A plant-based diet protects against chronic oxidative stress-related diseases. Dietary plants contain variable chemical families and amounts of antioxidants. It has been hypothesized that plant antioxidants may contribute to the beneficial health effects of dietary plants. Our objective was to develop a comprehensive food database consisting of the total antioxidant content of typical foods as well as other dietary items such as traditional medicine plants, herbs and spices and dietary supplements. This database is intended for use in a wide range of nutritional research, from in vitro and cell and animal studies, to clinical trials and nutritional epidemiological studies.We procured samples from countries worldwide and assayed the samples for their total antioxidant content using a modified version of the FRAP assay. Results and sample information  were registered for each individual food sample and constitute the Antioxidant Food Table.The results demonstrate that there are several thousand-fold differences in antioxidant content of foods. Spices, herbs and supplements include the most antioxidant rich products in our study, some exceptionally high. Berries, fruits, nuts, chocolate, vegetables and products thereof constitute common foods and beverages with high antioxidant values.This database is to our best knowledge the most comprehensive Antioxidant Food Database published and it shows that plant-based foods introduce significantly more antioxidants into human diet than non-plant foods. Because of the large variations observed between otherwise comparable food samples the study emphasizes the importance of using a comprehensive database combined with a detailed system for food registration in clinical and epidemiological studies. The present antioxidant database is therefore an essential research tool to further elucidate the potential health effects of phytochemical antioxidants in diet. It is widely accepted that a plant-based diet with high intake of fruits, vegetables, and other nutrient-rich plant foods may reduce the risk of oxidative stress-related diseases -6. Under3+/Fe2+ couple [Several assays have been used to assess the total antioxidant content of foods, e.g. the 6-hydroxy-2,5,7,8-tetramethylchroman-2-carboxylic acid (Trolox) equivalent antioxidant capacity (TEAC) assay , the fer+ couple ,17. Thus+ couple . This ma+ couple . A disad+ couple -23. We hThere is not necessarily a direct relationship between the antioxidant content of a food sample consumed and the subsequent antioxidant activity in the target cell. Factors influencing the bioavailability of phytochemical antioxidants, include the food matrix, absorption and metabolism -27. AlsoThe aim of the present study was to screen foods to identify total antioxidant capacity of fruits, vegetables, beverages, spices and herbs in addition to common everyday foods. In nutritional epidemiologic and intervention studies, the Antioxidant Food Database may be utilized to identify and rank diets and subjects with regard to antioxidant intake and as a tool in planning dietary antioxidant interventions. The database will be available online at the University of Oslo's web site.4 × 7 H2O from Riedel-deHaën AG , acetic acid and hydrochloric acid from Merck , FeCl3 × 6H2O from BDH Laboratory Supplies . MilliQ water  and methanol of HPLC-grade obtained from Merck was used for all extractions. 2-propanol (HPLC-grade) was obtained from Merck.TPTZ  was obtained from Fluka Chemie AG , sodium acetate trihydrate and FeSOThe antioxidant measurements have been conducted over a period of eight years, from 2000 to 2008. The samples were procured from local stores and markets in Scandinavia, USA and Europe and from the African, Asian and South American continents. Many of the samples of plant material, like berries, mushrooms and herbs, were handpicked. Commercially procured food samples were stored according to the description on the packing and analyzed within four weeks. Handpicked samples were either stored at 4°C and analyzed within three days or frozen at -20°C and analyzed within four weeks. Products that needed preparation such as coffee, tea, processed vegetables etc. were prepared on the day of analysis. Furthermore, all samples were homogenized, dry samples were pulverized and solid samples were chopped in a food processor. After homogenizing, analytical aliquots were weighed. Included in the database are 1113 of the food samples obtained from the US Department of Agriculture National Food and Nutrient Analysis Program. They were collected, homogenized, and stored as previously described . Three r3+ complex reduces to the TPTZ-Fe2+ form in the presence of antioxidants. An intense blue color with absorption maximum at 593 nm develops. The measurements were performed at 600 nm after 4 min incubation. An aqueous solution of 500 μmol/L FeSO4 × 7 H2O was used for calibration of the instrument. Validation of the assay is described in Halvorsen et al. 2002 [The FRAP assay of Benzie and Strain  was usedal. 2002 . BrieflyThe samples were classified into 24 different categories covering products from the plant kingdom, products from the animal kingdom and mixed food products. Information about sample processing , if any, was included, along with all sample specifications, i.e. product name, brand name, where the product/sample was procured and country of origin. The product information in the database was collected from the packing of the product, from supplier or purchaser. When this information was not available or the samples were handpicked, only country of origin is presented. Each sample is assigned to only one category. The classification was done according to information from the supplier or purchaser, or according to common traditional use of the food. Some foods may therefore be categorized otherwise in other food cultures. For products in the categories "Herbal/traditional plant medicine" and "Vitamin and dietary Supplements" some products may rightfully be classified as both an herbal medicine and a supplement, but are still assigned to only one category. All berries, fruits, and vegetables were fresh samples unless otherwise noted in the database. The Antioxidant Food Table contains 3139 samples. About 1300 of these samples have been published before ,17,28 buth percentile of plant-based foods is 4.11 mmol/100 g compared to 0.21 and 0.68 mmol/100 g for animal-based and mixed foods, respectively. The high mean value of plant-based foods is due to a minority of products with very high antioxidant values, found among the plant medicines, spices and herbs. In the following, summarized results from the 24 categories are presented.Our results show large variations both between as well as within each food category; all of the food categories contain products almost devoid of antioxidants Table . Please In the category "Beverages", 283 products were included, from coffee and tea to beer, wine and lemonades. Dry products like coffee beans and dried tea leaves and powders were also included. The highest antioxidant values in this category were found among the unprocessed tea leaves, tea powders and coffee beans. In Table Most of the breakfast cereals have antioxidant content in the range of 0.5 to 2.25 mmol/100 g, while 4 single products are above this range. Among grains and grain products, buckwheat, millet and barley flours are the flours with the highest antioxidant values in our study Table , while cIn the nuts and seeds category we analyzed 90 different products, with antioxidant contents varying from 0.03 mmol/100 g in poppy seeds to 33.3 mmol/100 g in walnuts, with pellicle and purchased with nut shell intact. Pecans with pellicle, sunflower seeds and chestnuts with pellicle, have mean antioxidant content in the range of 4.7 to 8.5 mmol/100 g . Chocolate products with cocoa contents of 24-30%, 40-65% and 70-99% had mean antioxidant contents of 1.8, 7.2 and 10.9 mmol/100 g, respectively.The dairy category included 86 products and the majority of these products were low in antioxidant content, in the range of 0.0 to 0.8 mmol/100 g. Dairy products with added berries or chocolate and cheeses like Brie, Gorgonzola and Roquefort are the most antioxidant rich products in this category.One hundred and thirty four products are included in the category "Desserts and cakes". In the upper range of this category we find dog rose soup and chocolate cookies. Eggs are almost devoid of antioxidants with the highest antioxidant values found in egg yolk (0.16 mmol/100 g).Margarine, butter, canola, corn and soybean oil are the highest ranking products in the "Fats and oils" category. Almost half of the fats and oils have antioxidant content between 0.4 and 1.7 mmol/100 g.th and 75th percentiles being 1.90 to 6.31 mmol/100 g, respectively. There were 13 samples with especially high antioxidant capacity in this category, including dried amla , wild dried dog rose (Rosa canina) and products of dried dog rose with antioxidant contents in the range from 20.8 to 78.1 mmol/100 g. Dried wild bilberries , zereshk (red sour berries) from Iran and fresh dog rose (from Norway and Spain) have mean antioxidant contents of 48.3, 27.3 and 24.3 mmol/100 g, respectively. Other examples of antioxidant rich berries are fresh crowberries, bilberries, black currants, wild strawberries, blackberries, goji berries, sea buckthorn and cranberries. The least antioxidant rich berry products are some of the berry jams with mean values of approximately 0.5 mmol/100 g.In Table A total of 278 fruits and fruit products and 303 vegetables and vegetable products were included in the database. In the analyzed vegetables, antioxidant content varied from 0.0 mmol/100 g in blanched celery to 48.1 mmol/100 g in dried and crushed leaves of the African baobab tree. In fruits, procured in 8 different countries, the antioxidant content varies from 0.02 mmol/100 g for watermelon to 55.5 mmol/100 g in the yellow pith of Spanish pomegranate. Examples of antioxidant rich fruits and vegetables were dried apples, flour made of okra, artichokes, lemon skin, dried plums, dried apricots, curly kale, red and green chili and prunes  from Peru has the highest antioxidant content of all the products in the database (2897.1 mmol/100 g). Other antioxidant rich products are Triphala, Amalaki and Arjuna from India and Goshuyu-tou, a traditional kampo medicine from Japan, with antioxidant values in the range of 132.6 to 706.3 mmol/100 g. Only four products in this category have values less than 2.0 mmol/100 g.This is the most antioxidant rich category in the present study and is also the category with largest variation between products. Half of the products have antioxidant values above the 90The category includes 52 products, including European, Scandinavian and American products. The variation in antioxidant content in dinner and dessert products for infants varies from 0.02 to 1.25 mmol/100 g. Interestingly, human breast milk (49 samples from Norwegian mothers) has a mean content of 2.0 mmol/100 g. In addition, the category includes two Norwegian dog rose products for infants with antioxidant contents of 6.7 and 18.5 mmol/100 g.An excerpt of the 425 spices and herbs analyzed in our study are presented in Table In this broad category, we have analyzed 251 products and found that the products with highest antioxidant content are tomato based sauces, basil pesto, mustard paste, sun dried tomatoes and tomato paste/puree, in the range of 1.0 to 4.6 mmol/100 g.The category "Vitamin and dietary supplements" includes 131 commercially available vitamin and dietary supplement products from USA, Norway, Mexico and Japan of which many have high antioxidant scores. Among them are supplements containing anthocyanins, vitamin C, green tea powder and multivitamins and multi-antioxidant tablets.The majority of the products in these categories were low in antioxidant content. Nevertheless, products like liver, bacon and some prepared chicken and beef products have antioxidant values between 0.5 and 1.0 mmol/100 g.With this study we present a comprehensive survey of the total antioxidant capacity in foods. Earlier small-scale studies from other laboratories have included from a few up to a few hundred samples -22,29-31When classifying the samples into the three main classes the difference in antioxidant content between plant- and animal-based foods become apparent. The results here uncover that the antioxidant content of foods varies several thousand-fold and that antioxidant rich foods originate from the plant kingdom while meat, fish and other foods from the animal kingdom are low in antioxidants. Comparing the mean value of the 'Meat and meat products' category with plant based categories, fruits, nuts, chocolate and berries have from 5 to 33 times higher mean antioxidant content than the mean of meat products. Diets comprised mainly of animal-based foods are thus low in antioxidant content while diets based mainly on a variety of plant-based foods are antioxidant rich, due to