian Genomic DNA Kit  and stored at -20°C until use.Human Umbilical Vein Endothelial Cells (HUVEC) and Human Aortic Endothelial Cells (HAEC)  were grown in Medium 200 with Low Serum Growth Supplement  at 37°C in a humidified atmosphere of 5% CO® Gold DNA polymerase , 1.5 mM MgCl2, and 0.2–0.3 μM of primers in 50 μl of 10 mM Tris-HCl pH 8.3 and 50 mM KCl. The PCR conditions were initial activation of the enzyme at 95°C for 10 min followed by 35 cycles of 95°C for 1 min, 56°C for 1 min and 72°C for 1 min and a final extension at 72°C for 7 min in a Thermal Cycler PTC225 . The amplified fragments were combined and concentrated to 60 μl using Microcon ® YM-30 Centrifugal Filter Devices .The fragments comprising the SNPs were PCR-amplified in individual reactions using 10–15 ng genomic DNA or one tenth of the cDNA products, 0.2 mM dNTPs, 1U AmpliTaq Ten SNPs located in coding regions of genes known to be expressed in HUVEC and HAEC cells were analysed. Information on the SNPs, including dbSNP  ID numbe2-group in their 5'- or 3'-end, respectively. The oligonucleotides were applied in duplicates to the slides at a concentration of 25 μM in 150 mM sodium phosphate pH 8.5 using a ProSys 5510A instrument  equipped with one Stealth Micro Spotting pin  to minimise the variation between spots in different "subarrays". The oligonucleotides were spotted in an "array-of-arrays" configuration that facilitates analysis of 80 individual samples in parallel on each microscope slide [The minisequencing primers or the complementary tag-oligonucleotides were covalently immobilised on CodeLink™ Activated Slides  by the mediation of a NHpe slide . In eachAliquots of 7.5 μl of the concentrated PCR products were analysed in five parallel "subarrays" for each sample, essentially as described previously . The PCRFive parallel reactions with a 4.5 μl aliquot of the concentrated PCR products were analysed for each sample, as described in detail in . Excess ® DNA polymerase  and the minisequencing primer was added. The extension reaction was allowed to proceed for 10 min at 50°C. The extended primers were released with alkali and the amount of incorporated tritium labelled nucleotide was measured.PCR was run with one of the primers biotinylated. The biotinylated PCR products were immobilised in a microtiter plate coated with streptavidin  and the unbiotinylated strand was removed with alkali treatment ,15. The Primers and probes for the TaqMan assays were designed by Applied Biosystems as Assay-by-Design (rs1042719 ADRB2 and rs5925 LDLR) or Assay-on-Demand (rs1433099 LDLR) service. The probes for the two alleles were labelled with the reporter dyes FAM and VIC respectively. The sequences of the primers and probes for the SNPs rs5925 LDLR and rs 1042719 ADRB2 are found in . The priReal time quantitative PCR was run in 25 μl TaqMan Universal PCR Master Mix (Applied Biosystems) with 200 nM of both labelled TaqMan probes, 900 nM PCR-primers and 10 ng genomic DNA or one tenth of the cDNA products. The PCR conditions were initial activation of the enzyme at 95°C for 10 min followed by 60 cycles of 95°C for 15 sec and 60°C for 1 min in a ABI7000 instrument .The signal intensity ratios were calculated based on normalised ΔRn fluorescence values obtained from the assay during the exponential phase of PCR. The ΔRn values were retrieved from cycle 38 for the SNP rs1042719 ADRB2, cycle 42 for the SNP rs5925 LDLR and cycle 43 for the SNP rs1433099 LDLR. Imbalanced expression of the SNP alleles was determined by a t-test as described below.® Express instrument  with the excitation lasers Blue Argon 488 nm, Green HeNe 543.8 nm, Yellow HeNe 594 nm and Red HeNe 632.8 nm with the laser power set to 80% and the photomultiplier tube gain adjusted to obtain equal signal intensities from reaction control spots for all four spectra. The fluorescence signals were extracted using the QuantArray ® analysis 3.1 software . The mean of the fluorescence signals for the duplicate spots was corrected for the average background in each "sub-array" separately. The data was handled and interpreted using the Microsoft ® Excel program.In Methods I and II fluorescence was measured using a ScanArray 2) were assigned by linear regression analysis of the relationship between the signal intensity ratios determined from the minisequencing assay and the known allelic ratios in the mixed samples for the quantification standard curves. Two-sample t-tests with two-tailed significance levels assuming unequal variance were performed to determine the lowest level of detection of a specific allele for the quantification standard curves and to evaluate the imbalanced expression of the two alleles of the SNPs in the cell lines.The genotype for each individual SNP was assigned by calculating a ratio between the fluorescence signals for the two alleles. Coefficients of determination (RUL participated in the design of the study and in RNA and DNA extraction, and performed all the laboratory work involving "in-house" minisequencing methods, performed the statistical calculations and drafted the manuscript. MF cultured the cells, performed RNA and DNA extraction, performed the assays with the reference method, and provided input to the manuscript. AD performed the assays using the SNPstream system. A-CS conceived the study, participated in its design, coordination and in preparation of the manuscript. All authors read and approved the final manuscript.Additional file1 is a pdf-file with information on the SNPs, including dbSNP ID number, nucleotide variation and the sequences of the primers and probes used in the microarray based minisequencing and TaqMan assays respectively.Click here for file


Screening of various gene markers such as single nucleotide polymorphism (SNP) and correlation between these markers and development of multifactorial disease have previously been studied. Here, we propose a susceptible marker-selectable artificial neural network (ANN) for predicting development of allergic disease.To predict development of childhood allergic asthma (CAA) and select susceptible SNPs, we used an ANN with a parameter decreasing method (PDM) to analyze 25 SNPs of 17 genes in 344 Japanese people, and select 10 susceptible SNPs of CAA. The accuracy of the ANN model with 10 SNPs was 97.7% for learning data and 74.4% for evaluation data. Important combinations were determined by effective combination value (ECV) defined in the present paper. Effective 2-SNP or 3-SNP combinations were found to be concentrated among the 10 selected SNPs.ANN can reliably select SNP combinations that are associated with CAA. Thus, the ANN can be used to characterize development of complex diseases caused by multiple factors. This is the first report of automatic selection of SNPs related to development of multifactorial disease from SNP data of more than 300 patients. In recent years, the number of patients suffering from allergic asthma has increased , and allIn the present study, we analyzed associations between SNPs and childhood allergic asthma (CAA), which is more strongly influenced by genetic factors than other types of allergic asthma. We performed this analysis using an artificial neural network (ANN), which is a computer-based algorithm that can be trained to recognize and categorize complex patterns -8. ANNs . Associations between combinations of important SNPs and CAA pathogenesis were investigated. A χ2 test was performed for all 2-SNP and 3-SNP combinations.Here, we propose an ANN model , IL-4Rα (148 G/A), ADRB2 (265 A/G) and C5 (4266 G/A) were 0.0036, 0.0155, 0.0541 and 0.0581, respectively. When CysLT2 (108 C/A), which had the lowest P-value of 25 SNPs, was used for discrimination between case and control as a sole factor, prediction accuracy was 54.4%, and the sensitivity and specificity was 12.8% and 95.9%, respectively, compared with the number of case and control subjects to assess discrimination performance . Thus, we constructed a susceptible marker-selectable ANN model, which can discriminate between cases and controls using the selected susceptible SNPs, and which can include the association between combinations of SNPs and development of CAA.Several reports have suggested linkage between asthma and chromosomes. For example, genes in the 5q31-5q33 region code for Th2-type cytokines   and ADRBWe used a three-layered ANN with input, hidden and output layers Figure . An ANN et al. [N'case/Ncase. In this case, N'case is the number of cases whose genotype pattern is match to control's genotype pattern at least one control (Ncase = 172 subjects). As shown in Figure Ritchie et al.  reportedet al. . If impoIL-4Rα (148 G/A), CysLT2 (2534 A/G) and IL-10 (-571 C/A)) to 17 SNPs  according to the order of Table To investigate the important SNPs, we counted the number of SNPs that remained within the last 10 input variables in 5 trials. The significance order of remaining SNPs was listed, and a score of order ranging from 1 to 10 points was determined, based on the significance order. The remaining SNPs were reordered according to sums of scores, as shown in Table CysLT2 254 A/G andCysLT2 254 A/G andThe results of diagnostic prediction using the 10 SNPs selected by PDM are shown in Figure P-value using the χ2 test. In models using 10 SNPs selected by PDM or the other 15 SNPs, the total number of 2-SNP combinations and 3-SNP combinations (Ncomb) is 90 (10P2) or 210 (15P2), and 360 (10P3/2) or 1365 (15P3/2), respectively. With respect to 2-SNP combination between the SNP of interest and SNP A, P-value was calculated as follows. When patients were limited with certain pattern of another SNP, such as AA major homozygote of SNP A, patient distribution of the SNP of interest was investigated. With respect to 3-SNP combination, between the SNP of interest, and SNP A and B, P-value was calculated as follows. When patients were limited with certain pattern of two other SNPs, such as AA major homozygote of SNP A and BB major homozygote of SNP B, patient distribution of the SNP of interest was investigated.To understand the importance of the 10 SNPs selected, we analyzed combinations of these 10 SNPs. We paid particular attention to SNP combinations associated with CAA, and assessed whether any combinations consisting of SNPs selected by ANN were associated with CAA. The relationships between 2-SNP or 3-SNP combinations and CAA development were examined by calculating P-value of the combination, the usual Bonferroni correction of P-values was first investigated. To select the 2-SNP