Gadus morhua) is a species with increasing economic significance for the aquaculture industry. The genetic improvement of cod will play a critical role in achieving successful large-scale aquaculture. While many microsatellite markers have been developed in cod, the number of single nucleotide polymorphisms (SNPs) is currently limited. Here we report the identification of SNPs from sequence data generated by a large-scale expressed sequence tag (EST) program, focusing on fish originating from Canadian waters.Atlantic cod . 3072 SNPs were selected for testing. The percentage of successful assays was 75%, with 2291 SNPs amplifying correctly. Of these, 607 (26%) SNPs were monomorphic for all populations tested. In total, 64 (4%) of SNPs are likely to represent duplicated genes or highly similar members of gene families, rather than alternative alleles of the same gene, since they showed a high frequency of heterozygosity. The remaining polymorphic SNPs (1620) were categorised as validated SNPs. The mean minor allele frequency of the validated loci was 0.258 (± 0.141). Of the 1514 contigs from which validated SNPs were selected, 31% have a significant blast hit. For the SNPs predicted to occur in coding regions (141), we determined that 36% (51) are non-synonymous. Many loci  are polymorphic in all populations tested. However a small number of SNPs (184) that are polymorphic in the Western Atlantic were monomorphic in fish tested from three European populations. A preliminary linkage map has been constructed with 23 major linkage groups and 924 mapped SNPs.These SNPs represent powerful tools to accelerate the genetic improvement of cod aquaculture. They have been used to build a genetic linkage map that can be applied to quantitative trait locus (QTL) discovery. Since these SNPs were generated from ESTs, they are linked to specific genes. Genes that map within QTL intervals can be prioritized for testing to determine whether they contribute to observed phenotypes. Gadus morhua) stocks declining dramatically over the last few decades [With many wild Atlantic cod  can then be employed by selecting broodstock based on genotypes at QTL that are relevant to economically important traits such as rapid growth, disease resistance and the control of early maturation. Currently, a limited collection of genetic markers is available for Atlantic cod, including restriction fragment length polymorphisms (RFLP), microsatellites and single nucleotide polymorphisms (SNPs) -9. Many The Cod Genomics and Broodstock Development Project (CGP) recently produced 158,877 expressed sequence tags (ESTs)  using a Putative SNPs identified in this study were subsequently validated for polymorphism across a number of geographically diverse Atlantic cod populations, ranging from Canada to the North-East Atlantic . These SNPs were also tested for Mendelian segregation in two families, and used to create a high-density genetic linkage map that can be applied in QTL analysis to facilitate cod broodstock selection.ESTs from the CGP collection were generated using several individuals per library to ensure that a significant proportion of the genetic diversity present in two Atlantic cod populations was captured. These populations originated from Cape Sable (off the southern tip of Nova Scotia) and from Bay Bulls, Newfoundland, and individuals from these collections had been used as parents in breeding programs based in New Brunswick and Newfoundland respectively. Two main methods were used for library generation; normalization of cDNA followed by directional cloning ,27, withThe software package Paracel Transcript Assembler (PTA) was used to cluster the EST set. This software groups similar sequences together into clusters, and then attempts to assemble sequences within a cluster . Thus clSearching 13448 contigs using an automated pipeline based on PolyPhred yielded a total of 170365 predicted SNPs  were chosen such that only one SNP per contig was included. The second panel selection consisted of various categories of SNPs chosen based on a prioritized strategy. Initially, SNPs from remaining contigs not represented on the first panel were selected. The panel was then completed by selecting SNPs that are neighbours on the same contig to SNPs that failed, or were monomorphic, on panel 1, and also SNPs that were neighbours to successful SNPs on panel 1  but having a different haplotype. A few SNPs were selected manually based on contig annotations, with some of these identified on SSH EST contigs. Thus the final set of validated SNPs (1620) was selected from 1514 contigs, with several contigs having multiple validated SNPs. These can be identified in the SNP set as they have an identical number, but a different suffix, as in cgpGmo-S177a and S177b for example.SNPs neighbouring failed panel 1 SNPs had slightly lower success rate (69%) when compared to other SNP categories, which ranged from 72 to 79%. Analysis of the polymorphism of successful panel 2 SNPs showed that SNPs neighbouring panel 1 failures have a higher number of polymorphic SNPs (78%) when compared to the "unique SNPs/contig" category (71%). Two categories showing the smallest number of polymorphic loci are the manually picked SNPs (35%) and the neighbours of monomorphic panel 1 SNPs (47%). Some of the manually picked SNPs were selected from SSH libraries which had each been generated from a single family, and thus this subset may contain a greater proportion of SNPs which are rare within the population as a whole.-05) in the NCBI non-redundant (nr) dataset. In total, 514 SNPs (32%) were associated with sequences having significant similarity to an entry in the NCBI nr database  had a significant blast hit . Of these 141 SNPs, 90 (64%) were predicted to generate synonymous substitutions, while 51 (36%) were non-synonymous. For non-synonymous SNPs, the resulting amino acid changes are shown in Additional file We present here a description of SNP characteristics in several populations of Atlantic cod Table . The invA few of the polymorphic SNPs were not in Hardy Weinberg equilibrium (HWE) in one or more of the four Canadian populations tested  were tested by genotyping the parents and progeny from 2 CGP families, to ensure that SNPs can be used reliably for linkage analysis. In each case, patterns of segregation were assessed in the 91 progeny genotyped for each family. Different, overlapping sets of SNPs could be assessed for segregation in each of the two families. In family B33 it was possible to examine the inheritance for 858 SNPs, whereas 832 SNPs were informative in family B87 , with a further three SNPs showing departure from Mendelian segregation in family B33 but segregating correctly  in B87 . However, the majority of the SNPs in this second category could be successfully placed on the linkage map.The generation of genomics resources within the CGP program is tightly integrated with family-based selective breeding programs based in New Brunswick and Newfoundland. As part of these programs, individual crosses are generated with known parental contribution, with the progeny from these crosses reared in separate tanks until they reach a suitable size for surgical implantation of a passive integrated transponder tag. Parents and 91 progeny from each of two independent crosses, families B33 and B87, were genotyped using the two Illumina GoldenGate panels described in this study  described earlier, JoinMap®4  was used®4 . A small describeOur goal was to develop a large collection of SNP markers from contigs produced by the CGP. After screening 13448 contigs generated from 97976 ESTs, we identified 4753 SNPs using the criteria of 4 reads minimum and a MAF > 25%. Assays have been developed for 3072 SNPs using 465 fish, which were genotyped using a Golden Gate assay. The success rate for this set of SNP assays was 75%. The SNPs were assessed for polymorphism by testing against Canadian and European populations and it was determined that 26% of SNPs were monomorphic. A large majority (1610) of the validated polymorphic SNPs described here are novel. However, on analysis, 10 SNPs in our collection were found to be identical to SNPs identified independently in a previous study . These SOncorhynchus keta  or in Oncorhynchus tshawytscha  [The frequency of our set of selected SNPs in Atlantic cod is 1/516 bp, which is similar to the frequency reported in Atlantic salmon of 1/614 bp [1/301bp) . SNP ideTo maximize the detection of real SNPs that were frequently variable in fish enrolled in the CGP breeding programs, stringent criteria were used to reduce the likelihood of selection of false or rare SNPs. By selecting SNPs where the minor allele was represented by at least two reads, we hoped to generate a set of markers that would prove useful for gene mapping and parental assignment. As a consequence, our SNP collection has been selected to prioritise SNPs that are frequently polymorphic in the populations being used for selective breeding in Atlantic Canada, and in related populations from Atlantic Canadian waters. Therefore, our set of SNPs may prove to be less informative for the analysis of populations with different geographical distribution, such as populations originating in North-East Atlantic. The fact that we found 184 SNPs that are polymorphic in Canadian populations but monomorphic in North-East Atlantic populations  and Newfoundland (Bay Bulls), Canada. By testing these SNPs against more eastern populations such as Ireland, Iceland and Norway, we have shown that they are also informative as markers across more geographic distant populations. Some SNPs (184) were found to be polymorphic only in all Canadian populations, and therefore have the potential for use as traceability markers.By genotyping two reference families, SNPs were checked for Mendelian segregation. The 64 SNPs that were removed from the validated set showed a significant departure from Mendelian segregation; they are more likely to occur on paralogous genes than to represent alleles since both parents and progeny were heterozygous. This is not uncommon when identifying SNPs in fish. In most studies around 2-4% of validated SNPs are assumed to be duplication SNPs  except fSchistosoma mansoni [Anopheles funestus [Salmo salar (82%) [Most of the SNPs described here are predicted to fall within non-coding sequence. This is expected in our dataset as all of the ESTs used in SNP identification were sequenced from the 3' direction, and thus the majority of each sequence is likely to represent the 3'untranslated region. Nevertheless, a minority of the SNPs identified here are predicted to occur in coding regions. The remaining SNPs are either in non-coding sequence, or on contigs with no significant sequence similarity. For the SNPs found in coding regions, only a subset of the polymorphism will result in a variation in the amino acid sequence of the encoded protein (i.e. the non-synonymous substitutions). SNP studies have reported a higher number of synonymous SNPs (sSNPs) when compared to non-synonymous SNPs (nsSNPs); the variation at non-synonymous sites has the potential to be associated with deleterious mutations. A higher number of sSNPs is usually