the thousands of bioactive antioxidant phytochemicals found in plants which are conserved in many foods and beverages.Most of the spices and herbs analyzed have particularly high antioxidant contents. Although spices and herbs contribute little weight on the dinner plate, they may still be important contributors to our antioxidant intake, especially in dietary cultures where spices and herbs are used regularly. We interpret the elevated concentration of antioxidants observed in several dried herbs compared to fresh samples, as a normal consequence of the drying process leaving most of the antioxidants intact in the dried end product. This tendency is also seen in some fruits and their dried counterparts. Thus, dried herbs and fruit are potentially excellent sources of antioxidants.Croton lechleri sampled in Peru to have exceptional high antioxidant content. This sap has a long history of indigenous use in South America for wound healing and as an antifungal, antiseptic, antiviral and antihaemorrhagic medicine. Proanthocyanidins are major constituents of this sap [Herbal and traditional plant medicines emerged as many of the highest antioxidant-containing products in our study. We speculate that the high inherent antioxidant property of many plants is an important contributor to the herb's medicinal qualities. In our study we identified Sangre de Grado, the sap from the tree trunk of the species this sap  and studthis sap ,34 and pthis sap . Other ethis sap , antibacthis sap ,38 and cthis sap . Arjuna,this sap ,41 whilethis sap .With their high content of phytochemicals such as flavonoids, tannins, stilbenoids, phenolic acids and lignans -45 berriNuts are a rich source of many important nutrients and some are also antioxidant-rich. The observed increase in antioxidant content in nuts with pellicle compared to nuts without pellicle is in good agreement with earlier studies showing the flavonoids of many nuts are found in the nut pellicle .After water, tea and coffee are the two most consumed beverages in the world, although consumption patterns vary between countries. Because of the fairly high content of antioxidants and the frequent use, coffee and tea are important antioxidant sources in many diets. Several different compounds contribute to coffee's antioxidant content, e.g., caffeine, polyphenols, volatile aroma compounds and heterocyclic compounds, ,50-52. MInterestingly, the antioxidant content in human breast milk is comparable to that in pomegranate juice, strawberries and coffee and on average higher than the antioxidant content observed in the commercially available infant formulas analyzed in our study. Breakfast cereals are also potential important sources of antioxidants; some of these products have antioxidant contents comparable to berries, which are fairly high, compared to other grain products and may be due to antioxidants added to the products in fortification process.Chocolate have for several years been studied for its possible beneficial health effects . Our resAs demonstrated in the present study, the variation in the antioxidant values of otherwise comparable products is large. Like the content of any food component, antioxidant values will differ for a wide array of reasons, such as growing conditions, seasonal changes and genetically different cultivars ,58, storInitial studies have been carried out to examine the association between intake of antioxidant rich foods and their health effects ,70. SomeThe Antioxidant Food Table is a valuable research contribution, expanding the research evidence base for plant-based nutritional research and may be utilized in epidemiological studies where reported food intakes can be assigned antioxidant values. It can also be used to test antioxidant effects and synergy in experimental animal and cell studies or in human clinical trials. The ultimate goal of this research is to combine these strategies in order to understand the role of dietary phytochemical antioxidants in the prevention of cancer, cardiovascular diseases, diabetes and other chronic diseases related to oxidative stress.R. Blomhoff is a shareholder in Vitas AS, D.R. Jacobs Jr is an unpaid member of the Scientific Advisory Council of the California Walnut Commission. The other authors declare that they have no competing interests.MHC took part in planning the study design, contributed to database management, sample procurement, drafting and writing of manuscript. BLH took part in planning the study design and was responsible for assay development and validation, sample analysis, and writing of manuscript, SKB took part in planning the study design and was the database creator and contributed to database management and writing of manuscript, SD, LS, CW, HS, IB, NB, WCW, KMP and DRJ contributed to sample procurement and writing of manuscript, KH, YU and CS contributed to sample procurement and analysis and writing of manuscript, RB was responsible for funding and study design and contributed to sample procurement and writing of manuscript. All authors read and approved the final manuscript.The Antioxidant Food Table, Carlsen et al. 2010. the main results of the present study; the table includes all the 3139 products with product descriptions, details and antioxidant analysis results, categorized into 24 categories and arranged alphabetically within each category.Click here for file


Molecular estimates of breeding value are expected to increase selection response due to improvements in the accuracy of selection and a reduction in generation interval, particularly for traits that are difficult or expensive to record or are measured late in life. Several statistical methods for incorporating molecular data into breeding value estimation have been proposed, however, most studies have utilized simulated data in which the generated linkage disequilibrium may not represent the targeted livestock population. A genomic relationship matrix was developed for 698 Angus steers and 1,707 Angus sires using 41,028 single nucleotide polymorphisms and breeding values were estimated using feed efficiency phenotypes  recorded on the steers. The number of SNPs needed to accurately estimate a genomic relationship matrix was evaluated in this population.Results were compared to estimates produced from pedigree-based mixed model analysis of 862 Angus steers with 34,864 identified paternal relatives but no female ancestors. Estimates of additive genetic variance and breeding value accuracies were similar for AFI and RFI using the numerator and genomic relationship matrices despite fewer animals in the genomic analysis. Bootstrap analyses indicated that 2,500-10,000 markers are required for robust estimation of genomic relationship matrices in cattle.This research shows that breeding values and their accuracies may be estimated for commercially important sires for traits recorded in experimental populations without the need for pedigree data to establish identity by descent between members of the commercial and experimental populations when at least 2,500 SNPs are available for the generation of a genomic relationship matrix. The method is computationally straightforward and robust , and does not require estimation of the SNP allele frequencies within the base population /g1 . With thpulation . Our estn = 100, 500, 1000, 2500, 5000, 10000, 15000, 20000, 25000, 30000, 35000 and 40000) of markers were randomly sampled with replacement from the full set of 41,028 markers to ensure a random representation of the entire genome within the marker subset. For each of i = 1,...,50 replicates at each value of n, a GRM (Gni) was estimated using the regression approach described above and correlations were estimated between the upper triangular elements of Gni and G  for all 2,405 animals and between Gni and A for all 1,707 AI sires and averages were produced across replicates.MATLAB  was used to test the number of markers necessary to precisely estimate the GRM by the regression approach in cattle. Subsets  and was relaxed to F>6.33 until a total of 384 SNP were retained for this analysis.Summary statistics presented in Table Nevertheless, the heritability estimates for all three traits were lower than literature estimates  and accuracies estimated as the correlation between true and predicted breeding values  were simWhile the calculation of genomic relationship coefficients is straightforward, it is not clear how many SNP are required to produce robust estimates of relatedness. Figure While estimates of genomic relationship coefficients based upon at least 10,000 SNPs appear to be extremely robust, estimates appear to be very sensitive to SNP sample size when fewer than 2,500 SNP are used Figure . This haWhen we estimated the GRM using the 1,536 SNPs with the highest MAF (average MAF = 0.4953), the correlation between genomic relationship coefficients was 0.8394, less than the minimum correlation obtained from 50 replicates of random sampling, suggesting that the sampled loci were more strongly linked than the majority of randomly sampled sets of 1,536 SNPs. To test this hypothesis, the average spacing between markers on the same chromosome was tested for the SNP panel with the highest MAF and also for the 200 bootstrap samples. The average spacing between markers in the high MAF panel was 1.8 Mb, and 298 of these markers were less than 250-kb apart on the same chromosome. Conversely, the average spacing of markers on the same chromosome across the 200 bootstrap samples was 38.69 Mb and their average MAF was 0.29. Thus, the SNP with the highest MAF within the Angus genome are tightly linked and provide less information about the relatedness of animals than a randomly sampled panel of SNPs. Just why this is the case is not clear.When we estimated the GRM using the 1,536 SNP with the lowest MAF (average MAF = 0.0614) the correlation between genomic relationship coefficients was 0.4802. However, when we sampled the 384 SNPs with the highest and lowest MAF , the correlations between genomic relationship coefficients estimated using 384 and 41,028 SNPs were 0.6947 and 0.2226, respectively. The former exceeds the largest correlation obtained in 50 replicate random samples of 384 SNP suggesting that by reducing the number of SNP from 1,536 to 384, the pattern of LD among the 384 loci with the highest MAF is not significantly different to that among randomly sampled loci. Finally, using a forward selection process we identified a panel of 384 SNPs that were most strongly associated with AFI and that had an average MAF of 0.2884. The correlation between genomic relationship coefficients estimated using this sample and the complete set of 41,028 SNPs was 0.6198, slightly lower than the average for randomly sampled SNPs.These results suggest that the small panels of SNPs that are soon likely to be commercialized within the beef and dairy cattle industries will have some utility for the estimation of genomic relationship coefficients and that this will allow the estimation of molecular breeding values for traits other than those targeted by the SNPs within the panels. However, our results also indicate that the greatest benefits of the technology will not be realized until inexpensive assays can be produced which query ≥2,500 SNPs. When smaller panels of 60- 90 SNPs are used for parentage identification a NRM could be constructed based on the inferred pedigree . We founUsing a genomic relationship matrix, breeding values and their accuracies may be estimated for commercially important sires for traits recorded in experimental populations without the need for pedigree data to establish identity by descent between members of the commercial and experimental populations. This matrix should ideally consist of at least 2,500 SNP in cattle populations, preferably those that are unlinked and not in extremely high linkage disequilibrium with one another. While sufficient numbers of SNPs are not yet available for all species to allow the precise estimation of genomic relationship coefficients, there are no technical limits to rapid and inexpensive SNP development using the deep sequencing of reduced representation libraries with next generation sequencing platforms ,33. The MMR helped conceive the study and its design, performed statistical analysis, assisted in collecting samples, drafted the manuscript and assisted in sample preparation and data collection, JFT helped conceive the study and its design, performed statistical analysis, assisted in collecting samples and helped draft the manuscript, RDS helped conceive the study and its design and assisted in sample preparation and data collection, SDM assisted in sample preparation and data collection, MCM assisted in sample preparation and data collection, SLN assisted in sample and data collection, MSK helped gather data and assisted in phenotype calculations, RLW helped conceive the study and its design, helped perform statistical analysis and helped draft the manuscript. All authors read and approved the final manuscript.


