The 11-M Madrid commuter train bombings of 2004 constituted the second biggest terrorist attack to occur in Europe after Lockerbie, while the subsequent investigation became the most complex and wide-ranging forensic case in Spain. Standard short tandem repeat (STR) profiling of 600 exhibits left certain key incriminatory samples unmatched to any of the apprehended suspects. A judicial order to perform analyses of unmatched samples to differentiate European and North African ancestry became a critical part of the investigation and was instigated to help refine the search for further suspects. Although mitochondrial DNA (mtDNA) and Y-chromosome markers routinely demonstrate informative geographic differentiation, the populations compared in this analysis were known to show a proportion of shared mtDNA and Y haplotypes as a result of recent gene-flow across the western Mediterranean, while any two loci can be unrepresentative of the ancestry of an individual as a whole. We based our principal analysis on a validated 34plex autosomal ancestry-informative-marker single nucleotide polymorphism (AIM-SNP) assay to make an assignment of ancestry for DNA from seven unmatched case samples including a handprint from a bag containing undetonated explosives together with personal items recovered from various locations in Madrid associated with the suspects. To assess marker informativeness before genotyping, we predicted the probable classification success for the 34plex assay with standard error estimators for a naïve Bayesian classifier using Moroccan and Spanish training sets (each n = 48). Once misclassification error was found to be sufficiently low, genotyping yielded seven near-complete profiles (33 of 34 AIM-SNPs) that in four cases gave probabilities providing a clear assignment of ancestry. One of the suspects predicted to be North African by AIM-SNP analysis of DNA from a toothbrush was identified late in the investigation as Algerian in origin. The results achieved illustrate the benefit of adding specialized marker sets to provide enhanced scope and power to an already highly effective system of DNA analysis for forensic identification. STR typing was also supplemented with standard mtDNA sequence analysis of a majority of exhibits. Seven complete STR profiles, originating from five personal items together with a handprint on the handle of the bag containing the undetonated El Pozo IED, failed to match any of the suspects held at the start of the court case in February 2007. Following an order from the Presiding Judge, the seven unmatched DNA extracts became the focus of specialist genotyping to attempt assignment of ancestry. The judge's direction specified that ancestry analysis should be confined to the comparison of European with North African variability in order to differentiate these two population groups alone.On the 11th of March 2004 ten improvised explosive devices (IED) were detonated on four commuter trains entering Madrid during the morning rush hour in a coordinated attack that killed 191 people and injured 1,755. DNA analysis supporting the subsequent investigation centered on standard forensic tests: autosomal and Y-chromosome STR profiling of 226 reference samples and more than 600 exhibits. The exhibits analyzed were predominantly contact traces that included fragments of detonated IEDs, suspect vehicles, an unexploded IED found at El Pozo train station and personal items recovered from bomb assembly sites or houses used by suspects. STR analysis used AmpAs quantities of DNA remaining from the initial STR analysis were very limited, an important pre-amble to the extended genotyping was estimation of likely success in assigning ancestry, i.e. choosing the approach that gave the minimum misclassification error from amongst the different ancestry marker sets available. The loci we opted to assess to differentiate Europeans from North Africans were: autosomal AIM-SNPs based on an established 34plex assay A simple Bayesian classifier analyzing the autosomal AIM-SNPs allowed an assessment of their ability to differentiate Europeans and North Africans by cross validation of training set samples used in the likelihood calculations. This permitted a review of classification error: how many training set samples were incorrectly assigned, and classification performance: the range of probabilities observed ahead of deciding the test priorities for scant case material. The classification error estimators of sensitivity and specificity, common to genetic association studies, simply correspond in this case to European and North African classification success in an arbitrary link to each population group.Population reference samples had been voluntarily provided at a previous date by residents of the Madrid metropolitan area with Spanish or Moroccan ancestry. Buccal swabs were collected by the Comisaria General de Policia Cientifica, Madrid with written, informed consent. Donors were informed the samples would be anonymized and used to derive allele frequency data representative of their population of origin. They verbally declared the population of origin of their parents to be either wholly Spanish or Moroccan and gave signed confirmation they understood the circumstances of sample use. DNA sets were stored with numbers and population labels only, at no point were numbers assigned to names from the consent forms. The University of Santiago de Compostela ethics committee gave institutional approval for the study.Training sets were made from the population reference samples collected as described above and comprised 48 DNAs each from Spanish and Moroccans resident in Madrid.Ancestry analyses were made of DNA extracted from the following forensic exhibits:Sample 1: Contact traces recovered (by swabbing) from a razor blade collected from the ruins of the Leganés flat occupied by a large group of suspects and subsequently destroyed by explosions triggered by the occupants.Sample 2: Contact traces recovered (by swabbing) from a handprint on the handle of a bag found at El Pozo station containing an undetonated IED.Sample 3: Buccal cells recovered (by cutting bristles) from a toothbrush collected at Leganés.Sample 4: Contact traces recovered (by cutting) from a blanket in