observed, and this is likely to be the result of evolutionary constraints preferentially eliminating variation at non-synonymous sites. For example, 80% of SNPs identified in coding regions in chicken  are syno mansoni , 68% forfunestus , 60% forfunestus , and 55%funestus . An evenar (82%) . We foun® [A preliminary linkage map has been constructed using the SNPs presented here. This map has been generated using the cross-pollination (CP) parameter set of JoinMap4® , which i® ,40. IndePrior to this analysis, Moen and colleagues  describeWe can generate separate male and female maps for most of the genome of Atlantic cod using the two families genotyped on the two SNP panels described here. The majority of the linkage groups in the consensus map could be identified in sex-specific maps, however these maps are less dense and, due to their bi-allelic nature, only a few informative SNPs are common between maps created with a single individual, making the merging of maps problematic. However, although there appears to be a significant difference in the recombination rates between male and female Atlantic cod , this haThis large collection of SNPs for Atlantic cod should prove of great utility for both the aquaculture industry, and for the management of wild fisheries. As improved automated genotyping systems have been developed, SNPs have become important markers for commercial diagnostics and parental genotyping applications. Due to lower individual information content, a higher number of SNPs is required for parental assignment  when comThe SNP collection presented here has been completed in parallel with the development of a 20000 feature, oligonucleotide microarray. In the CGP microarray, 1391 features have a validated SNP marker, which will lead to the association of features showing interesting transcriptional responses with QTL intervals, potentially providing useful tools for MAS. For example, cgpGmo-S1123 is located in the sequence coding for 3-oxo-5-beta-steroid 4-dehydrogenase (AKR1D1). This gene belongs to the Aldo-keto reductase family 1, member D1 and catalyzes the reduction of progesterone, androstenedione, 17-alpha-hydroxyprogesterone and testosterone to 5-beta-reduced metabolites, as well as playing a role in bile acid biosynthesis . This geThe SNPs described here have been derived from ESTs, and thus can provide anchor points for more extensive comparative genomic analyses. A second generation SNP array (1536 SNPs) has now been created by selecting SNPs from these two initial panels (CGP panels 1 and 2) to produce a third Golden Gate panel comprising validated, polymorphic SNPs (CGP panel 3). This is currently being used for QTL detection.Gadus morhua. The SNPs have been validated across a panel comprising several populations of wild cod, and using two family crosses. This large collection of SNPs will be valuable for developing diagnostic assays to distinguish between cod populations, as well as producing tools useful for the aquaculture industry. A dense genetic linkage map has been constructed using these SNP markers and will provide a valuable resource for QTL discovery and MAS.We present an extensive resource of SNP markers for Atlantic cod, The creation and the sequencing of the Atlantic Cod Genomics and Broodstock Development Project (CGP) EST libraries have been described in detail elsewhere . Librarihttp://ri.imb.nrc.ca/codgene.All EST sequences used in this study have been deposited in GenBank . AnnotatAutomatic SNP detection has been described in detail previously . ContigsIn total, 5 × 96 well plates of selected DNA samples were genotyped using the two Illumina Golden Gate panels. Two plates consisted of two references families, B33 and B87 with two parents and 91 progeny. The three remaining plates consisted of wild cod populations. In total, seven populations of Atlantic cod were genotyped for this study, with an average of 23 fish genotyped per population. The geographic location of collections covers the North Atlantic with a more detailed sampling for Atlantic Canadian populations. DNA extraction methods have been described previously . In summHigh-throughput genotyping was performed at the McGill/Genome Quebec Innovation Centre using the GoldenGate assay.-05 used as the cutoff to determine significance. All SNPs that were determined to be polymorphic after testing have been deposited in the GenBank SNP database under accession numbers ss131570222 to ss131571915  were identified in each of four Canadian populations of Atlantic cod. This was assessed separately in each population using Hardy-Weinberg exact tests calculated using GenePop v4.0 . Loci wi®4. Genotypes for progeny generated through the Illumina GoldenGate platform were converted to CP codes based on parental genotypes. Each cross was examined separately, with segregation ratios analysed for all loci, and those which showed abnormal segregation as determined using a chi-square goodness of fit test were removed (P < 0.005). Markers were then associated within linkage groups using the group function of JoinMap®4, using a LOD cut-off value of 5.0 or greater. Marker orders within linkage groups were determined and map distances calculated using Haldane's mapping function. Maps generated independently for the two families were compared, and a 1:1 correspondence between linkage groups confirmed. The corresponding groups from the two families were combined using the JoinMap®4 merge function, and a consensus map generated. This map was generated from the first round of JoinMap®4, which integrates markers that score highly for goodness-of-fit; no attempt was made to force additional loci with a reduced goodness-of-fit into the map.The genetic linkage map was constructed using JoinMap®4, followed by the generation of linkage group-specific maps for markers informative in each parent. The marker order in the merged map was retained in the parental maps by inputting this as a fixed order to direct map generation, with progeny then analysed to detect double recombination events.The marker order from the merged map generated was used to check maps generated individually for the four contributing parents to identify suspicious double recombinants that would indicate potential errors in the marker order. This was performed using the "Create Maternal and Paternal Population Nodes" function of JoinMapSH performed the initial selection of SNPs for inclusion in this study, analysed the data from the SNP panels and co-wrote the paper. BH was involved in the initial selection of SNPs for inclusion in this study, wrote custom Perl scripts which were used for SNP selection and analysis and was involved in drafting the paper. TB assisted with the analysis of cod populations and with generation of the genetic linkage map and helped with the drafting of the final versions of the paper. SB was responsible for the conceptualization, design and implementation of the SNP identification and analysis program, generated the genetic linkage map, and co-wrote the paper. All authors read and approved the final manuscript.Automated annotation of SNP-containing contigs and amino acid substitutions for non-synonymous SNPs. This includes best hit in the NCBI nr database together with the bit score and e-value, and alternative amino acids for any SNPs resulting in non-synonymous substitutions.Click here for fileProperties of selected SNPs for which a functional assay was developed. This includes SNP accession numbers, observed and expected heterozygosity, minor allele frequency, tests for departure from HWE and Mendelian segregation.Click here for fileAtlantic cod SNPs polymorphic in Atlantic Canada but monomorphic in the eastern Atlantic. List of SNPs that were monomorphic in the eastern Atlantic  but polymorphic in Atlantic Canadian populations of cod.Click here for filePreviously published SNPs. Names of previously published SNPs together with any alternative names for these SNPs that were used in this study.Click here for file


Animal identification is pivotal in governmental agricultural policy, enabling the management of subsidy payments, movement of livestock, test scheduling and control of disease. Advances in bovine genomics have made it possible to utilise inherent genetic variability to uniquely identify individual animals by DNA profiling, much as has been achieved with humans over the past 20 years. A DNA profiling test based on bi-allelic single nucleotide polymorphism (SNP) markers would offer considerable advantages over current short tandem repeat (STR) based industry standard tests, in that it would be easier to analyse and interpret. In this study, a panel of 51 genome-wide SNPs were genotyped across panels of semen DNA from 6 common breeds for the purposes of ascertaining allelic frequency. For SNPs on the same chromosome, the extent of linkage disequilbrium was determined from genotype data by Expectation Maximization (EM) algorithm. Minimum probabilities of unique identification were determined for each breed panel. The usefulness of this SNP panel was ascertained by comparison to the current bovine STR Stockmarks II assay. A statistically representative random sampling of bovine animals from across Northern Ireland was assembled for the purposes of determining the population allele frequency for these STR loci and subsequently, the minimal probability of unique identification they conferred in sampled bovine animals from Northern Ireland.6 SNPs exhibiting a minor allele frequency of less than 0.2 in more than 3 of the breed panels were excluded. 