In this first autosomal genome-wide association scan of g, we used a two-stage quantitative trait locus (QTL) design with pooled DNA to screen more than 500 000 single nucleotide polymorphisms (SNPs) on microarrays, selecting from a sample of 7000 7-year-old children. In stage 1, we screened for allele frequency differences between groups pooled for low and high g. In stage 2, 47 SNPs nominated in stage 1 were tested by individually genotyping an independent sample of 3195 individuals, representative of the entire distribution of g scores in the full 7000 7-year-old children. Six SNPs yielded significant associations across the normal distribution of g, although only one SNP remained significant after a false discovery rate of 0.05 was imposed. However, none of these SNPs accounted for more than 0.4% of the variance of g, despite 95% power to detect associations of that size. It is likely that QTL effect sizes, even for highly heritable traits such as cognitive abilities and disabilities, are much smaller than previously assumed. Nonetheless, an aggregated ‘SNP set’ of the six SNPs correlated 0.11 (P < 0.00000003) with g. This shows that future SNP sets that will incorporate many more SNPs could be useful for predicting genetic risk and for investigating functional systems of effects from genes to brain to behavior.General cognitive ability ( Nonetheless, combining these SNPs in an aggregated g‘SNP set’ yielded significant associations with g as early as 2 years of age, significant associations with reading at 7 years and several examples of significant genotype–environment interaction and correlation ’. Pooled DNA can be genotyped reliably on microarrays .Because 11 555 SNPs do not represent a genome-wide scan, the purpose of the present study was to apply the SNP-MaP approach to a genome-wide scan for The Twins Early Development Study (TEDS) is a large, longitudinal study set up to investigate the genetic and environmental bases of cognitive and behavioral development  and the Vocabulary test (e.g. ‘what does “strenuous” mean?’), both from the Wechsler Intelligence Scale for Children  from this stage who were present in the pooled DNA stage of g score was −1.5 for the low group and 1.6 for the high group. The low and high groups were each divided into 10 independent DNA pools  with about 40 children in each pool; each individual was randomly assigned to one pool. The 20 DNA pools were allelotyped on Affymetrix GeneChip Human Mapping 500K Arrays as a screen for allele frequency differences between the low and high g groups.A 1 standard deviation cut-off (i.e. corresponding to the top and bottom 16th percentiles) was used to select one member of a twin pair from the g scores (z-score range of the sample was −3.5 to 4.9). There were 2650 individuals (82.9%) from this stage who were present in individual genotyping stage of our 10K SNP-MaP study  , was quantified in triplicate using PicoGreen™ double-stranded DNA quantification reagent . Upon obtaining reliable triplicate readings, each individual contributed the same amount of DNA to their respective pool. Because individual samples differed in their concentrations, individual DNAs were adjusted to produce equimolar DNA contributions to the pools. We deemed 1 μl the minimum volume that could be added to a pool without compromising pipette error. Therefore, the amount of DNA contributed to the pools was determined by the mass of DNA contained in 1 μl of the most concentrated individual, in this case 98.6 ng/μl. Each individual therefore contributed 98.6 ng to the DNA pool. The range of concentrations was 14.7–17.2 ng/μl for the 10 pools from the low g group and 15.7–17.2 ng/μl for the 10 pools from the high g group.Genomic DNA for each individual, extracted from buccal swabs (eneChip® operating software (GCOS) version 1.4. Cell intensity (.cel) files were analyzed using gtypev.40. For quality control checks, a reference DNA individual provided by the manufacturer (sample number 100103) was also assayed on a separate microarray set.Each of the 20 DNA pools was allelotyped using the Affymetrix GeneChip Human Mapping 500K Array set in accordance with the standard protocol for individual DNA samples . Each microarray was scanned using the GeneChip® Scanner 3000 with high-resolution scanning upgrade, which was controlled using GSupplementary Materials and Methods in Relative allele signal scores, calculated using the 10K MPAM Mapping algorithm, have been shown to be reliable and valid indices of allele frequency in pooled DNA  smaller average variance of the low and high g groups , (3) smaller average variance within each microarray , (4) greater number of successful replicate pools and (5) greater minor allele frequency, as indexed by the average of the low and high g groups. Because we expect many more putatively significant associations from stage 1 than could be realistically individually genotyped , we used this composite to choose the top 47 SNPs with the highest composite scores. The SNP screen was restricted to the autosomes because the DNA pools included both boys and girls, which complicates analyses of SNPs on the X chromosome.To select SNPs for individual genotyping, we derived a rank-based composite score using five criteria from the stage 1 data set. The derivation of this composite score is presented as eneMapper version 4.0 software . SNPlex is a capillary electrophoresis-based multiplex genotyping system capable of genotyping up to 48 SNPs per sample per well (http://hapmart.hapmap.org/BioMart/martview).After excluding stage 1 individuals and selecting just one twin per pair, the 3195 individuals described earlier were genotyped using the Applied Biosystems’ SNPlex™ genotyping system and analyzed using Gper well , 2005 wer) and coding the three observed genotypes such that 0 = AA, 1 = AB and 2 = BB. In addition, we followed a procedure recommended by Because quantitative genetic research strongly suggests that the majority of genetic effects are additive, we were primarily interested in testing SNPs for their additive effect. Therefore, genotypes of SNPs passing quality control (see below) were tested for additive genetic effects using a Pearson correlation  replicates for both high and low groups. We also excluded SNPs with minor allele frequencies lower than 0.05 as power to detect association in this range is greatly reduced. After these exclusion criteria, the autosomal genome-wide screen consisted of 449 127 SNPs from the 500K microarray set.g groups was calculated for each SNP. The correlation between the low and high g groups was 0.993, indicating that the rank order of allele frequencies was highly reliable overall – a test analogous to genome control. Accordingly, between-group differences were small: The average allele frequency for the low and high Materials and methods, SNPs selected for individual genotyping were chosen on the basis of a ranked composite score, which took into account the between-group allele frequency difference, variance between and within biological replicate microarrays, number of successfully assayed arrays and minor allele frequency. Because of financial restrictions, we were limited to genotyping a single probe set of 47 SNPs with the highest composite scores. The mean absolute difference between low and high SNP-MaP allele frequency estimates for these was 0.108 (ranging from 0.05 to 0.26). The SNP with the largest difference was not selected as it exhibited high levels of variance and the minimum number of replicates, which counted unfavorably in the composite measure. g group against that of the high g group. Using conventional parametric statistical methods (Student's t-test) to test for allele frequency differences between the 10 pools for the low g group and the 10 pools for the high g group, all SNPs were significant at P < 0.05 (range: 0.0000002 ≤ P≤ 0.03). However, the composite selection criterion goes beyond traditional P-values to incorporate variance between and within groups, as well as the variance of a single SNP's measurement on a single microarray. Details about the 47 selected and successfully genotyped SNPs can be found in Table S1 in As explained in On a study-wide level of analysis, five out of 47 SNPs failed to call because of poor genotype clustering and were omitted; five SNPs were also omitted that showed unacceptably high genotyping error rates ; for the 37 SNPs passing these quality control criteria, we observed 9 mismatches out of 2981 genotypes (error rate 0.3%). Of these errors, there was bias toward calling heterozygotes.g unchanged. The effective sample size was n = 2782.Finally, 13% of the sample showing unacceptably low call rates (<80%) were omitted; fragmented DNA is a prerequisite to running SNPlex, and overfragmentation is the likely cause of these low call rates. At the SNP level of analysis, we excluded an additional approximately 4% of SNP genotypes whose peak height was <25% of the average peak height for that SNP across the study. At the cost of reduced sample size, these conservative criteria improved observed genotypic distributions under Hardy–Weinberg expectation, tightened genotype clusters and left the distribution of g throughout the distribution. Each individual's genotypes for the 37 SNPs were tested for additive genotypic effects. With 37 tests and an alpha of 0.05, two significant results would be expected on the basis of chance alone. As shown in g throughout the distribution using a nominal one-tailed alpha level of 0.05 (range: 0.0007 ≤ P≤ 0.043). We used a one-tailed test because the difference observed in stage 2 was required to be in the same direction as that seen in stage 1 screening. None of the six SNPs significantly deviated from Hardy–Weinberg expectation. A summary of stage 1 and 2 results for all 37 SNPs (including SNP locations) is provided in Table S1. After correcting for 37 simultaneous tests using a step-up false discovery rate (P < 0.03). However, the other nominally significant SNPs were also included in our SNP set (see below).The 37 successfully genotyped SNPs nominated by stage 1 were individually genotyped across the unselected sample of 2782 children in order to test the QTL hypothesis directly by assessing the extent to which the SNPs are associated with ery rate  of 0.05,r) across the six SNPs of 0.043; the largest correlation is 0.062 . Squaring these correlations (r2) to estimate effect size indicates that these associations account from only 0.1% to 0.4% of the variance of g scores. The average effect size is 0.2%, and the sum of the effect sizes of the six SNPs is 1.2%. Following the procedure suggested by The significant correlations are small, with an average correlation (g scores (age and sex regressed) for the three SNP genotypes. For the first five SNPs, the nonoverlapping standard error bars indicate that the homozygote genotypes differ significantly on g. For the sixth SNP , indicating that the homozygotes differ by about 0.12 of a standard deviation in their g scores . This finding indicates that even with very modest correlations such as these, homozygotes that differ at a single locus could provide biological clues into understanding the etiology of g. This point takes on greater significance when the SNPs are combined in an SNP set.ixth SNP , the sigg.The additive genotypic values for the six SNPs are uncorrelated because the SNPs are not in linkage disequilibrium with each other. This permits the creation of a composite SNP set that aggregates the small effects of each SNP and can be useful for certain purposes such as selecting individuals at genotypic risk in studies that are not sufficiently large to provide the power needed to analyze each SNP separately. Single nucleotide polymorphism genotypic values were recoded in a reversed direction for SNPs rs2496143 