a house occupied by a suspect.Sample 5: Contact traces recovered (by swabbing) from a woolen hat collected in the house where the bombs were assembled.Sample 6: Contact traces recovered (by cutting) from a scarf collected in a van used by suspects to go to one of the train stations attacked. The only female DNA of the seven extracts.Sample 7: Contact traces recovered (by cutting) from the same scarf as sample 6.Estimation of likely ancestry assignment error from each marker set was based on the observed distribution of variability between Europeans and North Africans either directly, by genotyping the 34 AIM-SNPs in samples of 48 individuals each from Spain and Morocco, or indirectly by reference to reported haplotype frequencies from previous population surveys of the Y chromosome The single base extension assay conditions, multiplex details and primer sequences were as previously described in detail From the 34-SNP profiles obtained a naïve Bayes classifier was used to make ancestry assignments as previously detailed i …popn that maximizes the posterior probability:l,…,xm) is the SNP genotype profile.The classification algorithm assigns an individual (ind) to the population from pophttp://mathgene.usc.es/snipper/) accepting custom training sets and classifying profile submissions based on a user's own choice of SNPs and populations (hyperlink: ‘classification with a custom Excel file of populations’). Assignment probabilities are given as −log likelihoods to ease handling of large differences in values. Misclassification error estimation options for uploaded training sets  include re-classification , verbose or non-verbose cross validation and bootstrap error. We used re-classification and verbose cross validation error estimators to assess the classification accuracy of the 34 AIM-SNPs for the two training sets. The complete 34-SNP training set profiles are available in supporting information, Unobserved alleles default to frequency 1/(2n+1) where n is training set sample size. The above algorithm is imbedded in an open-access web portal  but is computationally demanding. In contrast, classification performance for user defined SNP sets and populations can be obtained in seconds using the web portal.In order to compare the above classifier with an established alternative system of ancestry assessment we performed STRUCTURE analysis Five of the six male DNA samples had sufficient DNA left after AIM-SNP analysis to genotype Y-chromosome SNPs. A total of 21 Y-SNPs were genotyped using 3 multiplexes . Multiplex details, primer sequences and PCR conditions are as previously detailed As the five samples tested by Madrid with Y-STRs could be allocated into Eurasian haplogroups the choice of Y-SNP multiplexes was largely dictated by these prior results. Furthermore the Y-SNP multiplexes allowed a greatly improved resolution of the most common Eurasian haplogroups found on each side of the Mediterranean. Multiplex 1 analyzes SNPs more informative for European haplogroups and Multiplex 3 those more informative for North African haplogroups. Supporting information The two mtDNA hypervariable regions, HVS-I and HVS-II were sequenced for nucleotide ranges: 16024–16365 and 72–340, following previously described protocols Forty-eight Moroccan and Spanish individuals were originally available to us to make training sets. We also considered using a single North African population, termed Mozabite, included in the CEPH human genome diversity panel (CEPH-HGDP). The CEPH Mozabite samples were not used as these were considered to be a population much more divergent from Europe than others in this region. However training sets should ideally sample as much regional variation as possible. Therefore to reassess the original assignments made for the 11-M investigation we collected an additional 67 Spanish together with a broader North African sampling of: 97 Tunisians (three different regions), 35 Algerians and 31 Libyans with 29 CEPH Mozabite also included. The expanded training sets of 115 Spanish and 240 North Africans were then used in a fresh analysis to assign ancestry to the case profiles. The addition of extra reference populations also gave a well differentiated principal component analysis (PCA) plot based on two and three components. The PCA analysis had originally been attempted with the 48-sample training sets but the differentiation of Europeans and North Africans and case samples was not clear in some parts of the plot, notably in the middle of the two adjoining clusters.The results of the statistical assessment of the AIM-SNP classification accuracy from re-classification and cross validation are shown in Although the autosomal AIM-SNP genotyping assay amplified sub-optimal quantities of DNA, extracted mainly from contact traces on touched objects, 33 of 34 SNPs were successfully typed in all seven case samples. The SNP rs727811 was not detected in all tests performed due to problems with the extension primer that had not been corrected prior to receipt of the case material. Representative single base extension SNP genotyping profiles from four of the case samples are shown in supporting information, http://spsmart.cesga.es/snpforid.php) nInI values obtained and indicates a trend of mildly decreasing divergence between Europe and North Africa along an approximate NW to SE geographic axis in Europe. The divergence between Spanish and Moroccan samples is slightly higher than expected given the degree of shared mtDNA and Y-chromosome variability previously reported for these populations, but is in line with other divergence levels shown here by three mid-European populations from France, the French Basque region and North Italy. The Mozabites are more divergent from Europe than the Moroccans and this probably reflects lower levels of admixture with Europe since this population has a more isolated regional distribution amongst oases of the northern Sahara. Therefore the choice of a Moroccan training set to analyze differentiation between Europe and North Africa may take more account of the effects of admixture across the west Mediterranean than an initial plan to use the Mozabite sample as a training set. Finally, it has recently been inferred from analysis of X-chromosome SNPs