2 Further SNPs were found to reside in coding areas of the cattle genome and were excluded from the final panel. The remaining 43 SNPs exhibited genotype frequencies which were in Hardy Weinberg Equilibrium. SNPs on the same chromosome were observed to have no significant linkage disequilibrium/allelic association. Minimal probabilities of uniquely identifying individual animals from each of the breeds were obtained and were observed to be superior to those conferred by the industry standard STR assay.The 43 SNPs characterised herein may constitute a starting point for the development of a SNP based DNA identification test for European cattle. The identification and registration of livestock and monitoring of their movements is an essential part of agricultural policy for national governments. Such schemes underpin disease control, grant and subsidy management, food hygiene/safety assurance and facilitate product recalls if necessary.As a result of these benefits, and also in response to major animal health problems such as BSE  and Foot and Mouth Disease that impact livestock, producers and consumers, many countries in the developed world have adopted national databases, based on numbered ear tags, to record cattle identity and movements ,2.Trade globalisation has also strengthened the case for improved animal traceability. Modern consumers confronted with increased choice from multiple sources may also face elevated risks as a result of increased likelihood of chemical/pathogenic contamination in foodstuffs ,4. Such In light of the potential dangers highlighted above, the European Union (EU) passed Council Directive 92/102/EEC stating that all bovine animals in Member States should be identified with an ear-tag bearing a unique identification code . The morFuture development of tag schemes may include the use of electronic identification devices such as radio frequency identification (RFID) ear-tags, ruminal boluses and injectable transponders which automate the reading of animal identity, thereby reducing transcription errors .http://idea.jrc.it/) confirmed that the preferred option, RFID ear-tags, still suffered from the same accidental tag loss and fraudulent switching problems which afflict conventional schemes [Whilst lauding the ability of such systems to improve farm management, a recent EU report on uses of electronic identification in the agri-food industries  permits the use of relatively few loci to identify cattle and to determine their parentage ,17. SeveSingle Nucleotide polymorphisms (SNPs) are a much simpler form of DNA variation than microsatellites, involving a single nucleotide change at one position of the genetic code. This lack of diversity makes SNP markers less informative than microsatellites. As a result, single SNPs don't discriminate between individuals very well. However, genotyping of multiple SNPs can overcome this problem .SNP genotyping also has distinct advantages over STR genotyping. Owing to their smaller size, they are less prone to gametic mutation and therefore do not suffer from the same non-identical by descent problems that can affect STRs as they mutate between generations . SNPs arThere are approximately 1.7 million cattle in Northern Ireland spread over 25000 herds with an average herd size of seventy animals. The Department of Agriculture and Rural Development (DARD) runs a computer database called the Animal and Public Health Information System (APHIS) based on the two ear tag system required by the European Commission . This syet al., 2004 [We attempted to assemble a panel of SNPs which would identify animals from the Northern Irish population. SNP panels previously identified by Heaton et al., 2002 and Werner l., 2004 ,22 were For validation purposes, it was desirable to compare the capability of any new SNP panel against the existing industry standard test. We STR genotyped a representative subset of the Northern Irish Cattle population using the ISAG approved Stockmarks II system. A general random sampling across the whole province that included many breeds was considered sufficient to determine allele frequencies for each locus and subsequently to help determine the minimum probability of finding two animals that possessed the same STR profile. Ultimately this would enable us to ascertain the uniqueness of STR profiles in our population, vital information for deployment of this technology as a counter fraud and traceability tool.Allele frequencies of the 51 SNPs genotyped across the semen DNA panels from six breeds are listed in the Additional file Allele frequencies of the remaining 45 SNPs were used to calculate expected genotype frequencies in each of the breed panels. Chi squared analysis of expected and observed genotype frequencies revealed no significant difference (p > 0.05) for any of the SNPs. This indicates that in each of the breed panels, all SNPs are in Hardy Weinberg Equilibrium (HWE)  Table . Two furSeveral of the remaining 43 SNPs were observed to exist on the same chromosome. Genotype data for SNPs located on the same chromosomes were collated from each of the six breeds studied, and subjected to EM algorithm analysis to determine the degree of allelic association/linkage disequilbirum (LD) between polymorphisms Table .-11 to 4.65 × 10-13  surveillance, quality assurance of removal of tuberculosis and brucellosis infected animals from affected herds and investigations into alleged fraudulent identity swapping. The technique has also been deployed to aid farmers in re-establishing the identity of animals which have lost ear-tags and subsequently fall foul of animal identification requirements. In future, it is conceivable that DNA profiling could be used as a means of auditing the entire APHIS system in a way which is more powerful than any paper based audit. It is also foreseeable that such a traceability system could expand to become an effective means of tracking animals and their products from 'gate to plate'. These developments would greatly enhance the traceability capability of the existing system and lead to greater consumer and producer confidence.In recent times, international consortia have been moving towards configuration of a high throughput SNP based assay for cattle identification and parentage verification purposes. We believe that whilst more SNPs could be added to increase the discriminatory power between closely related individuals, the panel characterised here serves as a good starting point for many of the major European cattle breeds.n = 37), Belgian Blue (n = 35), Simmental (n = 32), Charolais (n = 34), Aberdeen Angus (n = 38) and Holstein (n = 29). Pedigree information was used to ensure that genotyped animals from each breed panel were unrelated at the parent and grandparent levels.Semen samples were collected for the six major cattle breeds used in Northern Ireland - Limousin  were selected for inclusion in a population representative sample set collected over a 6 month period. This sample set provided a confidence level of 95.0% that allelic frequencies observed were representative of the greater national bovine population. Numbers of animals per breed are shown in Table DNA was extracted from both clotted blood and semen samples using Qiagen QIAamp DNA Mini kits (Cat no. 51306) following manufacturers instructions.2, 0.2 μM of each primer, 2 mM of each dNTP, 10 mM Tris-HCl, 50 mM KCl, 0.5 U of Qiagen HotStar Taq polymerase. The cycling conditions used were an initial 96°C for 15 minutes to activate the hot start Taq, followed by 35 cycles of 30 s denaturation at 96°C, 1 min 30 s annealing at 53°C and 1 min 30 s extension at 72°C. Amplification products were SNP genotyped by pyrosequencing [DNA derived from semen (50 ng) was subjected to single-plex PCR for each of the SNPs genotyped. 20% of samples were genotyped a second time for quality control. PCR reactions were carried out in 25 μl total volume, 2 mM MgClquencing . Genbankquencing ,22.DNA derived from clotted blood (50 ng) was subjected to multiplex PCR at the eleven microsatellite markers in the Applied Biosystem's Bovine Stockmarks II assay (Cat no. 4307480) as suggested by the manufacturer. Amplified products were resolved by gel capillary electrophoresis on an Applied Biosystems ABI3100 Genetic Analyser. Analysis and allele calling was performed using Applied Biosystems Gene-Mapper software.et al [The NCBI bovine genome BLAST tool was utilised to determine the exact physical location of each of the SNP markers in the recent draft of the bovine genome, Btau 4. Using Haploview , EM algoet al , was undet al . This peAllele frequency data was used to determine population genotype frequencies for both sets of markers. Chi squared analysis of expected and observed genotype frequencies was undertaken to ensure that alleles observed were in HWE. Most common genotype frequencies at each marker were then determined. These most common genotype frequencies were then multiplied together and used to determine the minimal probability of two randomly drawn animals from the same population possessing the same SNP or STR profile .AA designed and performed SNP genotyping assays, participated in the study design determined allele frequencies, performed statistical analysis and drafted the manuscript. MT participated in study design and genotyping of STR markers. BMcK participated in study design and contributed to SNP genotyping. AC designed and performed SNP genotyping assays, contributed to databasing of results and oversaw extraction of DNA from blood and semen samples. JL performed STR genotyping and databasing of results and oversaw extraction of DNA from blood and semen samples.AM and DH contributed to study design. RF contributed to study design and provided SNP sequence details. RS conceived of the study and contributed to study design. All authors have read and approved the final manuscript.SNP allele frequency data. All SNP allele frequency data for each of the six breeds studied are contained in this table along with Hardy Weinberg chi square p values, most common genotypes and most common genotype frequency.Click here for fileSTR allele frequency data. All STR allele frequency data for the NI cattle population representative random sampling are contained in this table along with Hardy Weinberg chi square p values, most common genotypes and most common genotype frequency.Click here for file


Genome-wide association studies have become standard in genetic epidemiology. Analyzing hundreds of thousands of markers simultaneously imposes some challenges for statisticians. One issue is the problem of multiplicity, which has been compared with the search for the needle in a haystack. To reduce the number of false-positive findings, a number of quality filters such as exclusion of single-nucleotide polymorphisms (SNPs) with a high missing fraction are employed. Another filter is exclusion of SNPs for which the calling algorithm had difficulties in assigning the genotypes. The only way to do this is the visual inspection of the cluster plots, also termed signal intensity plots, but this approach is often neglected. We developed an algorithm ACPA , which performs this task automatically for autosomal SNPs. It is based on counting samples that lie too close to the cluster of a different genotype; SNPs are excluded when a certain threshold is exceeded. We evaluated ACPA using 1,000 randomly selected quality controlled SNPs from the Framingham Heart Study data that were provided for the Genetic Analysis Workshop 16. We compared the decision of ACPA with the decision made by two independent readers. We achieved a sensitivity of 88% (95% CI: 81%-93%) and a specificity of 86% (95% CI: 83%-89%). In a screening setting in which one aims at not losing any good SNP, we achieved 99% (95% CI: 98%-100%) specificity and still detected every second low-quality SNP. Genome-wide association studies (GWAS) with 100,000-1,000,000 single-nucleotide polymorphisms (SNPs) are a promising novel approach for dissecting the genetic background of complex diseases and have become common in the last two years . Here, aAnother important criterion is the quality of the results from the calling algorithm, which transforms the fluorescence signal intensities into one of the three possible genotypes. For evaluation, the visual inspection of signal intensities through cluster plots, also termed signal intensity plots, has been recommended ,5, and tThere are two principal approaches to tackle this problem. The first focuses on improving the calling algorithm itself . The secSignal intensities and genotypes of 6,752 participants in the Framingham Heart Study were provided as Problem 2 for the Genetic Analysis Workshop 16 (GAW16).Genotyping was performed using the Affymetrix GeneChip Human Mapping 500 kArray Set. Details of the study and the genome-wide SNP scan can be