and rs11761076 so that high genotypic values for all SNPs indicate higher n = 2676) using a missing data option that substituted the population mean genotypic value for missing SNPs. SNP-set scores were normally distributed . Squaring this correlation indicates an effect size of 1.1% that is comparable to the sum of the effect sizes of the six SNPs, which, as noted earlier, was 1.2%. The correlation between SNP-set scores and g scores was essentially unchanged when the individuals with missing data were excluded . g score against the SNP-set scores for the sample of 2676 individuals. It can be seen that the association is approximately linear, which indicates additivity of the genotypic values in the SNP set. The standardized g score difference between SNP-set scores below 3 and above 8 is 0.24 SD, comparable to a difference of about 4 IQ points. This difference suggests that selecting on the basis of extreme SNP-set scores could be effective for selecting groups with a genetic liability for low or high g, although the associations of the individual SNPs yield small effect sizes.The correlation between SNP-set scores and g), six SNPs survived our two-stage screen for QTLs that are associated with g across the normal distribution. In stage 1, which compared allele frequencies derived from pooled DNA for low and high g groups, a composite of five criteria was used to nominate SNPs for individual genotyping in stage 2. In stage 2, for financial reasons, a single SNPlex multiplex of 47 SNPs was used to individually genotype an independent and representative sample of 3195 individuals. Using an alpha of 0.05 to provide a reasonable balance between false positives and false negatives in the search for QTLs of very small effect size, six SNPs yielded significant associations, whereas only two significant associations would be expected on the basis of chance.In this first genome-wide association scan employing approximately 500 000 SNPs for general cognitive ability (g using 10K SNP microarrays identified four SNPs (r2 = 1) a SNP  reported in our previous study; yet, it showed no association in the current study. Association may, however, have been masked by the between-group variance for rs11385352.Genome-wide association is a powerful and systematic tool for identifying QTL associations; however, a finding of general significance is that no associations greater than 0.5% were detected although the effective sample of >2500 provided 95% power to detect them. Given the increased SNP density of the Affymetrix 500K microarray, it is surprising that this result does not substantially improve upon our previous work in cognitive and learning abilities and disabilities using unmultiplexed assays as well as 10K and 100K microarrays  to detect an effect size of 0.2%. However, the composite SNP set could be tested for replication in much smaller samples. The SNP set of five SNPs has an effect size of about 1% of the total variance of g. An effect size of 1% would require a sample of about 780 to reach 80% power to detect an association between SNP set and g.Until the six SNPs reported in this study are replicated in other samples, caution is in order because their average effect size is only 0.2% of the variance of g, the genomic features surrounding the locations of the six SNPs are interesting. Three of the SNPs  are in known genes , but none are in coding regions; given that only 1.7% of the SNPs on the Affymetrix 500K microarray are in coding regions [Affymetrix annotation data (July 2007)], this is hardly surprising. One SNP (rs7195954) lies in a region of known copy number variation (CNV) and may warrant more detailed CNV analysis. One SNP (rs11761076) is in close proximity (27 kb) with a gene but shows no evidence of being in linkage disequilibrium (LD) with it. One SNP (rs11691504) lies in large gene desert with no currently documented functional elements nearby. Finally, and potentially most interesting, is the high LD between rs1378810 and the 3′ untranslated region (UTR) of DNAJC13, which contains nine predicted conserved mammalian micro RNA (miRNA) regulatory target sites. None of the SNPs show a high degree of sequence conservation with other species, which may indicate human-specific effects.Although none of the six SNPs are functionally related to g group and 10 pools from a high g group. By allelotyping these 20 DNA pools on 500K SNP microarrays, we can conduct much more thorough stage 2 screening. A representative sample will be individually genotyped in stage 3 to test for SNPs that pass the hurdles of stages 1 and 2.One important limitation is that only 47 of the SNPs nominated by SNP-MaP in stage 1 were individually genotyped and tested for replication in the independent stage 2. In the future, we plan to mine these SNP-MaP data further. However, rather than enduring the expense of individually genotyping large numbers of SNPs, our ongoing research has converted stage 2 to a second independent SNP-MaP screen comparable to the stage 1 SNP-MaP design with 10 independent DNA pools created from a low Another limitation is that more sophisticated ways of analyzing pooled DNA are now available, for example ‘GenePool’ . HoweverOur study may potentially be limited in six other ways:k-corrected allele frequency estimates. k- correction improves the accuracy of absolute estimates of allele frequency for pooled DNA .SNPs with minor allele frequencies less than 5% were excluded in the SNP-MaP screen for reasons explained earlier. Moreover, our multiple-criteria composite preferentially selected SNPs with high minor allele frequencies . It seemg do not exist. The power to detect effects is also contingent on the sensitivity of the SNP-MaP approach as well as the coverage of the 500K microarray  in g with age, caution should be exercised when interpreting the replication of these SNP associations at other (particularly much older) ages.Our sample was 7 years old and results might differ at other ages. Although quantitative genetic research indicates that genetics largely accounts for stability of http://www.ncbi.nlm.nih.gov/omim) shows that over 10% of searches for ‘mental retardation’ involved X-linked loci (g.Finally, our SNP-MaP screening was confined to the autosomes. An analysis of Online Mendelian Inheritance in Man (OMIM, ked loci , suggest


Chlamydomonas and Plasmodium. We have identified a HAP2/GCS1 gene in the genome sequence of the sponge Amphimedon queenslandica. This finding provides a continuous evolutionary history of HAP2/GCS1 from unicellular organisms into the metazoan lineage. Divergent versions of the HAP2/GCS1 gene are also present in the genomes of some but not all arthropods. By examining the expression of the HAP2/GCS1 gene in the cnidarian Hydra, we have found the first evidence supporting the hypothesis that HAP2/GCS1 was used for male gamete fusion in the ancestor of extant metazoans and that it retains that function in modern cnidarians.The HAP2/GCS1 gene first appeared in the common ancestor of plants, animals, and protists, and is required in the male gamete for fusion to the female gamete in the unicellular organisms  The evolutionary history of the machinery of sexual reproduction in metazoans has not yet been explored in detail. Particularly lacking is information from taxa that bridge the transition from unicellular organisms to metazoans. Choanoflagellates, the sister group to metazoans, have not been shown to undergo sexual reproduction ArabidopsisPlasmodium and ChlamydomonasHydra and NematostellaThe increasing availability of genome sequences from diverse metazoan taxa promises to allow a tracing of this history by examining genes encoding proteins involved in sexual reproduction. One molecule of particular interest in this regard is the product of the HAP2/GCS1 gene, a gene that was originally identified because of its expression in male gametes of plants and unicellular eukaryotes and its requirement for fertilization in Amphimedon queenslandica and examined expression of the HAP2/GCS1 gene in Hydra. Our finding that the HAP2/GCS1 gene is present in sponges and is expressed in spermatogenic cells of Hydra supports the hypothesis.To test this hypothesis we looked for evidence of a HAP2/GCS1 gene in the genome of the sponge Amphimedon queenslandica and the placozoan Trichoplax adhaerens have only recently become available, thus precluding a clear definition in previously published studies Amphimedon queenslandica. Alignment of the sponge sequence with the corresponding sequence from the sea anemone Nematostella vectensis shows that the positions of three introns are perfectly conserved between the two genes  expressing HAP2/GCS1, we examined HAP2/GCS1 expression in sexual Hydra by in situ hybridization Hydra vulgaris, which produces both sperm and eggs routinely in the laboratory. The HAP2/GCS1 gene is expressed in the spermatogenic cells in the testis but not in the cells forming an egg . Genome sequences from additional arthropods and expression data for the genes in Pediculus, Apis, and Tribolium are needed to clarify the history and function of HAP2/GCS1-related genes in these organisms. At the least, we can conclude that the history of HAP2/GCS1-related genes in arthropods is complex.The distribution of sequences encoding HAP2/GCS1-related proteins in arthropods is unusual. This distribution requires multiple losses of a HAP2/GCS1-related gene during radiation of the arthropods , and were also obtained from the NCBI trace archive.HAP2/GCS1-encoding sequences in the Apis, Pediculus, and Tribolium were aligned using T-Coffee http://tcoffee.vital-it.ch/cgi-bin/Tcoffee/tcoffee_cgi/index.cgi). The alignment was saved as a Phylip file and the graphic was generated using Version 6.2 of CLC Sequence Viewer (www.clcbio.com).HAP2/GCS1 amino acid sequences from Hydra HAP2/GCS1 clone from the male polyp cDNA library Hydra polyps were mounted in Euparal  prior to being photographed. Hybridization with a sense probe gave no signal.The insert of the


At the current price, the use of high-density single nucleotide polymorphisms (SNP) genotyping assays in genomic selection of dairy cattle is limited to applications involving elite sires and dams. The objective of this study was to evaluate the use of low-density assays to predict direct genomic value (DGV) on five milk production traits, an overall conformation trait, a survival index, and two profit index traits .Dense SNP genotypes were available for 42,576 SNP for 2,114 Holstein bulls and 510 cows. A subset of 1,847 bulls born between 1955 and 2004 was used as a training set to fit models with various sets of pre-selected SNP. A group of 297 bulls born between 2001 and 2004 and all cows born between 1992 and 2004 were used to evaluate the accuracy of DGV prediction. Ridge regression (RR) and partial least squares regression (PLSR) were used to derive prediction equations and to rank SNP based on the absolute value of the regression coefficients. Four alternative strategies were applied to select subset of SNP, namely: subsets of the highest ranked SNP for each individual trait, or a single subset of evenly spaced SNP, where SNP were selected based on their rank for ASI, APR or minor allele frequency within intervals of approximately equal length.RR and PLSR performed very similarly to predict DGV, with PLSR performing better for low-density assays and RR for higher-density SNP sets. When using all SNP, DGV predictions for production traits, which have a higher heritability, were more accurate (0.52-0.64) than for survival (0.19-0.20), which has a low heritability. The gain in accuracy using subsets that included the highest ranked SNP for each trait was marginal (5-6%) over a common set of evenly spaced SNP when at least 3,000 SNP were used. Subsets containing 3,000 SNP provided