combination accompanied with minimum false positive, the criterion was P < 0.05/300. Here 300 cases correspond to 25C2. Under this severe condition, there were no significant SNPs. As the same as 2-SNP combination, any significant combination was not obtained on 3-SNP combination under the threshold of P < 0.05/2300. Next, to determine important combination, P-value without Bonferroni correction was used, that is P < 0.05. Results are shown in Table P < 0.05 among total 90 combinations. In the case of 3-SNP, 72 combinations with P < 0.05 were existed in 360 exhaustive combinations. However, combinations possibly include several false positive significant combinations. Therefore, we paid attention to the SNP, of which P-value effectively decreases by combining with genotype or allele of other SNPs. We defined effective combination value (ECV). ECV2 or ECV3 is the ratio of 2 or 3-SNPs P-value to the product of each P-value. ECV is not indicator for avoiding false positives but for evaluation of interaction. For example, in 2 SNP combinations, when patients were limited with certain pattern of another SNP, such as AA major homozygote of SNP A, patient distribution of the SNP X of interest is investigated (P = Pax). If the 2-SNP combination is independent (no interaction) each other, Pax equals multiplication of Pa and Px. ECV<1 means that the 2-SNP combination is not independent and two SNPs have any interaction each other.To evaluate NECV2<1 = 47). Among 13 combinations with P < 0.05 mentioned above, 11 combination also satisfied the same condition . When ECV2<0.5, NECV2<0.5 decreased the number to 27 and 10 of NECV2<1,P = 11 still remained. When ECV2<0.1, NECV2<0.1 became small and it was thought that positive combination may be lost. In the case of 3-SNP combination, only 20% of the total combination satisfied the condition P < 0.05. The combinations of 12% among the total combinations (43 combinations) satisfied ECV3<1. All of these 43 combinations also satisfied the condition P < 0.05. From these results, it was concluded that P < 0.05 is not strict criterion for 3-SNP combination analysis. In the case of 2-SNP combination, ECV2<0.5 was adequate as a selection of effective combination, because 77% of the combination with P < 0.05 still remained. From these consideration, we selected these two evaluation bases  in order to determine effective combinations and the combination with NECV<0.5,P (P < 0.05 and ECV<0.5) was picked up. The combinations were used for the following investigation. The number of combination which satisfies the condition, P < 0.05 and ECV2<0.5 in 2-SNP combination and P < 0.05 and ECV3<0.5 in 3-SNP combination was designated as Nef, the number of effective combination, respectively  for the 10 SNPs selected by PDM, 0.64 (23/36) for combinations between the 10 selected SNPs and the remaining 15 SNPs, and 0.08 (3/36) for combinations of the remaining 15 SNPs (Table Ncom/15P2 shown in Table 15P2) for the 10 SNPs selected by PDM, 0.5 (300/15P2) for combinations between the 10 selected SNPs and the remaining 15 SNPs, and 0.35 (210/15P2) for combinations of the remaining 15 SNPs . This is about 3% of the value multiplied each P-value of 2-SNP combination (0.5060). For patients with genotype GA of IL-4Rα  and genotype CT of C3 (4896 C/T), patient frequency against genotype of C3 (1692 G/A) had a P-value of 0.01784. For C3 (1692 G/A) alone, a P-value of 0.6993 was obtained, which was 40 times greater than the P-value of the 3-SNP combination. Thus the rate of correct identification of effective combinations evaluated by adjusted P-value and ECV selected based on PDM trials was higher than the corresponding randomized rate, implying that the ANN can reliably select SNP combinations that are associated with CAA.In the next step, 3-SNP combinations were analyzed. The effective rate, the random selection rate, and the concentration ratio were calculated as well as the case of 2-SNP combination Table . It was CysLT2 (2534 A/G) and IL-4Rα , among subjects with a CysLT2 (2534 A/G) genotype of AG or GG , there was an important correlation with IL-4Rα  genotype of GG, GA, AA (P = 0.00030). We examined the distributions of important combinations among subjects. A total of 52 CAA subjects and 24 healthy controls had genotype AG or GG at CysLT2 and genotype GG at IL-4Rα (148 G/A)  Figure .IL-10 (-571 C/A), IL-4 (-590 C/T) and C3 (1692 G/A) is a susceptible factor of CAA (P = 0.00426). No association with CAA was found for any of these 3 SNPs alone . Subjects with genotype CA at IL-10 (-571 C/A), genotype CT at IL-4 (-590 C/T)  and genotype GG at C3 (1692 G/A)  were estimated to be at high risk for pathogenesis of CAA. Furthermore, among the subjects with the same genotype pattern, the number of subjects with genotype AA at C3 (1692 G/A) were CAA, 3 and healthy controls, 13, respectively  and TT genotype at C3 (4896C/T) was 4 times the number of controls with that genotype combination  (P = 0.00271). There are no previous reports of association between these genotype combinations and CAA. The combination of IL-4Rα  and IL-4 (-590 C/T) was also associated with CAA (P = 0.00689); association between allergic asthma and this combination has previously been reported [Other remarkable combinations shown in Table reported ,16.IL-4Rα polymorphisms may be associated with enhancement of IL-4Rα function [IL-4, it was reported that the -590T allele increases the strength of the IL-4 promoter compared with the -590C allele [C3-deficient mice challenged with allergen show diminished airway hyperresponsiveness and lung eosinophilia, with dramatic reduction of the number of IL-4-producing cells and attenuation of IgE responses [C3 (4896 C/T) and genotype GG at IL-4Rα (148 G/A) may be associated with CAA, but details of interaction between these polymorphisms combinations and development mechanisms have not been clarified. The present findings indicate that, among subjects with an IL-10 (-571 C/A) genotype of CA and an IL-4 (-590 C/T) genotype of CT, there is important correlation with a C3 (1692 G/A) genotype of GG or AA /R5511827G) combination of function . As concC allele . C3 is aC allele . C3-defi27G combiA Figure .IL-4 (-590 C/T), AG or GG genotype at CysLT2 (2534 A/G) and GG genotype at IL-4Rα (148 G/A) were estimated to be at high risk for pathogenesis of CAA (P = 0.00022). However, 2-SNP interaction between CysLT2 (2534 A/G) and IL-4Rα (148 G/A) (P = 0.00030) markedly affected the 3-SNP interaction.CysLTs, which are produced by inflammatory cells including eosinophils, are mediators of leukotrienes, and have been implicated in the pathogenesis of allergic diseases. Recently, it has been reported that CysLTs can act as autocrine or paracrine mediators to stimulate rapid, nonexocytotic release of IL-4 . These fIn the present study, we examined correlation between CAA and 25 SNPs in 17 genes using an ANN model. We think that there are not a few main effects and interactions which can explain development of multifactorial disease like CAA, because it is thought that interactions of genetic risk factors might be different individually among CAA patients in spite of same disease. So it is very important to select multiple genetic factor models associated with multifactorial disease like CAA with high concentration ratio. We found that 10 of these SNPs are important factors in development of CAA. Important combinations among these 10 SNPs were also extracted. As described above, several of these combinations  for homozygote of the major allele,  for heterozygote, and  for homozygote of the minor allele. Positive and negative diagnoses were also converted into numerical data: 0.9 and 0.1, respectively.Nerror (number of missed points) and Er (sum of squared error) were defined and calculated for learning data and evaluation data as follows:For SNP analysis, we used a three-layered ANN with input, hidden and output layers Figure . For modNerror = Nerror,l + Nerror,e     (3)Y and T represent the predicted value and the teacher value, respectively. Nl and Ne represent the number of subjects as learning and evaluation data, respectively. Nerror is the number of output data with an error of >0.4 between the predicted value and teacher value as shown above. Er is calculated with the square of error as shown above. For ANN learning, the connection weights were initially randomly set from 0 to 1, and were altered using the back propagation methods [Erl. Learning rates of 0.1, 0.2, 0.3, 0.4 and 0.5 were examined. The maximum learning time was 2000 iterations. The best ANN model  was that in which Nerror reached minimum within the maximum learning time. When minimum Nerror was equal to that of other models within the maximum learning time, the model with minimum Er value was selected.where  methods  with leaPrediction accuracy of the constructed model was defined as follows. Threshold was set at 0.5. If the teacher value was 0.9, and the predicted value was greater than 0.5, the prediction was true ; for predicted values lower than 0.5, the prediction was false . If the teacher value was 0.1, and the predicted value was lower than 0.5, the prediction was true ; for predicted values greater than 0.5, the prediction was false .Ac) as follows:We calculated the prediction accuracy (Se) and specificity (Sp) of predicted values were defined as follows:The sensitivity  after the ANN model with 25 SNPs was constructed. In PDM, 1 SNP was excluded from input variables in turn, and ANN models were constructed with the remaining 24 SNPs by performing the cross-validation described below. From among the 25 models thus constructed, the model with minimum Cross-validation allows estimation of the prediction error of a model by leaving out a portion of the data as an evaluation data . In the An LR model was constructed using SPSS 11.5J statistic software for Windows , for comparison with the ANN model. All 25 SNPs were used as input variables of LR. For LR analysis, we used 50 main effects plus an intercept but not any interaction terms. As with the ANN model, the data set was divided into 5 groups and the cross-validation was performed. Prediction accuracy, sensitivity and specificity were calculated.P-value using a χ2 test. The χ2 test was used to evaluate the differences in frequencies of alleles or genotypes between cases and controls. The P-values shown in Table P-value with χ2 test. In order to determine important combinations, we use two evaluation bases ) mentioned in Results section.We also examined association between CAA and combinations of SNPs by calculating P-value using a χ2 test. ST and YH carried out the basic analysis using ANN and data preprocessing. YS and TS participated in providing of SNP data and the design of LR analysis. TK participated in the design of the study. HH conceived of the study, and participated in its design and coordination. All authors read and approved the final manuscript.YT carried out ANN modeling of SNP data including PDM and calculating