found in Cupples et al. .The ACPA algorithm, which analyses one SNP at a time, is described below for the case of exactly three different clusters, i.e., three genotype groups.1. Select all individuals with assigned genotypes, i.e., delete all missing genotypes.k = 1 to 3:2. For clusters k.a) Perform a principal-component analysis, using only data of cluster b) Transform the data of all clusters according to estimated first two principal components of step a).c) Calculate the Mahalanobis distance from the center of cluster k to all samples within cluster k.b as b = q3 + f. IQR, where q3 is the upper quartile and IQR is the interquartile range of the Mahalanobis distances computed in step c). Default: f = 3.d) Define the cluster boundary k to the samples not included in k.e) Calculate the Mahalanobis distance from the center of cluster ck, of samples not included in k falling in the boundary.f) Count the number c = ∑ ck3. Sum the number of samples falling in the boundary over all three clusters, i.e., c exceeds a threshold t. Default: t = 25.4. A SNP is called to have unreliable genotype assignments if c of each cluster-plot is logged in a file. The algorithm has been implemented in R (version 2.7.1) and uses the library GenABEL (version 1.4-1) for storing the genotype data. The cluster plot can be generated together with the cluster boundaries in portable document format (pdf).If the SNP has a low minor allele frequency leading to two clusters, the algorithm is adapted appropriately. The validation status Two example plots are displayed in Figure f , the size of the ellipses , missing fraction >0.02, and minor allele frequency <0.01. We randomly selected 1,000 SNPs of the remaining SNPs. Both readers judged independently whether a SNP should be excluded from or kept for further analyses. They also gave a level of uncertainty (certain/uncertain) of their decisions. Because we cannot expect ACPA to outperform the reading of experienced readers, we only considered SNPs for which both readers came to the same decision and expressed certainty. A good SNP is a SNP when both readers recommend keeping that SNP for further analysis. If both readers favor the exclusion of a SNP, we call this a bad SNP.The performance of the ACPA algorithm was evaluated by comparing ACPA with the decisions made by two experienced readers. First, we performed standard quality control (sQC) and omitted SNPs with deviations from Hardy-Weinberg-equilibrium (f = 1.5 and f = 3).We report sensitivity, specificity, positive predictive values (PPV) and negative predictive values (NPV) of ACPA as well as 95% confidence intervals (95% CI) using Wilson's score method . Here, sf = 1.5 we achieved a specificity of 99%, i.e., almost all good SNPs were recognized by ACPA.Out of the 486,605 BRLMM-called SNPs that were provided for GAW16, 343,427 SNPs successfully passed the sQC. For 695 of the 1,000 randomly selected SNPs, the two independent readers came to identical decisions, and both readers expressed certainty about their decision. Five hundred and eighty-eight (84.6%) were judged as correctly called SNPs, and the remaining 107 SNPs were classified as SNPs where genotype assignment was unreliable. Point and interval estimates for sensitivity, specificity, PPV, and NPV are shown in Table Increasing the cluster boundaries resulted in a loss of specificity (86%) but increased the sensitivity of ACPA to detect badly clustered SNPs. The PPV declined from 93% to 53%.f, resulting in narrow ellipses and hence only excluding very badly assigned clusters. For example, the specificity is 99% for f = 1.5. Of course, this comes at the cost of keeping many doubtful SNPs in the data (sensitivity = 51%). In a situation where it would be costly to include SNPs with a bad cluster plot, one could increase the value for f to exclude doubtful SNPs.For the randomly selected sample of 1,000 SNPs, both readers recommended the exclusion of 107 SNPs. Although data quality may vary, there will always be thousands of SNPs that should be excluded. This shows the necessity of analyzing cluster plots. The large amount of SNPs requires an automatic approach, and this is provided by ACPA. Because it would not be reasonable to exclude well clustered SNPs at the screening stage, the method must be highly specific. This can be achieved by choosing a small The necessity of visually inspecting cluster assignments from the genotype calling was demonstrated, and an intuitive approach was proposed for performing this task automatically. As a by-product, our novel software ACPA provides a convenient way to generate cluster-plots for any subset of SNPs.ACPA: Automated cluster plot analysis; GAW16: Genetic Analysis Workshop 16; GWAS: Genome-wide association study; NPV: Negative predictive values; PPV: Positive predictive values; SNP: Single-nucleotide polymorphism; sQC: Standard quality control.The authors declare that they have no competing interests.AS carried out the analysis, participated in writing ACPA, and drafted the manuscript. DFS participated in writing ACPA. MV and SS rated the SNPs. IRK participated in the design and coordination of the study. AZ conceived of the study and finalized the manuscript. All authors read and approved the final manuscript.


Application of single nucleotide polymorphisms (SNPs) is revolutionizing human bio-medical research. However, discovery of polymorphisms in low polymorphic species is still a challenging and costly endeavor, despite widespread availability of Sanger sequencing technology. We present CRoPS™ as a novel approach for polymorphism discovery by combining the power of reproducible genome complexity reduction of AFLP® with Genome Sequencer (GS) 20/GS FLX next-generation sequencing technology. With CRoPS, hundreds-of-thousands of sequence reads derived from complexity-reduced genome sequences of two or more samples are processed and mined for SNPs using a fully-automated bioinformatics pipeline. We show that over 75% of putative maize SNPs discovered using CRoPS are successfully converted to SNPWave® assays, confirming them to be true SNPs derived from unique (single-copy) genome sequences. By using CRoPS, polymorphism discovery will become affordable in organisms with high levels of repetitive DNA in the genome and/or low levels of polymorphism in the (breeding) germplasm without the need for prior sequence information. Arabidopsis and rice, for which draft genome sequences have been completed SNP discovery is an important area of molecular genetics research aimed at collecting sufficient exploitable sequence polymorphisms to enable high-resolution, high-throughput genotyping at lower costs in the future. However, for many crop species the efficiency of the SNP discovery process is often hampered by the fact that limited amounts of genome sequences are available compared to e.g. Zea mays) genome comprises 2300 to 2700 Mb ot selection The maize (The AFLP® technology Complexity Reduction of Polymorphic Sequences) and its application in maize. With CRoPS, tagged complexity-reduced libraries of two or more genetically diverse samples are prepared by AFLP, preferably using a methylation-sensitive restriction enzyme. Next, AFLP fragment libraries are sequenced at 5 to10-fold average redundancy in microfabricated, high-density picoliter reactions using the GS system Here we describe the CRoPS™ technology . A total of 754,199 reads  were obtained. The number of reads after the first filtering for Key sequences  was 739,042. Of these, 399,252 GS 20 raw sequencing reads remained after the final filtering by the GS 20 software. This number of sequence reads is higher than the specifications of the GS 20 but in line with other runs we performed earlier (data not shown) as well as results reported by others Further bioinformatics analysis took place “off-rig” (i.e. on a separate server) using the CRoPS pipeline . The GS Using the TIGR Gene Indices clustering tool (TGICL) Finally, SNPs were mined between the reads contained in an MSA. Parameters for SNP mining were set to include only SNPs for which both alleles were observed at least twice and SNPs not being part of homopolymers larger than 3 bases. The threshold for minimal distance to a neighboring SNP was initially set at one base, i.e. all SNPs were selected irrespective of their distance to (a) neighboring SNP(s). In addition, and importantly, SNPs were mined according to sample origin, i.e. only SNPs “segregating homozygously” between the two maize lines were included. By doing so, a strong filter was created to select against “false” SNPs resulting from alignment of highly homologous duplicated sequences as opposed to genuine SNPs derived from single-copy sequences in the sequenced genome fraction . As a reTo investigate the relationship between the number of putative SNPs and SNP mining parameters, SNPs were mined under different parameter settings regarding the minimal available sequence information flanking the target SNP , and the minimal interval of flanking sequence that must be devoid of additional SNPs . SNP mining was performed by varying these two parameters in all 36 (6 times 6) possible combinations, while keeping the other SNP mining parameters, including minimal representation of both alleles at least twice, homopolymer settings and segregation according to sample origin constant. As expected, the number of SNPs mined according to these more stringent criteria decreased to less than 50% , heteroduplex fragments are formed upon mixing the two (or more) samples at this step. Since the different samples contain different four base sample identification tags at their 5′ ends, we suspected that the 3′ ends (which do not match the four base sample identification tags at the 5′ ends of the opposite strand of the heteroduplexes) are removed and filled-in with the opposite strand as template for polymerization. The net result of such an event is a sample identification tag switch  was obtained. With varying levels of deviation from equal sample representation, this has also been observed in at least six other GS 20 runs (data not shown), despite our attempts to pool equimolar amounts of AFLP products. Clearly it would be beneficial to reach more equal sample representation to increase the number of SNPs mined per run. The same observation was made by Binladen and co-workers ot selection Other optimization steps expected to increase the output of CRoPS further include selection against plastid (chloroplast sequences) co-isolated with genomic DNA, the use of normalized genomic or cDNA www.maizegdb.org), using a modified CTAB procedure Total genomic DNA was isolated from leaf material of the two parental lines (i.e. B73 & Mo17) of the ISU mapping population  mixture was subsequently diluted 10-fold with T10E0.1 and 5 µl diluted mix was used as a template in a selective pre-amplification step, the so-called +1/+1 pre-amplification. Primer sequences for the +1/+1 pre-amplification were 5′-GTAGACTGCGTACACGGA-3′  and 5′-GATGAGTCCTGAGTAAC-3′ . Twenty µl PCRs were performed containing 5 µl diluted RL mixture, 30 ng HpaII primer, 30 ng MseI primer, 0.2 mM dNTP, 0.4 U AmpliTaq® (Applied Biosystems) and 1× AmpliTaq buffer. PCR was