more than 90% of the accuracy that could be achieved with a high-density assay for cows, and 80% of the high-density assay for young bulls.Accurate genomic evaluation of the broader bull and cow population can be achieved with a single genotyping assays containing ~ 3,000 to 5,000 evenly spaced SNP. In genomic selection (GS), selection decisions are made on genomic breeding values predicted from high-density single nucleotide polymorphic (SNP) markers. In dairy cattle, GS has the potential to double the rate of genetic gain to that of traditional breeding schemes due to a substantial reduction in generation intervals and increased selection intensities ,2. SigniAt the current price, high-density SNP genotyping assays are limited to applications involving elite sires and dams. An alternative is to use a more cost-effective low-density assay for genotyping more animals from the population. As shown for a single trait by Weigel et al. , a low-dThe utility of low-density arrays will depend in part on the genetic architecture of the target trait. In GS, prediction equations are derived from a training set, where animals are phenotyped and genotyped to predict breeding values based only on the genotype information of evaluation animals. This requires that the markers are in sufficient LD with the QTL and simulation studies have shown that accuracy of genomic predictions increases as LD increases -10. In tUtility of SNP subsets will also be affected by the relationship of the selection candidates to the training set. Although genomic predictions rely on LD between SNP and QTL, this LD can operate or be interpreted at a number of levels. In addition to population level LD, simulation studies and empirical data have demonstrated that the accuracy of prediction depends on the relatedness between animals in the training and evaluation populations ,15,16. AThe objective of this study was to evaluate the use of low-density SNP genotyping assays to predict the direct genomic value (DGV) of bulls and cows for commercially important traits in Holstein-Friesian dairy cattle. The impact of two analysis methods, the number of SNP needed for accurate DGV prediction, as well as strategies for SNP selection were explored.Phenotype and genotype data were available on 2,144 Holstein-Friesian bulls and 510 Holstein-Friesian cows. The traits analysed included milk production traits , an overall confirmation trait , survival index, Australian Profit Ranking (APR) and Australian Selection index (ASI). The ASI is an index given by (3.8 × protein ABV) + (0.9 × fat ABV) - (0.048 × milk ABV), APR is given by (3.8 × protein ABV) + (0.9 × fat ABV) - (0.048 × milk ABV) + (1.2 × milking speed ABV) + (2.0 × temperament ABV) +  + (0.34 × cell count ABV) - (0.26 × live weight ABV) + (3.0 × daughter fertility), whereas survival is given by (0.5 × likeability) +  + (3.0 × udder depth) + (2.2 × pin set).http://www.adhis.com.au). The phenotypes used were deregressed breeding values (DRBV) for protein percentage, fat percentage, ASI, APR and survival, and daughter trait deviations (DTD) for protein yield, fat yield, milk yield and overall type. The deregression procedure removed the contribution of relatives other than daughters to the breeding values, as detailed in [Phenotype information was provided by the Australian Dairy Herd Improvement Scheme . After quality control  and omitThe 2,144 bulls were divided in a training data set of 1,847 bulls born between 1955 and 2004 and a validation set of 297 young bulls born between 2001 and 2004, which represented progeny test teams for 2007, 2008 and 2009. A second validation set included 510 cows born between 1992 and 2004. Table Prediction equations for each trait were derived from the training set by either ridge regression ,20 or paX is an incidence matrix that relates genotypes to individuals, and where DGV is the vector of direct genomic values estimated with the marker genotypes, Regression coefficients are obtained from the solution of the mixed model equationsN is the number of training animals, y is a vector of phenotypes, X is a (N × p) matrix of genotypes encoded as 0 (homozygote), 1 (heterozygote) or 2 (other homozygote), I is a p × p identity matrix. The penalty term λ, which is the same for all SNP, overcomes the problem of ill-conditioning when multicollinearity among columns in X causes X'X to be singular, or nearly so. The system of equations was solved iteratively by the preconditioned conjugate gradient method [λ within a given range. RR is equivalent to the BLUP method of Meuwissen et al. [λ = σ2e /σ2g, where σ2e is the residual variance and σ2g the genetic variance.where t method . The 10-t method , with got method , was usen et al.  and Habin et al. , which aIn RR, the contribution of each bull can be weighted according to the number of daughters contributing to the phenotype. However, reliabilities of the phenotypes expressed as 'equivalent daughter contributions' were uniformly high, with small differences between the majority of training bulls, and weighting the contributions of bulls had no impact on the accuracy of DGV for method RR (results not shown).X. A PLSR component t = w Xis a linear combination of the SNP that have maximal covariance with the response vector, under the additional assumption that components are mutually orthogonal [y is regressed on the linear combinations of markers.The main idea of PLSR is to build orthogonal components  from the original genotype matrix thogonal . SubsequDifferent algorithms to extract the latent components and to obtain regression coefficients ribed in . The optribed in . Note thribed in .λ for RR was estimated at each step by cross-validation.The absolute magnitude of the regression coefficients was used to determine which SNP are most influential in the training data set. To select subsets of markers, all 42,576 SNP were ranked by their absolute value of In detail, we first fitted models including all 42,576 SNP. In the first iteration 40,000 SNP with the highest absolute value of the regression coefficient were retained in the SNP list. The number of SNP subsequently dropped in each iteration was 2,000 for subsets of up to 10,000 SNP, 500 SNP for subsets of up to 1,000 SNP, 100 SNP for subsets of up to 300 SNP and 20 SNP for subsets of up to 100 SNP.n evenly spaced SNP, we divided the total length of the autosomes into n intervals flanked by two markers to give segments of approximately equal length. Chromosome lengths and SNP positions were based on the physical map of cattle genome assembly Btau 4.0. Subsequently, the highest ranked SNP for ASI (strategy 2), APR (strategy 3) or the SNP with the highest minor allele frequency  in each segment, was added to the subset. Using the same subset of SNP, a model was then fitted for each trait to derive the prediction equations. Subsets of evenly spaced SNP were generated for sets including between 100 and 5,000 SNP. The accuracy of DGV obtained using a subset of SNP was compared to the accuracy from the analysis of all 42,576 SNP.Four alternative strategies of SNP subset selection were compared. Under strategy 1, separate subsets including the highest ranked SNP for each individual trait were created. Strategies 2-4 used a single subset of evenly spaced SNP. To select a subset of Accuracy of DGV predictions in validation sets of young bulls and cows using all 42,576 SNP and subsets including the highest ranked SNP for each trait are shown in Figure Overall, predictions by RR were slightly more accurate for larger SNP subsets but less accurate for smaller SNP subsets compared to PLSR. As shown in Table 2 = 0.56), fat percentage (h2 = 0.52), and milk yield (h2 = 0.28), were more accurate than predictions of traits with a lower heritability, such as overall type (h2 = 0.18) and survival (h2 = 0.03).The panels in Figures Figure Results showing the accuracy of DGV using subsets of SNP selected by each of the four strategies are restricted to the analyses of subsets of 100, 300, 500, 1,000, 3,000 and 5,000 SNP. To limit redundancy, results from the analyses using RR are not presented in detail, but RR performed very similar to PLSR as shown in Figure When the number of SNP in the subset was 1,000 or larger, using trait-specific subsets gave higher accuracies than using a common subset of SNP in both validation sets, with the exception of overall type for both bulls and cows Figure  and 4. I2 = 0.03), particularly subsets of SNP selected for APR and ASI.For the overall type trait, subsets including more than 1,000 of the highest ranked SNP for the trait gave lower accuracies than evenly spaced SNP selected for ASI and APR, which might be due the smaller number of training records available for this trait. All subsets containing less than 500 SNP performed poorly for survival, which has a low heritability  and the = 0.03, ,33,34. RBoth, RR and PLSR performed very similar in predicting DGV and differences were generally small. However, the highest accuracy of prediction of PLSR was obtained with subsets including considerably fewer SNP than the high-density assay and fewer SNP than the best subset for RR. This might indicate that using PLSR is less appropriate when analysing very large numbers of SNP, although the differences between the maximum accuracy of DGV and the accuracy obtained with 42,576 SNP was small. A similar result has been found by Solberg et al.  who haveComparisons of accuracies across traits between validation sets of cows and bulls were constrained by the fact that for cows the accuracy of DGV prediction, computed as the correlation between DGV and DRBV, could not be calculated for five out of the nine traits, as DRBV information was not available for cows. A possible remedy would be to use the correlation between DGV and estimated breeding value, r, as a measure of accuracy instead. When we computed r in bulls and cows (results not shown) we found higher values of r in cows than in bulls, and using r would considerably overestimate the accuracy of genomic selection of cows. The higher correlation between DGV and EBV in cows is probably due to a larger contribution of shared information to the EBV through pedigree relationships.Pedigree relationships between training and validation animals might also have contributed to the accuracy of DGV in cows, with 473 (92.7%) of cows having their sire in the training set compared to 80.8% of the young bulls, and the training set containing more sires of cows N = 164) than sires of young bulls (N = 30). This could partly explain why for protein, fat and milk yield, the r of cows was similar in size as the r for bulls, although the phenotypes of bulls are derived from many more daughter records than the cow phenotypes. Furthermore, the records of cows are included in the sire phenotype. However, the effect of cows contributing information to the sire phenotype is expected to be small, with bulls in the training set which sired a cow in the validation set having on average 5,259 phenotyped daughters. A larger variance of the phenotypes for cows compared to the pre-selected bull teams  than all SNP on the high-density assay. For example, subsets containing 300 evenly spaced SNP selected for ASI by PLSR had a mean pMAF = 0.33 compared to pMAF = 0.30 for SNP selected by RR and pMAF = 0.27 for SNP selected on only their location. This suggests that selection of SNP should be based on their expected contributions to the genetic variance, which is a function of the allele frequency in the training set. This also implies that accuracy of prediction will be lower for a validation set where the distribution of allele frequencies does not resemble that of the training set and why prediction equations derived in one breed do not predict accurate DGV when applied to other breeds, as shown by Hayes