Sustainable DNA resources and reliable high-throughput genotyping methods are required for large-scale, long-term genetic association studies. In the genetic dissection of common disease it is now recognised that thousands of samples and hundreds of thousands of markers, mostly single nucleotide polymorphisms (SNPs), will have to be analysed. In order to achieve these aims, both an ability to boost quantities of archived DNA and to genotype at low costs are highly desirable. We have investigated Φ29 polymerase Multiple Displacement Amplification (MDA)-generated DNA product (MDA product), in combination with highly multiplexed BeadArray™ genotyping technology. As part of a large-scale BeadArray genotyping experiment we made a direct comparison of genotyping data generated from MDA product with that from genomic DNA (gDNA) templates.Eighty-six MDA product and the corresponding 86 gDNA samples were genotyped at 345 SNPs and a concordance rate of 98.8% was achieved. The BeadArray sample exclusion rate, blind to sample type, was 10.5% for MDA product compared to 5.8% for gDNA.We conclude that the BeadArray technology successfully produces high quality genotyping data from MDA product. The combination of these technologies improves the feasibility and efficiency of mapping common disease susceptibility genes despite limited stocks of gDNA samples. In order to locate the disease variants involved in complex common disease it is now generally accepted that very large sample numbers will be required -4. Not oAt present, the gold standard method for generating gDNA from whole blood samples is through the process of immortalisation by transformation of the peripheral blood lymphocytes with Epstein-Barr Virus (EBV) . AlthougMolecular Staging, Inc. (MSI)  have developed a method for whole genome amplification by Φ29 polymerase Multiple Displacement Amplification (MDA). It has been reported by the company that this method can reliably amplify the whole genome from gDNA, whole blood and other clinical samples -8. Each Recent studies have been conducted using MDA product from Amersham ,10, and ®  and Invader®  with gDNA.The BeadArray genotyping platform of Illumina™ Inc.  offers a high-throughput, highly multiplexed and highly automated genotyping service facility . The BeaHLA-DRB1 genotype of each MDA sample was entirely concordant with the corresponding gDNA template, verifying the identity of each MDA sample and ruling out the possibility of contamination.We selected and sent to MSI 20 ng of 88 gDNA samples for amplification, from which an average of 200 μg of MDA product was yielded in 100 μl reactions. The yields ranged from 85 μg to 280 μg, with 61% of samples yielding between 100 μg and 200 μg. The When 100 ng of 448 gDNA samples were amplified using reagents supplied by MSI in kit form and the amplification carried out in-house, an average of 155 μg of MDA product was yielded in 150 μl reactions. The yields ranged from 31 μg to 260 μg, with 80% of samples yielding between 100 μg and 200 μg.INS) -23HphI (rs689), an allelic bias was observed in the MDA process, which resulted in the merging of the heterozygote cluster with the homozygote cluster of the major allele, making the correct assignments of genotype impossible, shown in Figure 2 [see INS in PCR reactions designed for the Pyrosequencing method  [INS may be situated in a sequence region that is predisposed to such allelic bias and the INS variable number of tandem repeats polymorphism, only 580 bp 5' to the -23HphI SNP, is a candidate for such an effect.Of 88 MDA products and their corresponding gDNAs tested at 95 SNPs using the TaqMan method of genotyping there were no samples that consistently failed to produce any data. This confirmed that, for all samples, amplification had been sufficiently successful for the TaqMan chemistry to perform at most SNPs. Genotype concordance rates between MDA product and gDNA and genotype failure rates are given in Table re 2 see . MDA prore 2 see , indicat Sweden) . These rFor 13 additional SNPs for which Invader genotyping was performed, comparison of genotypes generated from MDA product with those from gDNA are shown in Table Two SNPs were genotyped by TaqMan on the 448 samples amplified using reagents supplied by MSI in kit form (and the amplification carried out in-house), and on the corresponding gDNAs. The gDNA samples used for amplification had been extracted from whole blood. Genotype concordance rates between MDA product and gDNA and genotype failure rates are given in Table in silico assay design. These SNPs were selected for their relevance to a range of ongoing projects in our laboratory, located at genes of strong functional candidacy and within regions of linkage to type 1 diabetes e.g. the putative IDDM10 locus on chromosome 10p14-11. All SNPs were validated, having been identified either from empirically confirmed SNPs in dbSNP or from our own re-sequencing efforts. Based on ranking from the in silico design criteria [We commissioned Illumina to conduct a large-scale project using BeadArray genotyping technology involving 3,036 samples  and 384 SNPs i.e. >1.1 million genotypes. In the first instance, 757 SNP sequences were sent to Illumina for criteria , 404 SNPAs well as excluding SNPs that fail to produce robust genotypes, the Illumina protocol excludes samples that do not consistently perform. Of the total number of samples 10.5% were excluded and as a consequence very few data points were missing from the data set, resulting in an apparently low genotype failure rate Table . Within As our samples were family-based, a quality control check of misinheritance rates was possible using PedCheck . Of the Within the panel of 384 SNPs attempted by Illumina, 17 were controls for which we had already produced genotyping data by either TaqMan or Invader methods, enabling an evaluation of the BeadArray data for concordance. Two of these 17 control SNPs failed to be converted to a working assay, giving 15 SNPs and a maximum of 2,503 samples that were genotyped in common. Excluding failed duplicates noted above, comparison of BeadArray genotypes with existing data revealed a concordance rate of 99.6% , indicating the compatibility of the non-excluded gDNA samples with BeadArray, and the quality of existing data. Of the 15 control SNPs, 11 had been genotyped using TaqMan and four using Invader. The concordance rates for each platform were 99.7% using TaqMan  and 99.6% using Invader  when compared with BeadArray data, showing no significant difference between the two platforms.t-test P-value >0.05 for every SNP).Within the BeadArray experiment described above were 86 MDA products and their corresponding gDNA samples. These data were directly compared for sample failure rate and genotype failure rate as shown in Table In this study we have evaluated the Φ29 polymerase MDA whole genome amplification method from MSI by assessing the compatibility of its product with the established TaqMan and Invader genotyping chemistries and with the highly multiplexed BeadArray genotyping platform. We have also evaluated Illumina's BeadArray genotyping platform for a large-scale experiment using gDNA.n = 88 in order to be confirmed. This result is comparable to the smaller study by Tranah et al. [et al. with fluorescent minisequencing on Amersham MDA products compared to gDNA [INS -23HphI, rs689).At 95 SNPs, comparison of TaqMan genotypes generated from MDA product and gDNA templates revealed a very good concordance rate but a higher failure rate for MDA product compared to gDNA. This would need be estimated in a sample size larger than the current  to gDNA . One maret al. [et al. [et al. [Compared to the yields indicated in Dean et al. , our ave [et al. . Further [et al.  protocolet al. using 2,320 SNPs and five samples [In the evaluation of MDA product in conjunction with BeadArray technology, the high concordance rate between genotypes obtained from MDA product and gDNA templates is encouraging. A concordance rate of 99.86% has been reported by Barker  samples . However samples . As withThe performance of MDA product is continuously being monitored in our laboratory. In a study blinded to all genotypers and database administrators, 288 family-based gDNA samples , were replaced with MDA product and left in continual use in our genotyping pipeline for 12 months. The change went undetected by all users. The failure rate for MDA was 3.34% for 15,921 genotypes, compared to 2.39% for 19,272 gDNA genotypes. Therefore, this improvement in the MDA performance for TaqMan is likely to be applicable to BeadArray, which improves the feasibility of mapping susceptibility loci in complex traits.When using a highly multiplexed, highly automated genotyping platform, slight reductions in the quality of template material are likely to have a greater adverse effect on data than in scenarios in which markers are assessed individually and manual scoring is undertaken. Our results indicate that MDA is an adequate solution for the vast majority of SNP markers, even in this highly multiplexed allelic assay platform.It is noted that 5.8% of markers that passed the Illumina acceptable scoring threshold were in fact showing high misinheritance rates in our family samples. This problem was at the same magnitude as TaqMan for individually genotyped markers. This highlights the importance of checking potential positive results with a second genotyping technology.MDA should allow the continuation of genetic analysis on archived DNA in researchers' freezers worldwide, providing the very necessary increases in sample sizes so urgently required ,2,17.The combination of BeadArray high throughput, multiplex genotyping and amplified DNA (MDA product) successfully produced high quality genotype data thereby improving the feasibility and efficiency of mapping common disease susceptibility genes despite limited stocks of gDNA samples.For both the validation experiments (MDA product as a template for TaqMan and Invader genotyping) and for the combined experiment (MDA