performed for 20 cycles with the following cycle profile: 30 sec 94°C, 60 sec 56°C, 60 sec 72°C, followed by cooling down to 4°C.AFLP templates were prepared as described previously 10E0.1, and used for the second selective amplification step, the so-called +1/+2 selective amplification. Primer sequences for the +1/+2 selective amplification were 5′-‘P-ACACGTAGACTGCGTACACGGA-3′  and 5′-‘P-ACACGATGAGTCCTGAGTAACT-3′  for sample B73. The four most 5′ bases of these primers serve as sample identification tag (KeyGene™ SeqTag technology). These 4-nt sample identification tags were selected from a collection of 4-nt sequences differing by at least 2 nt to exclude the possibility that a single nucleotide substitution error could cause incorrect assignment of the sequence to a sample. Similarly, primer sequences for the +1/+2 selective amplification of the Mo17 sample were 5′-‘P-AGCTGTAGACTGCGTACACGGA-3′  and 5′-‘P-AGCTGATGAGTCCTGAGTAACT-3′ . Fifty µl PCRs were performed containing 5 µl diluted +1/+1 pre-amplification mixture, 75 ng HpaII primer, 75 ng MseI primer, 0.2 mM dNTP, 1 U AmpliTaq (Applied Biosystems) and 1× AmpliTaq buffer. PCR was performed for 30 cycles with the following cycle profile: 30 sec 94°C, 60 sec 56°C, 60 sec 72°C, followed by cooling down to 4°C.The +1/+1 pre-amplification reaction was diluted 20-fold with TNext, 100 µl of PCR products of each sample were purified using the QIAquick PCR Purification Kit (Qiagen). Concentrations of both samples were determined using the Nanodrop ND-1000 (Nanodrop Technologies), after which equal amounts of the two samples were pooled and further treated as one fragment library sample. This saves costs and prevents relying on physical compartmentalization to separate both samples. Furthermore this approach provides flexibility regarding processing multiple samples.Bst DNA polymerase fill-in step of the published protocol was left out.3.45 µg of the fragment library sample  were used as input for GS 20 library construction. The use of tagged and pooled PCR products, however, necessitated several adaptations in the published GS 20 library construction protocol 6 enriched beads, > 60% PassFilter, <20% Mixed+Dots and approximately 6000 Keypass reads.After library construction, a titration run was carried out using 16, 64, 256 and 512 copies per bead. The copies per bead ratio to be used in the titration run is estimated based on the concentration of sstDNA (single stranded AB library). Therefore, the outcome of the titration run determines the ratio which needs to be applied in the actual sequencing run. Based on the titration run carried out for this experiment, a 48 copies per bead ratio was selected, founded on the “Predicted Recovery” of approximately 2.10Emulsion PCR and bead enrichment were carried out according to the standard GS 20 protocol (Roche Applied Science). One full picotiterplate (PTP) (70×75 mm) with two regions was used. Enriched beads were divided over both regions. Sequencing was performed according to the manufacturer's instructions (Roche Applied Science).g relational database served as the central repository for all raw and processed data and the material and process definition.The basecalled reads from both regions were added together in one file and further processed for SNP mining using a fully automated pipeline (Keygene N.V.). The web based pipeline was written in Perl 5.8.0 and runs via an Apache web server on a Linux platform. Microsoft Internet Explorer was used as client. An Oracle 10The SNP discovery process consisted of four parts, namely (1) GS 20 data processing, (2) CRoPS pre-processing, (3) the CRoPS analysis, and (4) CRoPS SNP mining .g (Oracle). Furthermore, the AFLP adapter sequences were trimmed. Pre-processed reads were saved to the database. In the CRoPS analysis step, reads were clustered and assembled using the TGICL tool GS 20 data processing was performed on-rig using the standard GS software. This process resulted in the “GS 20 raw sequence reads” that were directly used for further processing in the CRoPS pre-processing step. During pre-processing, the origin of the reads was identified according to their four base sample identification tags. The implementation of this step was based on the internal BLAST function in Oracle 10Polymorphisms were selected during the SNP mining step. For each putative SNP a number of features were recorded, including relative position in the consensus sequence, sample count in the MSA, allele count per sample, the MSA depth (number of reads at the SNP position), distance to flanking SNPs, flanking size around SNP and the presence of a homopolymer stretch in which a SNP may occur. Mining rules were created from these features and defined as follows: 1) each allele should be present at least twice in a MSA, 2) SNPs should not be part of homopolymers larger than 3 bases, and 3) SNPs should be segregating according to sample origin. SNPs that passed the filters were selected as the best candidates for conversion into genotyping assays.The pre-processed sequence data will be deposited at the NCBI Short Read Archive (SRA) as soon as this archive is ready to accept the data (expected at the end of 2007). Until then, the data can be requested from the authors.2, 10 mM dithiothreitol (DTT), 1 mM NAD, 0.1% triton X-100; pH 7.6 at 25°C; New England Biolabs Inc], 2 U Taq DNA ligase (New England Biolabs Inc) and 0.5 fmol of each of the ligation probes. Next, 10 cycles of repeated denaturation, probe hybridization and ligation were performed in a Perkin Elmer 9700 thermal cycler (Applied Biosystems) using the following profile: initial denaturation for 2 min at 94°C, followed by 10 cycles of 15 s at 94°C and 60 min at 60°C, and storage at 4°C. After ligation, the mixture was diluted with 30 µl of 1×Taq DNA ligase buffer to 40 µl.Probes were designed for 30 putative SNPs in two multiplex (15-plex) SNPWave assays using ProbeDesigner software (Keygene N.V.) as described previously Ten µl of diluted ligation reaction was subsequently amplified in a 20 µl mixture containing 1×GeneAmp® PCR buffer (Applied Biosystems), 0.2 mM of each dNTP, 0.4 U AmpliTaq Gold DNA polymerase (Applied Biosystems), and 30 ng unlabeled forward primer (5-GACTGCGTACCAATTC-3) and 30 ng FAM-labeled reverse primer (5-GATGAGTCCTGAGTAA-3). The amplification profile was 12 min at 94°C, followed by 13 cycles of 30 s at 94°C, 30 s at 65°C with a reduction of 0.7°C per cycle to 56°C in cycle 13, followed by 1 min at 72°C. This was followed by 23 cycles of 30 s at 94°C, 30 s at 56°C and 1 min at 72°C, and storage at 4°C.Purification of diluted SNPWave PCR products and subsequent detection on the MegaBACE 1000  were as described previously SNPs and flanking sequences can be found in the supplementary file . Probe sTable S1SNPs and flanking sequences used for the 13-plex and 10-plex SNPWave assays(0.04 MB DOC)Click here for additional data file.


Allele transmissions in pedigrees provide a natural way of evaluating the genotyping quality of a particular proband in a family-based, genome-wide association study. We propose a transmission test that is based on this feature and that can be used for quality control filtering of genome-wide genotype data for individual probands. The test has one degree of freedom and assesses the average genotyping error rate of the genotyped SNPs for a particular proband. As we show in simulation studies, the test is sufficiently powerful to identify probands with an unreliable genotyping quality that cannot be detected with standard quality control filters. This feature of the test is further exemplified by an application to the third release of the HapMap data. The test is ideally suited as the final layer of quality control filters in the cleaning process of genome-wide association studies. It identifies probands with insufficient genotyping quality that were not removed by standard quality control filtering. Genome-wide association studies have led to the discovery of many novel, reproducible associations between genetic loci and disease phenotypes. An important step in the analysis of genome-wide association studies is the data cleaning/QC filtering step. The statistical analysis tools that are applied as QC filters typically include testing for Hardy-Weinberg equilibrium, testing for Mendelian inconsistencies, evaluating quality scores, etc. We propose a new genome-wide transmission test for family-based designs that is applied to the dataset after the QC filtering. It allows for the assessment of the genotyping error rate that is caused by miscalled genotypes that could not be detected by the QC filters. By applying the test to individual probands, probands with insufficient genotyping quality can be identified and removed from the dataset before the analysis. Over the last several years, genome-wide association studies (GWAS) have led to the identification of numerous, replicable associations between novel genetic loci and complex diseases/phenotypes However, even after the most careful quality control filtering, one has to recognize that it will not be possible to detect all inherent genotyping errors in the dataset and eliminate their influence on an association analysis. For many of the SNPs and probands, the genotyping error rate will not be “poor” enough to raise a “red flag” in the quality control filtering step and it will not be possible to remove them from the dataset for the association analysis.Here we propose a new quality control filter for family-based studies that allows the researcher to assess the genotyping quality of each proband by looking at the transmission patterns of the minor and the major allele within the same proband. That is, the new filter provides an additional evaluation of data quality at the proband level. For example, in a nuclear family in which one proband and both of the proband's parents have been genotyped, we can compare the number of SNPs for which the minor allele is transmitted from the heterozygous parents to the number of SNPs for which the major allele is transmitted. Since the null hypothesis of no association will be true for the vast majority of SNPs, we expect to observe about the same number of minor allele transmissions as major allele transmissions. However, in the presence of systematic genotypic error, this will be different. Undetected genotyping error can create larger numbers of transmissions of the major allele than the minor allele α-level In the presence of genotyping error, it is a well-understood phenomenon that standard family-based association tests (Transmission Disequilibrium Test In order to estimate the undetected genotyping error for an individual proband after quality control filtering, the information about the transmission patterns has to be aggregated across all of the proband's genotyped SNPs. Consequently, we define for each proband an individual transmission test statistic that can be used to infer the underlying, undetected average genotyping error rate for the selected proband.In order to keep the notation simple, we assume that one trio is available for genotyping at By summing over the Mendelian residuals However, the asymptotic properties of Since the sample