et al. [Although ranking of SNP was based on the magnitude of the estimated SNP effects, SNP selected on their rank had a higher average minor allele frequency . The cuGenomic selection has become a routine in dairy cattle breeding programs worldwide. The current cost of whole-genome selection based on dense SNP genotypes has limited the application to the selection of elite males and females that are likely to become parents of the next generation. Results of our study indicate that accurate genomic evaluation of the broader bull and cow population can be achieved with genotyping assays containing ~ 3,000 to 5,000 SNP. A chip containing 3,000 evenly spaced markers can provide approximately 90% of the accuracy achieved with a high-density SNP assay for genomic selection of bulls and cows combined across traits. Possible applications include the selection of replacement heifers and the pre-screening of young bulls and potential bull dams. Assays with evenly spaced markers are preferable as they can be used across traits and possibly across populations. It also allows for a high volume generic chip to be produced, which will lower assay cost per individual and will limit heterogeneity of genomic information compared to using multiple assays for different traits. Evenly spaced low-density assays might also permit the reconstruction of the genotype information of high-density assays through imputation, which is important in situations where, for example, high-density genotyping is limited to nucleus breeding herds. Increasing the proportion of animals genotyped will further increase the accuracy of genomic selection as the training data grows over time, particularly through genotyping of cows.The authors declare that they have no competing interests.GM was the principal investigator in the design of the study and methods, carried out the statistical analysis and drafted the manuscript. MSK and BJH participated in the analysis, had a role in data acquisition, assembly and data QC and contributed to the manuscript preparation. HWR contributed to project design, data acquisition, and contributed to the manuscript preparation. All authors read and approved the final manuscript.Accuracy of DGV of bulls and cows using subsets of 5,000 or less of the highest ranked SNP obtained by RR and PLSR. Enlarged representation of Figure Click here for fileAccuracy of DGV of bulls whose sires were included (Sire) or were not included (No Sire) in the training set depending on the method of SNP selection. Accuracy of prediction is shown for subsets including the highest ranked SNP (Trait), subsets of evenly spaced SNP including the highest ranked SNP for ASI (ASI), APR (APR) or SNP with highest minor allele frequency (MAF) obtained by PLSRClick here for fileAccuracy of DGV of cows whose sires were included (Sire) or were not included (No Sire) in the training set depending on the method of SNP selection. Accuracy of prediction is shown for subsets including the highest ranked SNP (Trait), subsets of evenly spaced SNP including the highest ranked SNP for ASI (ASI), APR (APR) or SNP with highest minor allele frequency (MAF) obtained by PLSRClick here for file


In recent years, capabilities for genotyping large sets of single nucleotide polymorphisms (SNPs) has increased considerably with the ability to genotype over 1 million SNP markers across the genome. This advancement in technology has led to an increase in the number of genome-wide association studies (GWAS) for various complex traits. These GWAS have resulted in the implication of over 1500 SNPs associated with disease traits. However, the SNPs identified from these GWAS are not necessarily the functional variants. Therefore, the next phase in GWAS will involve the refining of these putative loci.A next step for GWAS would be to catalog all variants, especially rarer variants, within the detected loci, followed by the association analysis of the detected variants with the disease trait. However, sequencing a locus in a large number of subjects is still relatively expensive. A more cost effective approach would be to sequence a portion of the individuals, followed by the application of genotype imputation methods for imputing markers in the remaining individuals. A potentially attractive alternative option would be to impute based on the 1000 Genomes Project; however, this has the drawbacks of using a reference population that does not necessarily match the disease status and LD pattern of the study population. We explored a variety of approaches for carrying out the imputation using a reference panel consisting of sequence data for a fraction of the study participants using data from both a candidate gene sequencing study and the 1000 Genomes Project.Imputation of genetic variation based on a proportion of sequenced samples is feasible. Our results indicate the following sequencing study design guidelines which take advantage of the recent advances in genotype imputation methodology: Select the largest and most diverse reference panel for sequencing and genotype as many “anchor” markers as possible. In the last five years, the capabilities and technology for genotyping large sets of single nucleotide polymorphisms (SNPs) has increased significantly. Current genome-wide SNP arrays have the capability to genotype over one million SNP markers across the genome. This advancement in technology has led to an increased number of completed and on-going genome-wide association studies (GWAS) for various complex disease and drug-related phenotypes. These GWAS have resulted in more than 350 publications and over 1500 SNPs implicated for association with multiple (>80) disease phenotypes or traits Indirect association, as a result of linkage disequilibrium (LD), is a key factor in the success of genetic association studies. As a result of LD, a disease-susceptibility SNP need not be genotyped, as long as it is “tagged” by a SNP or set of SNPs that are genotyped . Recently this concept has been further exploited by the introduction of methods to impute genotypes at untyped markers, based on genotypes at typed markers and information about LD within the region One approach for following up replicated findings from a GWAS would be to determine all genetic variation within the locus, especially rarer variants not currently included on GWAS SNP arrays, as they may play an important role in the etiology of the disease http://www.1000genomes.org) In this manuscript we explore the use of the recently developed genotype imputation method implemented in MACH GENE1 (unpublished data). Little is known in regard to common genetic variations within GENE1, and even less is known in regard to the relationship of GENE1 genotypes with protein function or clinical phenotypes. In an attempt to address these issues, we have sequenced GENE1 in 288 samples from the Coriell Institute's publically available “Human Variation Panel” (http://ccr.coriell.org/Sections/Collections/NIGMS/Populations.aspx?PgId=177&coll=GM), consisting of 96 African American (AA), 96 Caucasian American (CA), and 96 Han Chinese American (HCA). DNA was extracted by the Coriell Institute from EBV transformed lymphoblastoid cell lines.To explore various approaches for imputation of untyped markers using a reference panel determined from sequencing data, we utilized a recently completed sequencing study for a gene which we will denote as GENE1 being a relatively short gene (5 kb), the entire gene was sequenced using Sanger sequencing technology. PCR primers were designed to span the entire gene plus 1 kb beyond the 5′ and 3′ UTR. PCR products were bi-directionally sequenced on an Applied Biosystems 3730 DNA Analyzer and analyzed with SoftGenetic's Mutation Surveyor software. Sixty variants were cataloged across the three racial groups; eleven of these variants were previously-known SNPs that have entries in dbSNP (http://www.ncbi.nlm.nih.gov/projects/SNP/). www.hapmap.org) or have a minor allele frequency (MAF)>1%.As a consequence of http://www.genome.gov/26524516). Once downloaded, we focused on the region of the genome where the gene COMT is located based on the March 2006 build of the human genome as shown on the UCSC genome browser at http://genome.ucsc.edu/ . Four-hundred and six SNP markers were determined to be in the CEU population, 517 SNP markers in the YRI population and 290 SNP markers in the JPTCHB population.The initial goal of the 1000 Genomes Project was to sequence the entire genome in approximately 1200 individuals as a means of documenting all human genetic variation. Recently, the number of samples to be sequenced has increased to over 2000 samples GENE1 and COMT. To assess the various approaches, we have created the following hypothetical experiment. A SNP marker within a gene  has been determined to be associated with a disease phenotype based on a large GWAS, in which all subjects have been genotyped for a set of SNPs on a large SNP array . The GWAS consists of subjects from three different racial groups. To follow-up the association findings, denser genotyping of the gene using sequencing technologies (Sanger or Next-Generation) was completed in a proportion of the individuals from each racial group. Based on the sequence data for the subset of the study participants, we wish to impute untyped markers for the remaining subjects using current genotype imputation methods. Based on previous reports regarding comparison of genotype imputation methods To explore various approaches for imputing untyped markers to augment sequence data, using a reference panel determined from sequencing a portion of the study participants, we will utilize sequence data available for GENE1 from the Mayo Study and COMT from the 1000 Genomes Project are outlined in the following sections.In assessing the use of sequence data for genotype imputation, the experiment varied: the proportion of samples sequenced (or the size of the reference data), the number of markers genotyped for all subjects (“anchor” SNPs) and how these markers were selected, imputation based on the sequenced participants (“reference panel”) unphased genotypes or most-likely phased haplotypes and imputation based on race specific reference haplotypes or all reference haplotypes (regardless of race). The various simulation scenarios investigated using a priori determined the samples to be considered the reference panel, using all of their sequencing data. For the rest of the sample considered the “observed” sample, non-anchor SNPs were masked, but retained and considered to be the “true” genotypes. These “true” genotypes could then be compared to the imputed markers arrived at using MACH's most likely genotype call, with anchor SNPs in common between the reference population and “observed” sample. In addition to computing the concordance rates based on most likely genotype call, we also used the –mask option in MACH to compute quality measures for the imputed markers and estimated MAF based on the expected genotype .To assess the accuracy of the various simulation scenarios, we In Scenario 2, imputation was completed by race (as for Scenario 1), but imputation was based on all reference haplotypes for all three races. To assess the variation in haplotype assignment and impact on imputation accuracy, imputation Scenario 3 was completed, by race, with only unphased genotypes for the sequenced samples . It should be noted that not all genotype imputation methods allow for the use of only unphased genotypes and may require reference haplotypes. Lastly, Scenario 4 assessed the impact of the number of “anchor” markers genotyped for all subjects, with either 3 markers (Scenario 4.3), 5 markers (Scenario 4.5), or 7 markers (Scenario 4.7) used in the imputation with the same design as Scenario 1.COMT study, based on data from the 