product as a template for BeadArray genotyping) the same MDA samples were tested. We sent to MSI 20 ng (5 μl at 4 ng/μl) of 88 gDNA samples for amplification, which was performed as a service according to the protocol for human gDNA with the omission of the denaturing step . These gHLA-DRB1 and comparison made with data generated from the corresponding gDNA. HLA-DRB1 genotyping was performed using the Dynal Auto RELI™ SSO HLA-DRB Test system  for each gDNA sample and their MDA products. Although, these samples were amplified by MSI as a service, the reagents are also available from MSI in a kit form for amplification in-house. Following the amplification by MSI we have amplified 448 DNA samples by using the reagents in kit form and 100 ng (25 μl at 4 ng/μl) gDNA template in 100 μl reactions. In the interim, one major change to the MDA protocol had taken place, the inclusion of a denaturation of the DNA template prior to amplification. Previously no denaturation step took place. Two TaqMan markers were tested on these 448 samples and the genotype failure rate calculated.In order to verify the identity of each MDA-produced sample, genotyping was performed at SNP TaqMan assays were carried out for allelic discrimination, 8 ng of DNA (2 μl at 4 ng/μl) used in a 5 μl total reaction volume. TaqMan genotypes from the 88 MDA samples, described above, were compared with TaqMan genotypes generated from their corresponding gDNAs, at 95 SNPs, with a broad range of allele frequencies. The Invader method was used to genotype 13 additional SNPs on the same samples. Comparison was made between data generated from both templates by the measurement of genotype failure and genotype concordance rates.Of the 384 SNPs selected for genotyping 3,036 samples, 17 were control SNPs for which we had existing genotype data, generated by either TaqMan or Invader methods, with which comparison of genotype failure and genotype concordance rates were made. These 384 SNPs covered a broad range of allele frequencies.Incorporated into this experiment was an assessment of suitability and compatibility of the BeadArray genotyping method with MDA product. This involved 86 of the 88 amplified samples, described above, for which genotyping was attempted at all 384 SNPS. Concordance between genotypes generated from MDA product and gDNA templates, together with the genotype and sample failure rates of each template type were measured.In our laboratory we store genotyping data in a MySQL database on a Sun server. The volume of data expected from Illumina was the equivalent of 6 months' in-house genotyping. We separated phenotypic and pedigree information, which is associated with a sample, from genotype data, which is associated with a DNA plate and well position, with a link table to relate the two. Sample aliases are also supported, so that no recoding of identifiers is required, either to export or import Illumina data .RP participated in the design of the study, performed in-house genotyping experiments, and assisted in preparing the manuscript. HER prepared DNA and MDA amplified samples. BJB participated in the design of the study and assisted in the preparation of the manuscript. SN prepared DNA and MDA amplified samples. DS performed in-house genotyping experiments. MS prepared DNA and MDA amplified samples. RCJT participated in the design of the study and assisted in the preparation of the manuscript. AS participated in the design of the study. ACL and LJS performed bioinformatics. NMW managed the data and participated in its interpretation. JAT participated in the design of the study and assisted in the preparation of the manuscript. All authors read and approved the final version of the manuscript.INS -23 HphI SNP. Both the MDA product plots could not be scored. All plots represent the same individual samples with gDNA plots containing 8 additional samples.Figure 2 TaqMan and Invader fluorescence data plotted for the Click here for file


CHD7 on chromosome 8 was the underlying etiology in most of the affected patients.CHARGE syndrome is a complex of birth defects including coloboma, choanal atresia, ear malformations and deafness, cardiac defects, and growth delay. We have previously hypothesized that CHARGE syndrome could be caused by unidentified genomic microdeletion, but no such deletion was detected using short tandem repeat (STR) markers spaced an average of 5 cM apart. Recently, microdeletion at 8q12 locus was reported in two patients with CHARGE, although point mutation in We have extended our previous study by employing a much higher density of SNP markers (3258) with an average spacing of approximately 800 kb. These SNP markers are diallelic and, therefore, have much different properties for detection of deletions than STRs.A global error rate estimate was produced based on Mendelian inconsistency. One marker, rs431722 exceeded the expected frequency of inconsistencies, but no deletion could be demonstrated after retesting the 4 inconsistent pedigrees with local flanking markers or by FISH with the corresponding BAC clone. Expected deletion detection (EDD) was used to assess the coverage of specific intervals over the genome by deriving the probability of detecting a common loss of heterozygosity event over each genomic interval. This analysis estimated the fraction of unobserved deletions, taking into account the allele frequencies at the SNPs, the known marker spacing and sample size.The results of our genotyping indicate that more than 35% of the genome is included in regions with very low probability of a deletion of at least 2 Mb. Although no deletion was detected in the CHARGE study sample, we conclude that this method could be generally useful in other studies in which small deletions occur as part of the allelic spectrum of disease.CHARGE Association is characterized by ocular coloboma, cranial nerve abnormalities, common outflow tract heart defects, choanal atresia, cupped-shaped pinnae, Mondini dysplasia of the inner ear ,2 and grpatients  and may The diagnosis of CHARGE syndrome was established by examination by a participating clinical genetics specialist . A medical history questionnaire was completed by the parents or by direct interview. For the core genotyping, 8 Caucasian and 1 Hispanic case/parent trios were selected based on the presence of 4 major criteria or three major and three minor criteria for the syndrome . There wGenotyping for this study was carried out on an Orchid BioSciences SNPstream UHT platform  as previously described . For thiThis genotyping method utilized Orchid's single base primer extension chemistry (SBE) to identify which bases were present at the site of interrogation. Multiplexed reactions (12-plex) were performed in a single tube that incorporated labeled chain terminating nucleotides onto the ends of the SBE oligonucleotides. These reactions were then hybridized onto a microarray format, that facilitates the solid-phase sorting of the labeled extension-primers to a set of universal tagged primers arrayed on the surface of the plate. The universal tags were arranged on the surface of the microarray plate in a 384-well microwell layout. This microarray format created a generic design consisting of 384 4 × 4 arrays that contained 12 oligonucleotides that corresponded to 12 unique universal capture tags. The four additional oligonucleotides, plotted in each array, were used for positive and negative controls. Genotyping calls were determined by the presence or absence of incorporated dyes that appeared at each spot on the printed arrays.Two Assays-on-demand SNPs, rs422951 and ss1309424, flanking rs431722 were obtained from Applied Biosystems and genotyping was performed in 384 well-plates, using the TaqMan polymerase chain reaction-based method. The final volume reaction was 5 μl using 12 ng of genomic DNA, 2.5 μl of Taqman Master mix and 0.25 μl of 20X Assays-on-Demand SNP Genotyping Assay Mix. The plate was heated at 95° for 10 minutes, followed by forty cycles of denaturation at 92° for 15 seconds and annealing/extension at 60° for 1 minute. PCR plates were read on ABI PRISM 7900HT instrument with SDS v2.0 software. Individual genotypes that were ambiguous were excluded.in situ hybridization was performed as described elsewhere [Bacterial Artificial chromosomes (BACs) were selected from the public database  and obtalsewhere . Detectiet al. [The genotype error rate was estimated using the method of Gordon et al.  and as iet al. . ExpecteNOTCH4 gene on chromosome 6p21.32. Human DNA sequence from clone XXbac-300A18 (GenBank accession number AL662884) on chromosome 6p21 was used for FISH. This BAC clone encompasses the NOTCH4 gene, and was confirmed by PCR amplification of the clone sequence using NOTCH4 specific primers (data not shown). The analysis of the metaphase chromosomes after staining with DAPI showed two bright hybridization signals, indicating the presence of both alleles  and RP11-414L17 (GenBank accession number AC023102). Using these BAC clones, microdeletion of this region was excluded in all the affected patients in this study sample.SNP genotyping data were subjected to analysis for Hardy-Weinberg equilibrium. As an additional test of marker integrity, a transmission disequilibrium test was also performed for each of the SNP markers to examine for distortions in allele transmission in the trios. Unequal transmission of alleles from the heterozygote parents to the affected offspring was not determined by this analysis. Non-paternity was excluded in core pedigrees. Using the method of Gordon , we usedBecause none of the pedigrees were consanguineous, we used parental data to estimate the allele frequencies for each of the 3258 SNP markers . The EDDet al. have previously shown that SNP genotyping of child-parent trios provides valuable information about the presence of de novo microdeletion when sufficient families are studied [Despite various efforts to understand the molecular basis of CHARGE syndrome, with candidate genes sequencing ,15, comp studied . HoweverNOTCH4 gene on chromosome 6p21.32. The Notch gene family encodes highly conserved transmembrane receptors that are involved in intercellular signaling. The Notch signaling pathway plays an essential role in regulating embryonic vascular morphogenesis and remodeling [JAG1 is known to result in Alagille syndrome [NOTCH4 was further studied with FISH as well as flanking SNP markers for microdeletion. The results, however conclusively excluded a discernible microdeletion at this locus.Assuming