size for the genome-wide transmission statistic Using simulation studies, we examine the power of the transmission test statistic In each replicate of the simulation study, we simulate data on or model . That isQuality control filtering is applied in order to identify trios with particularly bad quality data. Specifically, trios with Mendelian inconsistencies are removed. Then the standardized genome-wide statistic Investigating differences specific to genotyping error models, we see that the most severe deviations occur under Models 1 & 3, which involve miscalling of homozygous major genotypes as heterozygotes and vice versa. For example, under Model 1, a misclassification probability (Results between the two types of SNP chip, distinguished by the generation of minor allele frequencies, are relatively minor, with the average transmission test generally being higher using a beta distribution. Under either setting, probands can be identified with sufficient power when exhibiting genotyping error rates consistent with Models 1, 2, 3 & 5. That is, unless genotyping errors only come about by miscalling between heterozygote and minor homozygote genotype classes, the new transmission test statistic is powerful to detect probands who have remained unfiltered by traditional quality control measures.In the previous simulations, we assume the absence of linkage disequilibrium between the loci. However, the SNP density on most modern SNP chips is so high that the genotyped SNPs are in linkage disequilibrium. We therefore repeated the simulation experiments under the assumption that the analyzed SNPs are correlated. In the presence of LD, the minor allele frequency is randomly drawn from either a beta or a uniform distribution and is then adjusted for the presence of LD using the linkage disequilibrium parameter D and the minor allele frequency at the previously generated locus, i.e. To verify the theoretically derived distribution of To illustrate the practical relevance of the proposed genome-wide test for single probands, we applied the methodology to the third release of the HapMap data http://www.broad.mit.edu/~debakker/p3.html. For example, SNPs were filtered if, within a population, the Hardy-Weinberg test p-value was less than 10−6, missingness was greater than 5% or if there were three or more Mendelian errors.The genotyping data was extensively cleaned as described at For the analysis, we selected a cutoff of 5% for the minor allele frequency as a quality control filter. Each proband was analyzed three times. First, the genome-wide transmission test statistic was computed for all 1,403,896 available SNPs. The second analysis was focused on the 249,889 SNPs that were available on both genotyping platforms and provided concordant genotype calls for both SNP chips. For the third analysis the SNPs that were available on only one of the SNP chips but not on the other were examined. Given the additional quality control checking based on concordant genotype calls on both SNP chips, the second analysis will be based on the SNPs with very highest genotyping quality, while SNPs that are used in the third analysis are of considerably lower genotyping quality.In the first analysis it is important to note that, for some probands, genome-wide transmission test statistics are observed that exceed values of 30, indicating substantial amounts of genotyping error in the data. The second analysis, which is focused on the SNPs that were available on both SNP chips and provided consistent genotyping results, produced much smaller test statistics for nearly all of the probands, even those who had very high transmission test results in the first analysis. Although 3 probands still show significant test results for the presence of genotyping error at an overall In general for the second analysis, based on our previous simulations, we do not observe any probands that seem to have excessive amount of genotyping error. This observation is intuitively expected, since the second analysis is based only on genotype data that was concordant on both platforms and, therefore, should be of relatively high quality. In the third analysis, we again observe probands for whom the genome-wide transmission test indicates substantial amount of genotyping error. These probands are the same ones who also exhibited the high test statistic values in the first analysis. This result is expected as well since the third analysis is focused on the SNPs where genotype calls could not be confirmed by a second platform and are likely of poorer genotyping quality than the SNPs used in the second analysis.As an exploratory analysis, we examined whether the probands with large values for the In this manuscript, we proposed a novel transmission test for the detection of genotyping errors in a single proband. In contrast to previously proposed family-based association tests, our approach can be applied just to a single proband with an arbitrary number of genotyped SNPs without the need to specify any LD structure. Our simulation results suggest that the genome-wide transmission test is sufficiently powerful to detect single probands with poor genotyping quality. This feature will allow the researcher to remove such probands from the dataset before the association analysis. Because the family-based association test statistic will be inflated regardless of which family member contains the genotyping error, we recommend removal of the entire nuclear family. In an application to the third release of the HapMap data, the proposed test was able to identify single probands with high genotyping error rates which are attributable to SNPs that could not be genotyped on both SNP chips. The key properties of the genome-wide test statistic, application to an arbitrary number of SNPs and an unspecified LD structure, will make the approach a useful tool for the quality control filtering in genome-wide association studies.


Recent developments of high-density SNP chips across a number of species require accurate genetic maps. Despite rapid advances in genome sequence assembly and availability of a number of tools for creating genetic maps, the exact genome location for a number of SNPs from these SNP chips still remains unknown. We have developed a locus ordering procedure based on linkage disequilibrium (LODE) which provides estimation of the chromosomal positions of unaligned SNPs and scaffolds. It also provides an alternative means for verification of genetic maps. We exemplified LODE in cattle.The utility of the LODE procedure was demonstrated using data from 1,943 bulls genotyped for 73,569 SNPs across three different SNP chips. First, the utility of the procedure was tested by analysing the masked positions of 1,500 randomly-chosen SNPs with known locations (50 from each chromosome), representing three classes of minor allele frequencies (MAF), namely >0.05, 0.01<MAF ≤ 0.05 and 0.001<MAF ≤ 0.01. The efficiency (percentage of masked SNPs that could be assigned a location) was 96.7%, 30.6% and 2.0%; with an accuracy (the percentage of SNPs assigned correctly) of 99.9%, 98.9% and 33.3% in the three classes of MAF, respectively. The average precision for placement of the SNPs was 914, 3,137 and 6,853 kb, respectively. Secondly, 4,688 of 5,314 SNPs unpositioned in the Btau4.0 assembly were positioned using the LODE procedure. Based on these results, the positions of 485 unordered scaffolds were determined. The procedure was also used to validate the genome positions of 53,068 SNPs placed on Btau4.0 bovine assembly, resulting in identification of problem areas in the assembly. Finally, the accuracy of the LODE procedure was independently validated by comparative mapping on the hg18 human assembly.The LODE procedure described in this study is an efficient and accurate method for positioning SNPs (MAF>0.05), for validating and checking the quality of a genome assembly, and offers a means for positioning of unordered scaffolds containing SNPs. The LODE procedure will be helpful in refining genome sequence assemblies, especially those being created from next-generation sequencing where high-throughput SNP discovery and genotyping platforms are integrated components of genome analysis. The last decade has seen a rapid expansion in the number of genomes from a diverse range of species being sequenced . Furtheret al., [A number of strategies can be adopted to place polymorphic markers on chromosomes via linkage maps -10, Radiet al.,  applied Recently we showed  that polHere we modify the initial LODE procedure for assigning SNPs to chromosomes and positioning SNPs within chromosomes. First, the efficiency of using genome-wide LD information is investigated by using mapped SNPs as a test set. Next, the procedure is applied to assign positions for 4,688 out of 5,314 unpositioned SNPs on Btau4.0, which were either un-assigned or assigned with ambiguity based on BLAST against Btau4.0, from a high-density SNP panel of 73,568 SNPs. We also suggest the chromosomal locations of un-ordered scaffolds. Finally the LODE procedure was used to confirm the order of mapped SNPs across the genome as a means to check the quality of genome assembly.http://www.affymetrix.com) and 54 k , used for genotyping 1,536, 441 and 377 Australian Holstein-Friesian (HF) bulls, respectively, were combined into a single dataset for the current analyses. There were duplicate samples and duplicate SNPs within and between datasets. Only unique samples and SNPs with higher call rate (% genotype assignment) were selected to include in the final dataset. Any inconsistent genotype was set to unknown. The final combined dataset represented 73,569 unique SNPs and 1,943 bulls with an average of 628 bulls genotyped per SNP. The mean coefficient of coancestry among these 1,943 bulls is 0.025, with 0.0 and 0.035 for the first and third quartiles, respectively.Data from three SNP genotyping arrays, namely 15 k , 25 k  that is not assigned to a chromosome (referred to as 'Un'). We used the Batu4.0 assembly to demonstrate utility of the LODE procedure since the assembly contained a number of SNPs not assigned to chromosomes. Comparison of LODE positions were also made against another bovine assembly build UMD3.0 which has recently become available.The location of each of the 73,569 SNPs in the bovine genome was assessed from BLAST alignment of SNP flanking sequences with the Btau4.0 assembly SNP positions on Btau4.0 were categorised as follows: i) 'mapped' (single assignment to a chromosome); ii) 'ambiguous' (more than one assignment in the genome); iii) 'Un' (single assignment to 'Un' sequences only); iv) 'unassigned' (no assignments in the genome). Collectively, the last three categories  are here called 'unpositioned'.r2) with mapped SNPs. The r2 estimates were obtained using GOLD [et al. [et al. [The location of each unpositioned SNP was estimated on the basis of its LD a. maximum r2 > 0.1 b. number of mapped SNPs with r2max and n0.1, in the latter case after excluding chromosomes for which n0.1<3. A chromosome with top ranking for both parameters was identified as the candidate chromosome for that unpositioned SNP.3. Chromosomes were then ranked according to r2max was