1000 Genomes Project, the “anchor” markers were selected for two situations: a candidate gene study  and a genome-wide association study (Scenario 4) , while for COMT this scenario assessed the impact of how the “anchor” markers were selected: one based on candidate gene study in which SNPs were selected by tagging the region and one based on a priori defined SNPs on a GWAS SNP array. SNPs were defined to be on the GWAS panel if they were contained in HapMap, as many large GWAS SNP arrays are designed based on HapMap SNPs.For the nario 4) Table 2.COMT, tagging was completed by race using Haploview v4.1 2 of 0.8. This resulted in 125, 187 and 117 tagSNPs selected for CEU, YRI, and JPNCHB populations, respectively. These markers, for each race, were then taken to be the “anchor” markers genotyped on all samples in the candidate gene study. To mimic the case in which the “anchor” markers were on a genome-wide SNP array (Scenario 4), SNPs were chosen as any SNPs present in HapMap, resulting in 25 for the CEU population, 50 SNPs in the JPTCHB population and 32 SNPs in the YRI population To mimic the candidate gene scenario, we consider the common situation where the anchor markers, genotyped on all subjects, would be LD based tag SNPs. Therefore, to determine anchor markers for GENE1, using Sanger Sequencing technology, detected five novel variants with MAF greater than 5% and 20 novel variants with MAF greater than 1% not previously reported (GENE1 was completed using MACH 1.0 (http://www.sph.umich.edu/csg/abecasis/mach/index.html) using the commands: mach1 -d markers.dat -p pedigree.ped -h hap.haplos -s all.snp --rounds 150 --greedy --mask 0.02 --geno --dosage --quality -o machOut for scenarios using reference haplotypes and mach1 -d markers.dat -p pedigree.ped --rounds 150 --greedy --mask 0.02 --geno --dosage --quality -o machOut for the scenarios in which only unphased genotypes were used in the imputation (Scenario 3). Percent of concordant genotype calls was computed by comparing the imputed genotypes to the “true” observed genotypes.Sequencing of reported Table 1.Lastly, in general, as the proportion (or number) of samples sequenced increased , the quality of the imputation increased. Thus, not only does number of markers genotyped on all subject impact the accuracy of imputation, so does the size of the reference sample. In addition to variation in results due to the number of markers genotyped on all subjects and size of the reference set, two different reference sets of the same size  resulted in slightly different imputation accuracy (data not shown).COMT was completed using MACH 1.0 using the commands using the commands: mach1 -d markers.dat -p pedigree.ped -h hap.haplos -s all.snp --rounds 150 --greedy --mask 0.02 --geno --quality -o machOut for all scenarios.The various scenarios for genotype imputation using the 1000 Genomes Project sequence data for polymorphic markers in GENE1, we looked at the SNP concordance rates and SNP quality score for COMT for the various imputation scenarios , in terms of both concordance rates and minimum SNP quality scores. In terms of highest minimum quality score, imputation based on reference haplotypes for all races (Scenario 2) was the “best”; however in terms of concordance between the observed and imputed most likely genotype, imputation based on race specific reference haplotypes was “best” (Scenario 1). The figures also show a slight increase in imputation performance as the size of the reference panel increased.Lastly, with one goal of sequencing to be to detect rare variants, we compared, for the three races, the relationship between the estimated MAF, based on the imputed dosage, after imputation and the quality of imputation Figure 5In this manuscript present the use of the recently developed genotype imputation method for sequencing studies, where the reference panel consisting of sequencing data for a fraction of the study participants. By sequencing only a portion of the samples for the follow-up of signals detected from a GWAS, followed by imputation in the remaining samples, one can significantly reduce the cost to localize the punitive variant involved in the etiology of complex disease and pharmacogenomic phenotypes. In addition, by utilizing sequence data on a portion of individuals in the study, we are able to have a perfectly matched reference panel, in terms of linkage disequilibrium, without relying on the assumption that the HapMap populations represent our study population (as HapMap based haplotypes are the current standard reference data used for genotype imputation).http://www.1000genomes.org), incorporation of this information can also be utilized to determine variants not already identified in public databases. However, one limitation of the use of 1000 Genomes project for imputation for all subjects is the possibly that this “healthy” cohort does not adequately represent the genetic diversity observed in the affected individuals . Thus, there will still be a need to sequence individuals, in particular, those individuals with the disease or phenotype of interest.Sequencing a portion of our study population also allows us to determine and assess association of rare variants not present in the HapMap database. Upon completion of the 1000 Genomes project Given that sequencing produces unphased genotypes, if possible, imputation should be carried out on the unphased genotypes in the reference panel as opposed to the most likely phase haplotypes to account for the uncertainty in haplotype assignment.Genotype as many “anchor” markers as possible, in that, the number of markers genotyped on all subjects impacts accuracy. Therefore, additional genotyping of a few common SNP markers not already genotyped on all subjects using a cost effective platform, like Taqman, may be needed if the GWAS SNP array does not provide adequate coverage in the locus to be sequenced.


There has been considerable effort focused on developing efficient programs for tagging single-nucleotide polymorphisms (SNPs). Many of these programs do not account for potential reduced genomic coverage resulting from genotyping failures nor do they preferentially select SNPs based on functionality, which may be more likely to be biologically important.r2 linkage disequilibrium between single nucleotide polymorphisms (SNPs) to select tagSNPs. Snagger distinguishes itself from existing SNP selection algorithms, including Tagger, by providing user options that allow for: (1) prioritization of tagSNPs based on certain characteristics, including platform-specific design scores, functionality , and chromosomal position, (2) efficient selection of SNPs across multiple populations, (3) selection of tagSNPs outside defined genomic regions to improve coverage and genotyping success, and (4) picking of surrogate tagSNPs that serve as backups for tagSNPs whose failure would result in a significant loss of data. Using HapMap genotype data from ten ENCODE regions and design scores for the Illumina platform, we show similar coverage and design score distribution and fewer total tagSNPs selected by Snagger compared to the web server Tagger.We have developed a user-friendly and efficient software program, Snagger, as an extension to the existing open-source software, Haploview, which uses pairwise Snagger improves upon current available tagSNP software packages by providing a means for researchers to select tagSNPs that reliably capture genetic variation across multiple populations while accounting for significant genotyping failure risk and prioritizing on SNP-specific characteristics. CYP19. Subsequent haplotype tagging SNPs were then genotyped in a larger case-control second-stage sample examining the association with breast cancer ), MAF (MAFm), functionality , and chromosomal position .A SNP Score is assigned to each potential tagSNP in set P. It is a function of the SNP's probability of genotyping success .WGS, WMAF, WT, WL) are applied to each parameter, and can be modified by the user. If desired, the user can preferentially weight parameters so that tagSNPs having specific characteristics are more likely to be selected. The default value for each weight is zero except for the probability of genotyping success weight, where the default value is one. Thus, without user-specified weightings across parameters, SNP Scores only rely on genotyping success.Weights . Initially, i pis added to its own BinB. Then, each SNP ini p's BinA is sequentially added to its BinB if it is contained in the BinA of every SNP currently ini p's BinB.A secondary bin, BinB, is created for each SNP TagSNP picking starts by selecting the SNP from each BinB with the highest SNP Score and adding it to a temporary set, Q. From this narrowed set of potential tagSNPs, the SNP with the most SNPs in its respective BinA, t, is picked as a tagSNP and added to the set T. This tagSNP is removed from every BinB as well as the set of potential tagSNPs, set P. All SNPs in LD with t are removed from every BinA and the remaining set of SNPs to be captured, set C. This algorithm is repeated until either set C or set P become empty. If set P is empty, but set C is not, any SNPs remaining in set C are marked as "untaggable". Singletons are handled in the same way in the selection process, but become untaggable  if they violate a minimum tagSNP distance or design score requirement.r2, design score, and surrogate picking parameter specifications  that is derived from the Pr [GS] of all chosen tagSNPs that can act as a proxy for that SNP. Given a SNP m with n tagSNPs:The first method uses the probability of genotyping success, Pr  Figure . As tagSCPS on all SNPs whose corresponding tagSNPs capture a minimum number of SNPs. With this method, a SNP is not considered captured until its CPS meets or exceeds the user-defined threshold, its tagSNP's "captured" number is less than the cut-off, or there are no surrogates available. The software provides default values of 0.999 and 8 for the minimum CPS and minimum "captured" number of SNPs, respectively. The possible values for Pr [GSm] when using default parameters and Illumina design scores range from 0.43 to 0.98. It follows that any tagSNP predicting at least eight SNPs will at minimum require one surrogate tagSNP in order to achieve a CPS of 0.999 for the predicted SNPs. If either the tagSNP or surrogate has a sufficiently low design score that the threshold is not met, more surrogates will be selected as long as they are available. The default values are chosen so as any SNP in a large bin will have only one chance in a thousand that all the tagSNPs predicting it will fail.The user can enforce a minimum The second method relies on a function that gives the required number of surrogates based on the how many SNPs a tagSNP is tagging Figure :1 and Θ2 values specified by the user. Every time a tagSNP is chosen, the above function is evaluated to check if and how many surrogates should be added. The surrogates are chosen from the tagSNP's BinB and added to the list of tagSNPs.where T is the number of surrogates needed and M is the number of SNPs tagged by a tagSNP, with the ΘSnagger's preferential selection of tagSNPs was evaluated using HapMap Public Release 21a genotype data [For simulation purposes, we randomly marked one-eighth of all potential tagSNPs  as located in a coding region of the chromosome, which in practice would include both synonymous and nonsynonymous SNPs. The remaining 6,543 SNPs were marked as non-coding. In calculating the SNP Score, SNPs in the coding region received a weight of 1 and all other parameters had a weight of 0. Snagger selected 1,323 tagSNPs, of which 457 (34.5%) were "coding" SNPs. Favorable weighting for coding SNPs increased the proportion of tagSNPs located in a coding region nearly three times , and of