the genotyping error rate of 0.5%, the probability of observing >2 inconsistencies per marker is very low. The SNP marker rs431722 showed Mendelian inconsistency in four of nine pedigrees, with apparent loss of a parental allele in each case i.e. a frequency much higher than expected for the genotyping error rate. Interestingly, this SNP is located within the modeling . Moreovesyndrome , nonsyndsyndrome  and possThis screen is expected to detect deletions of about 1–2 Mb depending on the overlap of the SNP markers with the deletion interval. Variable coverage for each chromosome was determined for approximately 2 Mb microdeletion in this study. Almost 50% of chromosomes 7, 19 and 20 were excluded for any microdeletion greater than 2 Mb. The least coverage was observed for chromosome 15 and 18, with exclusion of 20% of the chromosome for the presence of a similar genetic aberration. Overall, we can estimate that approximately 36% of the genome had >80% chance for detecting a common 2 Mb deletion in at least 2 patients with CHARGE Syndrome.There are several limitations to this approach in studying the genetics of CHARGE syndrome. Although the marker density is high, the reduced amount of information per marker means that only some of the trios give the possibility of a conclusive result. Denser marker sets would be predicted to fill most of the gaps, but the regions around the centromeres are likely to be difficult with any currently available technique.The strategy out lined in this paper would work equally well for conventional Mendelian traits in which the mutant alleles included at least some deletions. Using a much denser SNP map of 1 marker every 5–10 kb, as is anticipated for whole genome association analyses, it would be possible to detect most deletions given a sufficient representation of deletions within the spectrum of gene mutations.In this report we show that a SNP genotyping screen has excluded moderate length submicroscopic deletions in a subset of patients with CHARGE syndrome. Further analysis by microarray comparative genome hybridization methods or denser SNPs will allow a comprehensive assessment of the role, if any, of microdeletions in CHARGE syndrome.The author(s) declare that they have no competing interests.SRL carried out the data analysis, FISH and TaqMan assays and drafted the manuscript. AMS assisted in the data analysis. MP carried out the SNP genotyping, JWB conceived of the study, and participated in its design and coordination. He performed the statistical analysis and drafted the manuscript. SDF, CAB, and NLG participated in patient enrollment and LMM assisted in DNA extraction. WJC and JAT assisted in manuscript preparation. All authors read and approved the final manuscript.The pre-publication history for this paper can be accessed here:


High-throughput genotyping of single nucleotide polymorphisms (SNPs) generates large amounts of data. In many SNP genotyping assays, the genotype assignment is based on scatter plots of signals corresponding to the two SNP alleles. In a robust assay the three clusters that define the genotypes are well separated and the distances between the data points within a cluster are short. "Silhouettes" is a graphical aid for interpretation and validation of data clusters that provides a measure of how well a data point was classified when it was assigned to a cluster. Thus "Silhouettes" can potentially be used as a quality measure for SNP genotyping results and for objective comparison of the performance of SNP assays at different circumstances.We created a program (ClusterA) for calculating "Silhouette scores", and applied it to assess the quality of SNP genotype clusters obtained by single nucleotide primer extension ("minisequencing") in the Tag-microarray format. A Silhouette score condenses the quality of the genotype assignment for each SNP assay into a single numeric value, which ranges from 1.0, when the genotype assignment is unequivocal, down to -1.0, when the genotype assignment has been arbitrary. In the present study we applied Silhouette scores to compare the performance of four DNA polymerases in our minisequencing system by analyzing 26 SNPs in both DNA polarities in 16 DNA samples. We found Silhouettes to provide a relevant measure for the quality of SNP assays at different reaction conditions, illustrated by the four DNA polymerases here. According to our result, the genotypes can be unequivocally assigned without manual inspection when the Silhouette score for a SNP assay is > 0.65. All four DNA polymerases performed satisfactorily in our Tag-array minisequencing system."Silhouette scores" for assessing the quality of SNP genotyping clusters is convenient for evaluating the quality of SNP genotype assignment, and provides an objective, numeric measure for comparing the performance of SNP assays. The program we created for calculating Silhouette scores is freely available, and can be used for quality assessment of the results from all genotyping systems, where the genotypes are assigned by cluster analysis using scatter plots. High-throughput single nucleotide polymorphism (SNP) genotyping assays generate large amounts of data, which usually is presented as scatter plots of signals corresponding to the two SNP alleles. A robust SNP genotyping assay is characterized by large distances between the three clusters that define the genotypes and small distances between the data points within each cluster. Numeric quality measures for the scatter plots would allow objective and automatic assessment of the success of a SNP assay."Silhouettes" were introduced in 1987 as a general graphical aid for interpretation and validation of cluster analysis [In this study we created a program (ClusterA) to calculate numeric Silhouettes for assessing the quality of genotype clusters obtained in SNP genotyping assays. We show the utility of Silhouettes and the program by applying it to our "in-house" developed four-color fluorescence minisequencing system for SNP genotyping in a microarray format . Single We created a program, denoted ClusterA, for calculating numeric "Silhouettes" for clustered data, such as for example the three clusters of signal ratios commonly obtained in SNP genotyping assays. Figure Allele2/  is plotted, since this is the major determinant for genotype assignment in our system. The logarithm of the sum of the signals from both alleles  plotted on the y-axis is only used to set the cut-off values for failed genotype calls. Figure Here, we applied ClusterA to calculate "Silhouettes" for comparing the quality of the genotype clusters obtained in our "in-house" Tag-array minisequencing system. For each scatter plot, the mean of the average silhouette widths for the three genotype clusters were used to define a "Silhouette score" for each SNP assay. Thus the Silhouette score condenses the cluster quality for each SNP assay into a single measure that ranges from 1.0 to -1.0. When calculating the Silhouette score, the distance between data points can be measured either in one dimension, for example on the x-axis, or in two dimension using vectors, as illustrated in Figure The examples in panels E, F and G of Figure Here we exemplify the use of Silhouette scores by comparing the performance of the TERMIPol, Therminator, KlenThermase and ThermoSequenase DNA polymerases in the Tag-array minisequencing system . Twenty-Figure In the comparison between the enzymes, KlenThermase displayed the highest average Silhouette score, ThermoSequenase had the highest median Silhouette score and also obtained the highest Silhouette score most frequently Table . In addi. We believe that the ClusterA program for calculating Silhouette scores created in the present study is a useful and general tool for any genotyping system, where the genotypes are called by cluster analysis with the aid of scatter plots.We conclude that "Silhouette scores" for assessing the cluster quality is well suited for comparing the performance of SNP assays. Here we used a one-dimensional calculation of the Silhouette scores, by measuring the distances between the data-points along the x-axis only. A two-dimensional Silhouette calculation using vectors should be applied when genotypes are assigned by scatter plots with the fluorescence signals corresponding to the two alleles on the y- and x-axis. Both options are available in the ClusterA program that also calculates mean, variance and F-statistic for the input data set. The program is freely available through our website Genomic DNA was extracted from blood samples from 16 volunteer blood donors using the Wizard genomic DNA purification kit .2-groups to form 80 subarrays per slide, each with 60 cTags as duplicate spots. Finally the microarray slides were scanned, and the fluorescent signals were measured.Twenty-six SNPs, selected to be located in unique PCR amplicons, were included in the test panel. For information on the single nucleotide polymorphisms and oligonucleotides used, see the Cyclic minisequencing reactions were performed in solution with 10 nM of each of the 52 tagged minisequencing primers using 0.1 μM ddATP-Texas Red, ddCTP-Tamra and ddGTP-R110 and 0.15 μM ddUTP-Cy5 , and 0.064 U/μl of one of the four DNA polymerases in 15μl of 0.02% Triton-X, 4.1 mM MgCl2 and 33.6 mM Tris-HCl pH 9.5. The cyclic extension reactions were performed on a Thermal Cycler PTC-225  with an initial 96°C for 3 min followed by 55 cycles of 95°C and 55°C for 20 s each. The DNA polymerases were; TERMIPol , Therminator , KlenThermase , or ThermoSequenase . A custom made reaction rack holding the arrayed slides with a silicon grid to give 80 separate reaction chambers was used during capture of the minisequencing reaction products on the Tag-arrays.® instrument . The excitation lasers were: Blue Argon 488 nm for R110; Green HeNe 543.8 nm for Tamra; Yellow HeNe 594 nm for Texas Red and Red HeNe 632.8 nm for Cy5. The fluorescence signal intensities were determined using the QuantArray®analysis 3.1 software . The QuantArray file was exported to the SNPSnapper v4.0 software ) for genotype assignment. Raw data as fluorescence signals and signal ratios are provided as supplementary material, see Allele1+SignalAllele2) plotted on the y-axis, and the fluorescence signal fraction, obtained by dividing the fluorescence signals from one allele by the sum of the fluorescence signal from both SNP alleles , on the x-axis [The fluorescence signals were measured from the microarray slides using a ScanArray ExpressLL planned the experiments, guided the laboratory work and performed the analysis of results, interpreted the data and drafted the manuscript. AA carried out the laboratory work and part of the data analysis and provided input to the manuscript. MJ programmed the ClusterA program and took part in the interpretation of Silhouettes. ACS initiated the study, supervised it, and coordinated the manuscript writing process. All authors have read and approved the final manuscript.Lists the dbSNP identification numbers and the sequences of the PCR and minisequencing primers.Click here for fileIncludes the raw fluorescence signals and the fluorescence signal intensity ratios for the two experiments as a tab delimited text file.Click here for file