identified. If the r2max of the second chromosome exceed 2/3 r2max of the candidate chromosome, the SNP was not assigned to any chromosome. This improved the accuracy of assignment from 92.1% to 98.9% , the chromosome with next highest -3, the cr2max.For each unpositioned SNP that could be assigned to a chromosome, its location on that chromosome was allocated the same position as that of the mapped SNP with which the unpositioned SNP has The above LODE procedure was first tested for its ability to determine the location of SNPs whose location was actually known. Three test sets involved determining the location of a total of 1,500 "masked" SNPs . Each set comprised SNPs with a different MAF class, namely 0.001<MAF ≤ 0.01 ; 0.01<MAF ≤ 0.05 ; >0.05 . The extent to which the procedure was successful was assessed in terms of "efficiency" (the percentage of "masked" SNPs that were assigned a location), "accuracy" (the percentage of "masked" SNPs that were assigned to the correct chromosome), and "precision" . After testing the LODE procedure with the above test sets, the same procedure was applied to unpositioned bovine SNPs.To provide further evidence of the utility of the LODE procedure, we used a comparative mapping approach to confirm the genome location of unpositioned bovine SNPs against the human genome assembly hg18, since this represents the most complete mammalian genome to date. This approach was considered helpful since the location of the unpositioned SNPs could not be validated on Btau4.0 directly.ftp://hgdownload.cse.ucsc.edu//goldenPath/hg18/. Secondly, the 'LiftOver' tool http://genome.ucsc.edu/cgi-bin/hgLiftOver was used with default settings to convert LODE positions from the bovine Btau4.0 assembly to the human hg18 assembly.The comparative position of bovine SNPs was estimated in the human genome using two approaches. Firstly, BLAST was used to align the flanking sequences of unpositioned SNPs with the hg18 assembly The LODE procedure was used to recompute the positions of all SNPs mapped to the genome which were genotyped and met minimum criteria for inclusion as detailed above and MAF >0.05. The procedure was performed in batches, where the positions of 10% (every 10th) of SNPs of a chromosome were masked. The positions of the masked SNPs were recomputed based on the LD information of the remaining SNPs in the genome. The chromosomal assignments and positions estimated by LODE were compared with original positions on Btau4.0 and also with UMD3.0.A total of 870 (96.7%) of the 900 test SNPs with MAF>0.05 were allocated a chromosomal position by LODE. All but one (i.e. 869 = 99.9%) of the positions were the same as the Btau4.0 accepted assembly position. The comparison of estimated and known SNP positions  could not be positioned (Table 92 SNPs 30.6%) from the second test set (0.01<MAF ≤ 0.05) were positioned, with only one mis-assignment (1.1%). Comparison of the estimated LODE positions and the known positions showed high agreement  positions on the human hg18 assembly were obtained from the BLAST and LiftOver procedures for 284 SNPs from the panel of 4,688 SNPs positioned by LODE. The chromosomal assignments for 230 (81%) of these SNPs were identical between BLAST and LiftOver. 54 (19%) of the 284 SNPs had different chromosomal assignments on hg18 by the two above procedures, which may be due to the LODE positions being outside of conserved syntenic blocks between bovine and human chromosomes. Such blocks are normally very small and quite variable in length. Comparison of the chromosomal positions of the 230 SNPs, with same chromosomal assignments, shows very strong agreement (cor = 0.95)  and slightly lower accuracy (2 mis-assignments) when compared to the thresholds adopted in our study  imposed by matrix dimensions of marker density, thus limiting application to full genome analyses. MAF of SNP to be placed has a significant effect on the efficiency of the LODE procedure, as shown in detail in the result section by running the three different test sets of varying MAF (Table P < 0.0001), 54 could be given assignments and all of these assignments were correct. Linkage studies generally exclude such SNPs from analysis [In this study we reported and validated a procedure to accurately and efficiently map SNPs based on LD information. The LODE procedure offers particular advantages in the positioning of problem SNPs for which no unambiguous assignment on a draft genome assembly could be made, as well as a means for positioning of unordered scaffolds containing SNPs. Miller et al.  used a ge et al.  was of sanalysis . FinallyThe LODE procedure described here is complementary to other commonly used methods to assemble maps, including linkage maps and physical maps such as Fluorescence in situ hybridization (FISH) and Radiation Hybrid mapping , but offde novo ordering of markers can result in robust framework maps, but such maps required information from often large and specific resource populations. Indeed linkage maps have been assembled for many species including a broad range of markers . In the . It is recommended to always test the LODE strategy on a panel of mapped SNPs with known positions, before applying the procedure to unmapped SNPs; and, if necessary, to alter some of the thresholds criteria.However, despite the high degree of accuracy of placement, the LODE procedure still only provides an approximation of the exact localisation (precision) of SNPs within a chromosome, since it is dependent on the accuracy of prior genome assembly as a reference framework and the density of known SNPs to allow positioning of unknown SNPs. Hence, the precision of positioning SNPs with the LODE procedure will increase with increasing SNP density and accuracy of the sequence map. However, quality of the assembly can be assessed by using the LODE procedure to confirm the location of SNP markers with assigned positions, and provides for an independent cross check as shown in the result section. The initial density of marker maps, in order for the LODE procedure to be effective, will depend upon the extent of LD in the population which is often population specific. In the case where no reference positions are available as in the case of http://www.sheephapmap.org/) can be used to place and validate SNP locations, and order of unplaced scaffolds where they contain SNPs with appropriate genotype information. The LODE procedure is likely to be of significance in the future as developments in next-generation sequencing technologies are providing deep sequencing coverage at an affordable price [LODE procedure can also be very helpful in refining sequence and genetic maps for species where comparative genome assemblies are used to build a virtual assembly for the species of interest, such as has recently been done for sheep . Populatle price -41. Thesle price  but thesle price  will allThe LODE procedure described in this study is an efficient and accurate procedure for positioning SNPs, and offers a means for positioning of unordered scaffolds containing SNPs. The LODE procedure will be helpful in refining genome sequence and checking assemblies, especially those being created from next-generation sequencing where high-throughput SNP discovery and genotyping populations are components of genome analysis.MSK conceived the method and the study, contributed in its design, data collection, analysis and was the primary author for assembling the manuscript. MH provided bioinformatics support in SNP positioning and comparative mapping. MN and JS participated in the method development and manuscript preparation. FWN contributed in the method development, interpretation and manuscript preparation. HWR is project leader and contributed in project concept, design, interpretation and manuscript preparation. All the authors have read and approved the final manuscript.The comparison of different threshold combinations for chromosomal assignments. This file presents the results from alternate criteria that were tested during the development of the preferred strategy for the LODE procedure.Click here for fileChromosome wise comparison of estimated (LODE) and known positions (Btau4.0) of 869 SNPs allocated a chromosomal position by LODE out a test set of 900 SNPs (MAF>0.05). This file contains 30 scatter plots, one for each bovine autosomes (1-29) and X-chromosome.Click here for fileThe comparison of estimated (LODE) and known positions (Btau4.0) of 91 SNPs allocated a chromosomal position by LODE out a test set of 300 SNPs (0.01<MAF ≤ 0.05). The comparisons for positions are presented in a signal scatter plot for all the 91 SNPs combined over all chromosomes.Click here for fileList of 5,314 SNPs (MAF>0.01) from three SNPs chips which were unpositioned on bovine assembly Btau4.0. This table presents the chromosomal positions for 4,688 of 5,314 SNPs estimated by LODE. The BLAST results against another bovine assembly UMD3.0 are also given.Click here for fileAssignment of 'Un' scaffolds to chromosomes by aligning all SNPs within 'Un' a scaffold to one chromosome by LODE procedure. This file contains list of 494 "Un" scaffolds, chromsomal assignments by LODE, number of SNPs in each scaffold and the length of each scaffold.Click here for fileDetailed chromosome-wise comparison of chromosomal assignments of 52,987 SNPs repositioned by LODE procedure with original positions on Btau4.0. This file contains 30 scatter plots, one for each bovine autosomes (1-29) and X-chromosome.Click here for file


Case-control genetic studies of complex human diseases can be confounded by population stratification. This issue can be addressed using panels of ancestry informative markers (AIMs) that can provide substantial population substructure information. Previously, we described a panel of 128 SNP AIMs that were designed as a tool for ascertaining the origins of subjects from Europe, Sub-Saharan Africa, Americas, and East Asia.In this study, genotypes from Human Genome Diversity Panel populations were used to further evaluate a 93 SNP AIM panel, a subset of the 128 AIMS set, for distinguishing continental origins. Using both model-based and relatively model-independent methods, we here confirm the ability of this AIM set to distinguish diverse population groups that were not previously evaluated. This study included multiple population groups from Oceana, South Asia, East Asia, Sub-Saharan Africa, North and South America, and Europe. In addition, the 93 AIM set provides population substructure information that can, for example, distinguish Arab and Ashkenazi from Northern European population groups and Pygmy from other Sub-Saharan African population groups.These data provide additional support for using the 93 AIM set to efficiently identify continental subject groups for genetic studies, to identify study population outliers, and to control for admixture in association studies. TheseIn the current study, we further examine the ability of a set of 93 AIMs to ascertain the ancestry of diverse population groups. This study was facilitated by the recent publically available Human Genome Diversity Panel (HGDP) genotypes that incThe individuals used in these studies include those from