all potential coding region tagSNPs, nearly half were selected (457 of 936).We also compared tagSNPs selected for the HapMap CEPH population across 10 ENCODE regions, using a SNP Score with preferential weighting of MAF in the HapMap Yoruba  population to no weighting at all. This was done in order to demonstrate the ability of Snagger to preferentially pick tagSNPs in one population  which are common in another , such that resulting genotypes could potentially be compared in the future to the ungenotyped population. An MAF weight of 1 (for both CEPH and Yoruba populations) on the SNP Score and weights of 0 on all other parameters resulted in 21 percent of picked tagSNPs having an MAF between 0.4 and 0.5 in the Yoruba population. When not weighing on Yoruba MAF, only 12 percent of picked tagSNPs had an MAF between 0.4 and 0.5 in the Yoruba population. Also of note is the reduction in monomorphic tagSNPs in the Yoruba population from 19% with no weighting to 16% with weighting. The distribution of Yoruba MAFs for the chosen tagSNPs is shown in Figure The efficiency and coverage of tagSNPs selected by Snagger and the web server Tagger were compared using the same HapMap SNP data and ENCODE regions Table . Design In terms of the number of tagSNPs selected, Snagger was more efficient than the web server Tagger. Across every ENCODE region, ten to thirty fewer tagSNPs were selected by Snagger than Tagger Table .. TagSNPTo evaluate Snagger's ability to select tagSNPs outside of a targeted region, we looked across 76 gene regions. There were a total of 6282 common (MAF = 0.05) SNPs targeted for capture using CEPH and Han Chinese genotypes from HapMap Public Release 21a .Choosing tagSNPs only from within each region yielded 1702 tagSNPs with an average Illumina design score of 0.902 . In either CEPH or Han Chinese populations, 75 SNPs were untaggable because they were within 60 base pairs of another tagSNP or had a design score equal to zero and could not be captured by any other tagSNP. In CEPH and Han Chinese populations there were 56 and 36 untaggable SNPs, respectively.When we allowed tagSNPs to be picked from outside the region there were 1731 tagSNPs selected with an average design score of 0.917. In either CEPH or Han Chinese populations, there were 61 untaggable SNPs, and in CEPH and Han Chinese populations there were 45 and 28 untaggable SNPs, respectively.The development and implementation of tagging SNP selection methodologies have received significant attention in recent years. Our program, Snagger, improves upon other tagSNP picking software by combining preferential tagSNP picking and the ability to select tagSNPs across multiple ethnic populations into one software package. With features not available in other software, including surrogate tagSNP picking to offset the risk of failed assays and the ability to pick better tagSNPs from outside a targeted region, Snagger improves coverage of genomic variation. Like Tagger, Snagger adds flexibility by allowing the user to force-include or force-exclude user-defined SNPs. The software is built on the basis of Haploview's framework, making it both familiar and graphically appealing to the user, and includes Haploview's LD plot and haplotype display, which allows the user to visually investigate patterns of variation.r2 ≥ 0.8). Snagger distinguishes itself from Tagger by including a second step. To preferentially select SNPs with certain characteristics, Snagger creates a set of bins (BinB) with SNPs that are in LD with every other SNP in the bin. Calculated from a number of user-specified parameters, a SNP Score is assigned to each SNP. The SNP in each BinB with the highest SNP Score becomes the potential set of tags to pick from. Thus, a SNP Score that can be flexibly weighted allows the user to influence the characteristics of chosen tagSNPs. The second set of bins maximizes coverage while minimizing the number of tagSNPs selected. From our evaluation, we show that Snagger on average selects fewer tagSNPs than the web server Tagger when preferentially selecting tagSNPs on design score.Our program, Snagger, has some similarities to the algorithm used by Tagger. Both programs create a bin (BinA in Snagger) for each SNP that contains the set of SNPs in high LD  or are located in a 5' or 3' untranslated region are suspected to have a greater likelihood of having a biological effect ,24. The Another key feature of Snagger is the ability to weigh by probability of successful genotyping on specific high-throughput platforms. Genotyping failures can reduce effective genomic coverage, especially when tagSNPs acting as a proxy for many SNPs fail. Snagger addresses this by preferentially choosing tagSNPs with high probabilities of success, while maintaining efficiency in the number of tagSNPs selected. Though it may be necessary for the software to pick tagSNPs with lower probabilities of success in order to capture every SNP, the user can enforce a minimum design score for all tagSNPs. For reference, 18% of HapMap SNPs in the ten HapMap ENCODE regions have a probability of success below 0.776, which corresponds to an Illumina design score of 0.4, the default minimum. In addition, Snagger can select surrogate tagSNPs that will backup low-scoring tagSNPs that act as a proxy for several SNPs. Furthermore, since some genotyping platforms  require that all tagSNPs being genotyped have a minimum base pair distance, Snagger can enforce a minimum distance between tags, which further reduces the chance of genotyping failure. We compared our program to the web server Tagger and show that the tagSNPs chosen by Snagger had comparable, if not higher design scores than those selected by Tagger.Snagger's ability to select tagSNPs across multiple populations in a user-friendly manner is advantageous for studies involving multi-ethnic cohorts and admixed populations. Other software programs have focused on the most efficient way to select tags including TAGster , but do We developed a software application, Snagger, to select an efficient set of tagSNPs that captures the most genetic information and can reliably be genotyped. It is freely available and we include the executable : Platform independentProgramming language: JavaOther requirements: Java Runtime Environment 1.4.2_12 or higherLicense: MIT LicenseAny restrictions to use by non-academics: NoneCKE carried out the software engineering effort and performance analysis and participated in the drafting of the manuscript. WHL participated in the development of an alpha version of the program and the drafting of the manuscript. DL participated in the development of an alpha version of the program. DJV and DVC supervised the development of the program and the drafting of the manuscript. All authors read and approved the final manuscript.Input:1, s2,..., sm} within a contiguous genomic region.• A set of SNPs S = {sr2 values for each pair of SNPs in S having a physical distance less than a user-specified threshold, such that the pairwise r2 value of two SNPs si and sj is defined as: r2.• A table containing 1, i2,..., in} to force-include as chosen tagSNPs, where I ⊆ S.• A set of SNPs I = {i1, e2,..., eo} to force-exclude from being chosen as tags, where E ⊆ S.• A set of SNPs E = {er2 minimum threshold defined as: r2min. All tagSNP-SNP pairs must have a pairwise r2 value that meets or exceeds this threshold.• A user-specified SS based on SNP design scores, other annotations, and user-defined weights.• A SNP Score function Output:1, t2,..., tr} such that T ⊆ S and each t ∈ T tags a subset of SNPs in S.• A set of tagSNPs T = {t1, u2,..., us} such that U ⊆ S and no tag SNP in T tags any SNP in U.• A set of "untaggable" SNPs U = {uAlgorithm:1, c2,..., cp} be the remaining set of SNPs to capture, such that C initially contains the SNPs in S that are located within the region of interest.1) Let C = {cC ⊆ S.2) Add all force-included SNPs to the final list of tagSNPs.For each ii ∈ I, add ii to T.3) Remove all SNPs from the set of SNPs that still need to be captured those SNPs that are tagged by the set of force-included tagSNPs.For all possible pairs of ti ∈ I and cj ∈ C, if r2 ≥ r2min, remove cj from C.4) Determine the remaining set of SNPs that can possibly be tagSNPs.1, p2,...,pq} be the set of potential tagSNPs such that P = S - E - ILet P = {p.r2 values.5) Determine the set of SNPs for which each potential tagSNP can act as a proxy based on their pairwise For each pi ∈ P, create a BinA such that p.BinA ⊆ S. For each cj ∈ C where r2 = r2min, add cj to p.BinA.6) For each potential tagSNP, find a set of potential tagSNPs that can act as proxies for it and every other potential tag in the set.For each pi ∈ P, create a BinB such that p. BinB ⊆ P and pi ∈ p. BinB, where all possible pairs of bj ∈ p. BinB and ck ∈ p. BinB, r2 ≥ r2min.SS  and add it to a temporary set.7) For each potential tag, determine its best proxy according to the user-defined scoring function Let Q = {}. For each pi ∈ P, let pi-best be the SNP with the highest SNP Score, SS, in pi.BinB. Add pi-best to Q.8) From the temporary set of best proxies, choose the SNP that tags the most number of SNPs and add it to the final set of tagSNPs.Let t be the SNP in Q with the largest BinA. Add t to the set of tagSNPs T.9) Create a set of SNPs that will be removed from the list of potential tags.Let R = {}. Add t to R.10) Remove all the newly tagged SNPs from every potential tagSNP's BinA. If a potential tagSNP's BinA becomes empty, it can no longer be a tag and should be removed.For each pi ∈ P, p. BinA = p. BinA - t. BinA. If pi.BinA = {}, add pi to R.11) Remove all newly tagged SNPs from the set of SNPs that are left to be captured.Let C = C - t. BinA.12) Remove all the potential tags that were marked for removal from every potential tagSNP's BinB.For each pi ∈ P, let pi.BinB = pi.BinB - R.13) Remove all the potential tags that were marked for removal from the set of potential tagSNPs.Let P = P - R.14) If there are no more SNPs to capture or there are no more potential tagSNPs to choose from , the tagSNP picking is done. Otherwise, choose the next tagSNP by repeating from Step 7.15) Mark any SNPs that still need to be captured as untaggable.Let U = C.Haploview with Snagger as an executable JAR file. The file is contained inside a .zip file, and must be extracted before it can be accessed. This file contains the program Snagger as an extension to Haploview version 3.3. It can be executed on any operating system with the Sun Java Runtime Environment version 1.4.2_12 or higher installed [nstalled . It doesClick here for fileArchive of source code for Haploview with Snagger. This file contains an archive of the source code for Haploview with Snagger. It is compressed using the GNU Tar and GNU zip (gzip) archive utilities inside a .zip file, in that order. To extract the archive with Windows, use WinZip [e WinZip . For Unie WinZip  then unte WinZip . For comClick here for fileHaploview with Snagger User Guide. This file contains a user guide in PDF format with instructions on how to use Snagger. General Haploview instructions not relating to Snagger are available from the Help menu of the program.Click here for fileSample score file. This file is an example score file that will work with Snagger. It should be input in the software's window titled: "Load Score File and HapMap Data".Click here for fileArchived folder of sample HapMap data. This file contains an archived folder of sample HapMap data that corresponds with the SNPs in the sample score file. It is in GNU Tar format and should be uncompressed to a local hard disk before using. The resulting uncompressed folder should be specified in the software's window titled: "Load Score File and HapMap Data". To extract on Windows, use WinZip [e WinZip . For Unie WinZip .Click here for file