The Affymetrix algorithm was trained on a database of ethnically diverse DNA samples to create SNP call zones that are used as static models to make genotype calls for experimental data. We describe here the implementation of clustering algorithms on large training datasets resulting in improved SNP call rates on the 10K GeneChip.High throughput microarray-based single nucleotide polymorphism (SNP) genotyping has revolutionized the way genome-wide linkage scans and association analyses are performed. One of the key features of the array-based GeneChip® Mapping 10K 2.0 Array was used to identify 822 SNPs that were called consistently less than 75% of the time. These SNPs represent on average 8.25% of the total SNPs on each chromosome with chromosome 19, the most gene-rich chromosome, containing the highest proportion of poor performers (18.7%). To remedy this, we created SNiPer, a new application which uses two clustering algorithms to yield increased call rates and equivalent concordance to Affymetrix called genotypes. We include a training set for these algorithms based on individual genotypes for 705 samples. SNiPer has the capability to be retrained for lab-specific training sets. SNiPer is freely available for download at .A database of 948 individuals genotyped on the GeneChip® without sacrificing accuracy, thereby increasing the amount of valid data generated.The correct calling of poor performing SNPs may prove to be key in future linkage studies performed on the 10K GeneChip. It would prove particularly invaluable for those diseases that map to chromosome 19, known to contain a high proportion of poorly performing SNPs. Our results illustrate that SNiPer can be used to increase call rates on the 10K GeneChip This isn genome -6. The Gf 258 Kb . The assSuccessful hybridizations are detected fluorescently using a streptavidin-phycoerythrin conjugated molecule and an antibody-mediated signal amplification technique. Each SNP is interrogated in both the sense and antisense direction by multiple "quartets" of 25-mer oligonucleotide probes. These probe quartets consist of both perfect match (PM) and mismatch  conformations for the major (A) and minor (B) SNP alleles being investigated. SNP genotype calls are ultimately made using the integration of fluorescent signal intensities at each location across the quartets.® array, see the manuscript by Liu et al. [To make each individual SNP genotype call the Affymetrix software employs a key mathematical filter, a feature extraction calculation, and finally fits each SNP into a trained statistical model. We will briefly review the Affymetrix calling approach on Affymetrix 10K Mapping Array. A more detailed description is available through Affymetrix or through previous publications . The matu et al. . If a SN® assays it is clear that even in samples with the highest overall call rates there are some SNPs consistently called less than other SNPs. In this article we report that infrequently called SNPs on the GeneChip® Mapping 10K 2.0 Array are primarily due to problems associated with the boundaries of the statistical model call zone and therefore are related to suboptimal training of the MPAM algorithm for those particular SNPs. We detail the creation of an application, SNiPer, which utilizes two training-based clustering algorithms to increase overall call rates thereby increasing the amount of usable genotype data on each chip.After completing thousands of 10K GeneChip® we compiled a database of 948 individuals that were genotyped in the last two months in our laboratory. The call rate of these samples was required to be greater than 90%. The frequency at which each SNP was not called – the "NoCall" rate – was calculated , G-C content of the tiled probe, PCR amplicon size, and the distribution of calls for each SNP in relation to the statistical model call zone. Comparison of the DS values clearly indicated that when well-performing SNPs  fail to be called they do so primarily because of the detection filter, while the majority of poorly performing SNPs fail for other reasons. For SNPs with "NoCall" rates less than 25%, the average "NoCall" rate was determined to be 5.5% ± 4.7% and the detection filter failure rate  was 2.9% ± 1.6%. Alternatively, for SNPs with "NoCall" rates greater than 25%, the average NoCall rate was 35.8% ± 11.6% and the detection filter failure rate was only 9.4% ± 10.3%. Failure of the detection filter causes ~50% of the total failures for the top performing SNPs but only ~25% of the total failures for the worst performing SNPs. Probe G-C content was not found to impact call rate. Interestingly, PCR amplicon size does play a role in the frequency at which a SNP is called. The Affymetrix specified PCR cycling parameters favor the production of amplicons less than 1 kb. The average amplicon size for the top 100 worst performing SNPs was 696 bp ± 181 bp while the 100 best performing SNPs were found on amplicons of 521 bp ± 87 bp (two-tailed t-test = p < 0.01). This finding underscores the fact that degraded sample DNA will result in lowered call rates, especially for those SNPs residing on larger sized amplicons. However, the samples used in our study consisted primarily of genomic DNA of high quality as determined by agarose gel electrophoresis. Therefore, while amplicon size can be linked to call rate, further investigation yielded that the more critical factor is the location of the MPAM model silhouette for each SNP. As indicated above, SNP failure of the detection filter is not the primary reason that the worst performing SNPs are not called. As an example one can look at the twenty worst performers. Only six of these SNPs fail the Affymetrix detection filter in at least one-third of the samples. Visual inspection of the GDAS call zones for the remaining SNPs suggests that the majority of the other poor performers are due to inadequate localization of the particular SNP model silhouette, a probable result of inadequate training of the Affymetrix MPAM algorithm for these SNPs. In other words, the RAS1/RAS2 intersection point was closely clustered for the SNP allele but still resulted in a "NoCall" because this cluster was primarily located outside the boundary of the silhouette. We were also able to find examples of widely varying RAS1 values in conjunction with tightly clustered RAS2 values and the opposite case as well. These findings are illustrated in Figure The ability to call these poorly performing SNPs was investigated using the algorithms discussed in the methods section. Through the use of real-time clustering we were able to decrease the average overall "NoCall" rate from 5.22% ± 0.03% to 0.97% ± 1.27%  compiler . The useSNiPer was created to increase call rates without sacrificing accuracy. Two main approaches were explored; the creation of new static models based on our large database of individual genotypes or the development of a way to cluster new samples against an existing library of data. The second option was investigated further because it affords the end user the ability to adapt the clustering as new data is generated much more easily. However there are two major problems facing real-time clustering. The first is prohibitively long runtimes and the second is the elucidation of the proper input parameters for the algorithm variables. The runtime issue can be solved by proper algorithm choice and optimization of the algorithm for increased efficiency. The second hurdle is relatively straightforward for individual genotyping purposes due to the knowledge that the data should cluster in three separate groups.Algorithm choice began with the investigation of PAM, CLARANS, and WAVECLUSTER. PAM and CLARANS are both medoid-based partitioning algorithms and both were found to produce high quality clusters. However, they were abandoned because of extremely poor runtime efficiency on large data sets that make real-time clustering time-consuming. WAVECLUSTER is a wavelet transformation algorithm known to scale extremely well to very large data sets because it requires only one pass through the data. We focused on the sequential use of two algorithms known as PANN (Partitioning Around Nearest Neighbors) and MDBSCAN  since they are less sensitive to input parameters.PANN is a partitioning algorithm similar to K-Means except it utilizes the Affymetrix distance between groups correction in place of the typical distance to the nearest centroid calculation. K-Means clustering fails because it tends to split high-density clusters while PANN takes advantage of the fact that the number of clusters and their approximate locations can be predicted. PANN uses a naïve approach for its initial assignment and reassigns points with a correction based upon the ideal that a point should belong to the cluster with the nearest neighbor. The steps of PANN can be summarized as following:1. Calculate the three centroids representative of the three clusters.2. Assign each point to the cluster with the nearest centroid.3. For each point, find its nearest neighbor in each of the three clusters. Assign each point to the cluster with the smallest nearest neighbor distance.4. Repeat Step 3 until results converge (i.e. no points are moved to different clusters).MDBSCAN is a modified version of the DBSCAN algorithm that includes a pre-processing filter for calculating input parameters and a post-processing filter to assign points considered noise . MDBSCAN. Experiments determined that results converged for the value λ = 35.1. Calculate the value epsilon, NEps, the set of all points that are within Eps distance from the current point.2. For each point, find the epsilon neighborhood size(NEps) ≥ MinPts, then mark it as a core point. For our purposes the value MinPts = 4 was used.3. For each