the HGDP, HapMap, the New York Cancer Project (NYCP)  and sampThe previous genotypes included those from 128 European Americans, 88 African Americans, 60 CEPH Europeans (CEU), 56 Yoruban sub-Saharan Africans (YRI), 19 Bini sub-Saharan Africans, 23 Kanuri West Africans, 50 Mayan Amerindians, 26 Quechuan Amerindians, 29 Nahua Amerindians, 40 Mexican Americans (MAM), 26 Mexicans from Mexico City, 28 Puerto Rican Americans, 43 Chinese (CHB), 43 Chinese Americans, 43 Japanese (JPT), 3 Japanese Americans, 8 Vietnamese Americans, 1 Korean American, 45 Filipino Americans, 2 unspecified East Asian Americans, and 64 South Asian Indian Americans. The Maya-Kachiquel were Maya from the Kachiquel language group as previously described and is f.The additional subject sets in the current study included the following HGDP genotypes: Adygei , Balochi (15), Bantu from Kenya (12), Bantu from South Africa (8), Basque (13), Bedouin (47), Biaki Pygmy (32), Burusho (7), Cambodian (10), Columbian (7), Daur (10), Druze (43), Kalash (18), Lahu (8), Mandenka (24), Maya (13), Mbuti Pygmy (15), Melanesian (17), Mongolian (9), Mozabite (30), Palestinian (26), Papuan (16), Pima (11), Russian (13), San (7), Uygur (10), Yakut (15), Yi (10), and Yoruba (25). Other additional samples not previously studied included Ashkenazi Jewish (40), Swedish (40), Irish (40) and other European Americans (190) from the NYCP. Genotypes from the HGDP subjects were obtained from the NIH Laboratory of Neurogenetics For all subjects, blood cell samples were obtained according to protocols and informed-consent procedures approved by institutional review boards, and were labeled with an anonymous code number linked only to demographic information.SNPs and individual samples with less than 90% complete genotyping information from any data set were excluded from analyses. SNPs that showed extreme deviation from Hardy-Weinberg equilibrium (p < 0.00001) in individual population groups were also excluded from these analyses.st was determined using Genetix software[F software that app software. Hardy-WPopulation structure was examined using STRUCTURE v2.1,11 paramPCA was performed using the EIGENSTRAT statistical package.st, was used as a common measure of population differentiation and calculates the inter-population compared to intra-population variation. Using the Weir and Cockerham algorithm[st values of selected population groups between random marker sets and the 93 SNP AIMs. The studies included samples derived from HapMap [Methods). The random SNP Fst values were obtained using three random non-overlapping sets of 3500 SNPs distributed over the autosomal genome (minimum of 50 kb distance between SNPs). The small differences in these triplicate independent samplings  indicate that this approach resulted in good estimations of paired Fst values.Wright's F statistic, Falgorithm  continental groups  were very similar when comparing the AIM and random SNP sets . Overall, the Fst values determined using the 93 AIM set were highly correlated with the Fst values determined using the random SNPs (r2 = 0.70) , multiple runs of this data set showed stable results at K = 5 and K = 6. When larger numbers of groups were assumed (i.e. K > 6) there was variation in the results. In particular runs various cluster groups would be present or absent. These included some runs in which Bedouin subjects corresponded to an individual cluster group, and others in which the South Asian populations was represented as a single cluster rather than two clusters as shown for K = 9 in Fig The population genetic structure of 1620 subjects was examined using the STRUCTURE program ,11 that 2 = 0.94; for the African cluster, r2 = 0.99; for the Amerindian cluster, r2 = 0.99; for the East Asian cluster, r2 = 0.99; for the European cluster, r2 = 0.90; for the South Asian cluster, r2 = 0.56; and for the Oceana cluster, r2 = 0.97. The weakest correlations were observed for the Burusho, Balochi, and Kalash South Asian ethnic groups, and the Adygei individuals. For these particular ethnic groups the membership in the European and South Asian groups was substantially different using the 93 SNP AIM set than the result obtained using 3500 random SNPs  and two sub-Saharan African clusters (r2 = 0.44) provides support for the ability of the 93 SNP AIM set to partially discern these additional aspects of population substructure.There was also a correlation between the 93 AIM set and 3500 random SNP set when the splitting of the European and Sub-Saharan African populations was observed . The correlation in membership in the two European clusters (r2 = 0.94), than did random sets of 500 SNPs (r2 = 0.90). The performance of the 93 SNP AIM set was also examined using restricted sample groups  to assess the European and sub-Saharan African contribution in the Mozabite ethnic group. For this comparison, the STRUCTURE analyses were performed using K = 2. Here, the correlation between the 93 SNP AIM set and 3500 random SNPs was stronger than the correlation observed for 500 random SNP sets and nearly comparable to 1000 random SNPs. The 93 SNP AIM set showed an r2 = 0.85 with a 3500 random SNP set. Each of three independent random 500 SNP sets showed lower r2 values with the 3500 random SNPs . For sets of 1000 random SNPs, very high correlations were observed . As a comparison, 93 random SNP sets showed much lower correlations . Together, these data suggest that the 93 SNP AIMs capture more ancestry information than random sets of 500 SNPs and are nearly comparable to using 1000 random SNPs.The performance of the 93 SNP AIM set was also compared with results obtained using different numbers of random SNPs. Overall, the 93 SNP AIM set showed marginally higher correlations with group membership determined using 3500 random SNPs (rDiscussion). Using >0.85 membership in a cluster as the criterion for inclusion, most of the subjects within each self-defined or collected population group corresponded to the expected continental group  of the individuals were included in the European group and none of the South Asian individuals were included in any of the other continental categories. Of these eight South Asian individuals included in the European group, five belonged to the Kalash ethnic group. For the South Asian subjects, the criterion of >0.85 membership in a specific cluster included only a minority (27/104) of these subjects. Decreasing the criterion to >0.50 membership resulted in the inclusion of the majority of the South Asian individuals within a single cluster (67/104). Using this criterion, there were only 6 individuals of other ethnic groups that were included in the South Asia group .As expected, the vast majority of individuals from admixed populations  were not included in any of the continental groupings. In addition, the Uygur individuals from central Asia and all but one of the Mozabite subjects were excluded from any of the continental groups using the >0.85 cluster group membership criterion.The same data set was also examined using principal component analyses (PCA). Nearly all of the variance detected using the 93 AIM set was defined by the first four principal components (PCs) Fig . SimilarAlso similar to the cluster groups defined by STRUCTURE, putative subject groups corresponding to continents or admixed population groups could be assigned using the individual subject eigenvector scores. Here, we used the self-identified or collected European, East Asian, sub-Saharan African, Oceana, and Amerindian populations groups to define the groups. The criterion for inclusion was the mean +/- two standard deviations (SD) of the eigenvector scores for each of the first four PCs . Using this definition, most of the subjects within each self-defined or collected population group were included within a self-matching group  included 87 of the 104 self-identified subjects in the South Asian grouping. However, 170 of the remaining 1516 subjects (not self-identified as South Asian) were also included in this group. When the criterion was changed to the mean +/- 1 SD, only 25 of the 104 self-identified South Asian subjects were included in this group and 8 other non-South Asian subjects were also included. Thus, for the 93 AIM set data, the PCA analyses did not perform well with respect to identifying South Asian subjects.Discussion).Using PCA, we further examined these individual population groups. There was partial grouping of certain ethnic groups when only those subjects within individual continental groups were analyzed separately Fig . This waThe current study provides additional validation for the use of a set of AIMs in human genetic studies. We show that a set of 93 SNP AIMs can distinguish a wide variety of diverse population groups in a sampling that includes the most populous groups in the United States as well as many groups from each continent with the exception of Australia. For example, even very diverse tribal groups within Africa can be readily distinguished from other continental groups. In addition, since the AIMs were in large part initially selected to distinguish between Amerindian, European, and African ancestry, the same set of AIMs provides good information for individual admixture in the largest admixed population groups (African American and Mexican American) in the United States. This AIM set also distinguished most of the South Asian and Central Asian individuals from those of European or East Asian origin and was also effective in grouping Oceana populations. Although there were specific limitations (e.g. for distinguishing South Asian populations), overall, the data suggest that this AIM set performs better than 500 random SNPs for distinguishing continental population differences.® platform that can be efficiently applied in small laboratory settings and is commercially available as a marker set .Although many previous studies have identified AIMs that distinguish particular combinations of continental groups ,18-20, tWe have previously discussed and provided general guidelines for the application of AIMs ,4. In thst values there is no clear distinction between these different European and South Asian ethnic groups  identify continental subject groups for genetic studies, 2) identify study population outliers, and 3) control for admixture in association studies.PAW and FMDLV are employees of Applied Biosystems.The study was conceived by MFS, FMDLV and designed by RN, RKo, and MFS. PKG, MEAR, RKi, LMB, GS, and JWB recruited subjects, and obtained and prepared DNA samples used in these studies. Genotyping was performed by RN and PKG. Analyses were performed by RN, RKo, CT and MFS. The manuscript was written by RN and MFS with contributions from RKo, CT, LMB, PAW, FMDLV and PKG. All authors have read and approved the final manuscript.SNP Ancestry Informative Markers. List of SNPs including sequence context and chromosomal locations.Click here for filest values between 93 SNP AIM set and 3500 random SNPsCorrelation of F. Figure showing the correlation between interpopulation Fst values calculated using the 93 SNP AIM set compared with the result using random 3500 SNPs (mean from three independent sets).Click here for file