point, if 4. Find a random core point and add it to a new cluster.NEps to the cluster and remove them from the database.5. For each core point in the cluster, add all the points in its 6. Repeat Step 5 until no more new points can be added to the cluster.7. Repeat Step 4 until no more core points remain in the database.8. For the remaining points in the database, assign them to the cluster with the nearest centroid.Our investigations found that the best performance is derived through the sequential use of the PANN and MDBSCAN algorithms. The input data required by these algorithms is the same; columns containing Affymetrix SNP ID numbers, predetermined (via Affymetrix GDAS) or dummy genotype calls, and columns denoting the RAS scores in both the antisense and sense direction for each SNP. A file is then designated as the data output location and the data is clustered using both algorithms. If a SNP does not pass the Affymetrix DS threshold then it also receives a "NoCall" from SNiPer and is not clustered. SNiPer is designed to handle multiple samples at once and we have successfully clustered and called 96 samples in ~60 minutes time. After generating a data set from each algorithm a "strict" filter is applied whereby if the genotype calls did not agree between PANN and MDBSCAN the final output for that SNP was a "NoCall". SNiPer can be downloaded freely from the supplementary data page .Amplicon sizes were determined by taking the chromosomal location of each SNP on the microarray chip and finding the nearest upstream and downstream cut sites for the Xba nuclease. The SNP chromosomal locations were extracted from chromosome report data files downloaded from NCBI's FTP site . Xba cutSNP: single nucleotide polymorphismPCR: polymerase chain reactionFSP: fragment selection by PCRRAS: relative allele signalMJH performed SNP genotyping, participated in the conceptualization of SNiPer, and drafted the manuscript. DWC helped conceptualize and write code for SNiPer, performed the statistical analysis of the SNP data, and helped draft the manuscript. ADS helped conceptualize and write code for SNiPer. JJC performed analysis of SNiPer output data. DH-L performed SNP genotyping. JVP performed statistical analysis of the SNP data and implemented the PCR amplicon script. DAS provided oversight and funding for the project. All of the authors have read and approved the final manuscript.SNP identifiers and their observed No Call Rates.Click here for file


Pooling genomic DNA samples within clinical classes of disease followed by genotyping on whole-genome SNP microarrays, allows for rapid and inexpensive genome-wide association studies. Key to the success of these studies is the accuracy of the allelic frequency calculations, the ability to identify false-positives arising from assay variability and the ability to better resolve association signals through analysis of neighbouring SNPs.k-correction factors from relative allele signal (RAS) values that remove biases and result in more accurate allelic frequency predictions. Our results show that the least accurate SNPs, those most likely to give false-positives in an association study, are identifiable by comparing their frequencies to both those from a known database of individual genotypes and those of the pooled replicates. In a disease with a previously identified genetic mutation, we demonstrate that one can identify the disease locus through the comparison of the predicted allelic frequencies in case and control pools. Furthermore, we demonstrate improved resolution of association signals using the mean of individual test-statistics for consecutive SNPs windowed across the genome. A database of k-correction factors for predicting allelic frequencies for each SNP, derived from several thousand individually genotyped samples, is provided. Lastly, a Perl script for calculating RAS values for the Affymetrix platform is provided.We report the accuracy of allelic frequency measurements on pooled genomic DNA samples by comparing these measurements to the known allelic frequencies as determined by individual genotyping. We describe modifications to the calculation of Our results illustrate that pooling of DNA samples is an effective initial strategy to identify a genetic locus. However, it is important to eliminate inaccurate SNPs prior to analysis by comparing them to a database of individually genotyped samples as well as by comparing them to replicates of the pool. Lastly, detection of association signals can be improved by incorporating data from neighbouring SNPs. Technologies from Affymetrix  [®. The average percentage of SNPs called across all 107 samples was 90.9%. Only individuals with a call rate above 80% were included in the present study. For example, 2,783 SNPs were called for all individuals and 3,525 SNPs were called for >98% of individuals. In our experience using this platform on over 4,000 samples we determined that the call rate is highly dependent on DNA quality and that high quality genomic DNA yields a call rate of 95–98%. The samples used in this study have been collected over several years with variable DNA quality. It is to be expected expect that large-scale whole-genome association studies will also be forced to utilize DNA of less than optimal quality since hundreds to thousands of individuals are needed. Thus the genomic DNA used in this study will likely be representative of what could be expected in a whole-genome association study on a disease where several-thousand individuals are needed.Allelic frequencies for 10,205 SNPs on 107 samples were determined by individually genotyping on the 10K GeneChip (99.9%) . We foun®. In all, 9 microarrays were used for the pooled genotyping compared to 107 microarrays for the individual genotyping.Pools were created in triplicate from the individually genotyped samples. The individuals were from the Old Order Amish and Old Order Mennonite populations of southeastern Pennsylvania . Pool 1 k-correction factor based on their derivation from over 3,000 individuals genotyped on the 10K GeneChip®. The training set consisted of 3,000 individuals that were genotyped in our lab within the past year. All had call rates above 80% with an average call rate of 95%. None of the 3,000 individuals used for calculation of k-correction factors were included in the pooled genomic DNA.The predicted allelic frequencies from pooled genotype samples were calculated for each SNP using a k-correction factor is to allow for calculation of a predicted allelic frequency from peak heights, or in this case fluorescence signal, whereby p = A/(A+kB) [K-correction factors have recently become well established and have been used successfully in primer extension assays whereby measurements in SNP allelic frequencies on pooled genomic DNA have been taken by HPLC, mass spec, and by fluorescence in TAQMAN assays [p is the predicted allelic frequency of the A allele, A is the fluorescent signal intensity measure of the A allele, and B is the fluorescent signal intensity measure of the B allele. The k-correction factor can be calculated for a given SNP using a heterozygote who is AB, effectively 50% A and 50% B. Conveniently, output of the Affymetrix GeneChip software for the Affymetrix 10K Mapping Array includes Relative Allele Signal (RAS) values which have been previously used to determine k-correction values  . K-correN assays -19. For k-correction factors based on the Affymetrix 10K GeneChip® Mapping Array have been previously calculated directly using only heterozygous RAS values [x = (RASx-AAave)/(BBave) where AAave is the average RASx score for individuals AA in the training set, and BBave is the RASx score for individuals BB in the training set. The value of X refers to whether the calculation is for RAS1 or RAS2, and nRAS values are calculated for both RAS1 and RAS2. Thus, two predictions of allelic frequency are obtained: one from RAS1 and one from RAS2. Each RAS variable has distinct variability, and as shown in Figure Whereas S values , we suggS values . Thus fok-correction factors derived from RAS1 and RAS2 values using this training dataset are available at [Values making up each of the RAS1 and RAS2 mean values are provided for homozygous AA, homozygous BB, and heterozygous individuals on a website based on our 3,000 person database which is being made available to the public as part of this publication. These lable at  and as s® we found a median difference in allelic frequency between individually genotyped samples and pooled samples of 5.1%, a mean difference of 6.3%, and a standard deviation of 4.9%. Figure For the 10,205 SNPs on the Affymetrix 10K GeneChipWhile the differences in frequencies between pooled and individual genotyped samples show that calculating frequencies from pooled samples is highly accurate, it is perhaps of greater importance that we are able to predict those SNPs that are unreliable and largely inaccurate. Assays genotyping 500,000 SNPs will likely not have the ability to be as selective and thus are likely to provide a large number of SNPs that do not reliably quantitate allelic frequencies from pooled genomic samples. As shown in Table k-correction factors and predicting allelic frequencies will be unreliable for these SNPs, even if pooled replicates show low variability. SNPs with the highest variance in pool replicates were also unreliable. As a practical measure, we found that applying both filters for too many "NoCalls" in a training set and having a high variance in pooled replicates was more effective than either measure alone. We could identify 1/3rd of the worst performing SNPs (greater than 12% difference), by removing the worst performing 5% of SNPs based on variance in pool replicates and removing the worst performing 5% of SNPs based on excessive "No Calls" when individually genotyped. Consequentially, the removal of those SNPs that are either poorly called in a training set of individually genotyped samples or highly variable across pooled replicates significantly decreases the number of false positives. The number of SNPs removed should maintain a balance between retaining dense SNP coverage and excluding those SNPs more likely to give false positives. Ultimately, removing potential false positives will be a compromise between the coverage of the SNP microarray and the genetic diversity of the population.We found that rarely called SNPs are also likely to be called inaccurately Table . In thisIt is of interest to note that allelic frequencies calculations were more accurate as SNPs approached homozygosity. For example, for those SNPs with allelic frequencies from 0% to 20% and from 80% to 100% the mean difference was 5.2% vs. a mean difference of 7.1% for SNPs with allelic frequencies between 20% and 80%. This finding may be due to inaccuracies in the assays as SNPs approach 50%, since the variance for heterozygotes is higher than the variance measured for homozygotes.In order to assess whether it is feasible to use pooled genotyping to identify the genetic locus for a disease, we created case and control pools for the disease sudden infant death with dysgenesis of the testes (SIDDT) and a pool of Amish control individuals.fcase is the allele frequency for the case group and fcontrol is the allele frequency for the control group.A test for proportions was employed to detect statistical differences between cases and controls. This test-statistic is more often used in pooled studies since frequency data are generally not whole-integers . Shown i2 distribution with one degree of freedom. The SNP with the highest significance, rs949748, had a p-value of 0.00016 and was in the SIDDT locus at chromosome 6q21. However, it is expected that the SNP with the lowest p-value will not always be at the correct disease locus. Even strong single SNP association signals will likely be obscured in the noise when 500,000+ SNPs are probed. Thus we employed a moving window whereby the mean test-statistic of several consecutive SNPs was calculated at each SNP position across the genome. The objective of the moving window was to leverage the fact that neighbouring SNPs will likely be in linkage disequilibrium, whereby one SNP is at least partially predictive of the neighbouring SNP. The number of SNPs contained in the moving window was varied between 1 and 25. Shown in table The distribution follows an approximate χTo compute the statistical significance of averaged test-statistics, we used a permutation test. With this approach the consecutive order of SNPs was randomized in four hundred separate bootstrapped datasets. P-value statistics were calculated from the distribution of these datasets. Shown in Table It will not always be the case that SNPs are in linkage disequilibrium and a windowing-based approach will be effective. The permutation statistics can be used to test this scenario in order to see if the frequency of a given mean window test-statistic is indeed significant. The Old Order Amish and Mennonite populations used in this study arise from a population founded in approximately the sixteenth century with expectedly larger regions of identity by descent. The Amish and Mennonites are not one large isolated population. It is more accurate to say that both these populations derive from the Swiss Anabaptists (circa 1525). These groups are socially and genetically unique even though both came from the same geographical region. Thus undoubtedly some stratification exists between our two cohorts and it is encouraging that the correct region was easily identifiable despite any stratification .Based on previous research in this population, the 10K Mapping Array was anticipated to be of sufficient density whereby many of the SNPs would be in relative linkage disequilibrium throughout this regional population. Indeed, the permutation statistics of moving windows support this notion as the 6q21 region shows a p-value of <1e-6 for window sizes of 10 SNPs and greater, far lower than would be expected with ~10,000 SNP measurements. Other methods have been developed that reduce noise using haplotype data from SNPs in linkage disequilibrium . In the Our results show that (1) pooling genomic samples is highly accurate; (2) unreliable SNPs most likely to give false-positives can be largely identified and removed prior to association analysis; and (3) a moving window of averaged test-statistics can be used to detect association signals. Additionally, we have described modifications as to how allelic frequencies are calculated from RAS values of pooled samples that remove systematic biases.ave) can be effectively used to derive k-correction factors by k = RASAVE/(1-(RASAVE), and as such, can be used to accurately predict allelic frequencies [Pioneering work on pooling studies by other research groups has shown that the average Relative Allele Signal (RASquencies ,11,12. PAVE value that is less accurate than RAS2 alone. We suggest instead that these values be treated as separate measures, each with their distinct variance. In the case of RAS values with a large variance, these values should not be used due to the increased chance of a false positive.First, RAS1 and RAS2 should not be averaged since they are separate probe sets with distinct variances. One may unnecessarily propagate unwanted variance by averaging. For example in Figure k-correction factors derived from a typical RAS value directly obtained from the GDAS software. For example, the average RAS1 for a given SNP of an AA individual may be 0.9, the average RAS1 for a heterozygous individual may be 0.5, and the average RAS2 for a BB individual may be 0.1. When one uses the approach outlined by Butcher, et al, the k-correction factor is 1.0, whereby the RAS value of the average heterozygote is divided by one minus this value [k-correction approach on non-normalized RAS values, one would predict an allelic frequency of 90%, whereas the actual frequency is 100%, a bias of 10%. These biases would be most pronounced as pools approach dominance by one allele type, as would often be the case for a SNP highly associated to a disease.Second, we highly recommend that RAS values for each SNP be normalized prior to calculation of allelic frequencies. When these values are not normalized prior to calculating a predicted allelic frequency a significant bias is introduced since the RAS values, as produced by the Affymetrix GDAS software, generally are not 0.0 or 1.0 for homozygous BB and AA respective alleles. Indeed, on a training set of 1000 individuals we found that 34% of SNPs who were called AA had a RAS value less than 0.9 and 35% of SNPs called BB had a RAS value greater than 0.1. This bias can be seen in an example calculation using ® arrays, they are not provided for the 100K or 500K. This is partly due to the fact that RAS values are no longer used to make a SNP call. We have developed a simple Perl script which generates RAS values, still useful in pooling, for the 100K and 500K Affymetrix GeneChip® platform from CHP files. This tool is available on our website [While RAS values are readily obtainable from the Affymetrix software for the 10K GeneChip website . While ok-correction factors derived from over 3,000 genotyped individuals for the 10K version 2.0 SNP genotyping platform. Other research groups have created central repositories for k-corrections using non-normalized RAS values and we will work with these teams to contribute these values to this valuable centralized resource [Additionally, we are making public on the same site both normalized and non-normalized resource .a priori for each SNP using training sets from large numbers of individual microarrays or by replicates of pooled samples. We further show that despite the issues of population stratification, admixture, and subgroups that are difficult to detect when pooling, the cost savings make pooling a first step that we suggest should logically precede the investment of millions of dollars. We describe here a method by which 100K and 500K Affymetrix SNP array data can be parsed into RAS scores and pooled inbalances accurately assessed in an outbred population.Prior to the investment of large resources into individual genotyping thousands of individuals, one may first consider pooling samples at a low cost to rapidly ascertain gross population stratification concerns and potentially identify the regions of the genome with the strongest association to the trait. The sheer number of SNPs interrogated will lead to a high number of false positives, due to both actual variation in genotype frequencies of the underlying groups and to technical variance. We demonstrate that technical variance can be detected ® Mapping 1.0 and 2.0 Arrays [10K SNP genotyping was performed as detailed by Affymetrix on the 10K GeneChip0 Arrays . In shorThe chp_2_ras.pl script processes one or more CHP text files from Affymetrix 10K and 100K SNP chips, calculates RAS1 and RAS2 scores and outputs them in an Excel spreadsheet. Testing shows that for 10K chips, chp_2_ras.pl produces the same scores as those produced by Affymetrix' GDAS software. chp_2_ras.pl is distributed as part of TGen-Array, a collection of Perl scripts and modules that provide parsing and object-oriented interfaces to common microarray files. The script can be downloaded at the TGen bioinformatics website .DWC and MJH performed SNP genotyping, participated in the concept of the paper, and drafted the manuscript. DHL, VLZ, MJH, and AML conducted pooling and SNP genotyping. DWC and JMP performed statistical analysis of the SNP data. DAS participated in study design, coordination, and manuscript drafting. All of the authors have read and approved the final manuscript.Calculated k-correction factors for pooling on Affymetrix 10K GeneChip Mapping Array based on 3,000 person database.Click here for fileThe chp_2_ras.pl script processes one or more CHP text files from Affymetrix 10K, 100K, and 500K EA SNP chips, calculates RAS1 and RAS2 scores and outputs them in an Excel spreadsheet. Testing shows that for 10K chips, chp_2_ras.pl produces the same scores as those produced by Affymetrix' GDAS software. GDAS does not calculate RAS values for 100K chips. It should be noted that SNPs on 100K chips do not necessarily contain even numbers of sense and antisense probes and in fact only about 40% have 5 sense and 5 antisense probes. The remaining SNPs have a 6–4 or 7–3 probe bias towards either sense or antisense. This is important because part of the RAS calculation involves taking the median of the "successful" probes and median may not be the best approach if only 3 probes exist in one direction and some may have failed and been discarded. chp_2_ras.pl is distributed as part of TGen-Array, a collection of Perl scripts and modules that provide parsing and object-oriented interfaces to common microarray files. The TGen-Array site contains online documentation for all modules and scripts in the distribution including pages that show the source code so the code and algorithms may be inspected.Click here for file