that Moroccans are more genetically distant from other more homogenous populations of the North African Mediterranean region Due to the problem with rs727811 outlined above the training sets and their analyses excluded this SNP. Training set allele frequencies were estimated and compared with previously published data nI values are included at the bottom of In order to further assess the representativeness of the training sets we calculated divergence between the Spanish and a combined group of six CEPH-HGDP European populations . Prior to the analyses described here we had applied the same approach to analyze subjects in a drug response trial with self-declared ancestry to be ‘European-American’ or ‘African-American’. By examining the range of probabilities in both groups we were able to identify subjects showing lower than average ancestry probabilities who could therefore represent individuals with highly admixed parentage and genomic backgrounds (unpublished data). This highlights a widely recognized characteristic of self-declared ancestry: that it is not always an adequate indication of the genetic ancestry detected by a set of markers and their subsequent analysis Given the relatively straightforward framework devised for assessing population variability ahead of genotyping limited DNA, the near-complete SNP profiling of challenging case material  and the high classification probabilities obtained in four of the seven profiles, we considered the first application of autosomal AIM-SNPs to assist a major investigation to be successful. Understandably there has been continued discussion relating specifically to the origin of the handprint on the bag containing the IED, not least because possible involvement of the Basque separatist group ETA was suspected for a large part of the investigation period and a Spanish mining contractor was ultimately convicted of supplying the explosives used in the attacks. All European suspects in the case were excluded as donors of the STR profile of sample 3. While the source of the DNA remains an open question, one positive outcome of the ancestry analysis we provided has been an overhaul of procedures for handling items recovered from terrorist attacks or major crime scenes in Spain. Amongst the Presiding Judge's concluding remarks at the trial was the comment that any number of people could have handled the bag between collection from the scene and forensic analysis, since it was first thought the bag belonged to a victim.We were initially cautious in recommending the use of 34 SNPs to differentiate such closely related population groups, particularly as the groups, while occupying different regions, can be realistically considered to be members of the same Eurasian metapopulation An important observation in the combined analysis of training sets and case profiles was the generally close match of each sample's classification likelihood and their STRUCTURE cluster plot patterns indicating that each approach can aid interpretation by revealing different perspectives of the same Bayesian assessment of individual ancestry. Our classification approach provides a simple system to obtain profile-by-profile likelihoods that are more difficult to execute with STRUCTURE, while STRUCTURE can reveal patterns of group membership from the cluster plots that indicate the degree of admixture between two sets of reference populations. Therefore we would advocate use of both methods in parallel to fully assess the SNP variability of training sets and de novo samples. The use of principal component analysis provides a third way to compare the position of unknown samples with reference data. Analyzing extended training sets with PCA gave consistent patterns to those of STRUCTURE and our single profile classifier. Similarly we observed a high level of consistency between the ancestry assignments made with the original training sets and those based on expanded Spanish and more broadly based North African sampling. All the newly derived ancestry probabilities were consistent with the original values giving identical orders of magnitude and supporting the original assessments made with more limited training set sampling. The improvement in the North African cross validation probabilities underlines the importance of the widest possible sampling of regional diversity in order to properly prepare a Bayesian classifier. This is particularly important when differentiating closely related population groups that may also show high within-group variation.Finally, as the forensic genetics community becomes increasingly interested in the development of DNA tests that can assist an investigation with intelligence data supplementary to mainstream STR-based identification, it is worth emphasizing that ancestry analysis when carefully applied alongside STR profiling has the potential to yield information critical for the progress of the investigating team and their search for suspects. Though dealing with admixed populations can mean that an ancestry assignment does not necessarily allow the definition of a series of physical characteristic phenotypes normally more closely associated with ancestry such as skin, hair and eye color. Of particular interest here is the fact that a component marker of the 34plex AIM-SNP assay: rs12913832, was recently shown to be associated with blue eyes in homozygous G form, with the A allele associated with non-blue eyes Figure S1Example electropherograms of four case samples from single base extension genotyping. Solid colors depict peaks identified as extension products by reference to pre-validated mobility windows with an average range of +/−0.5 bp. Peaks with relatively high signal strength not shown as solid colors (notably in the green channel) represent either random signals or those outside the mobility window of each allele.(17.69 MB TIF)Click here for additional data file.Table S1http://mathgene.usc.es/snipper/analysispopfile2.html). Samples are ordered to match the LR values plotted in Excel file of 48 Spanish and 48 Moroccan 34plex training set profiles (excluding rs727811) in valid format for data input to the SNP ancestry analysis portal used ((0.28 MB XLS)Click here for additional data file.Table S2Reference allele frequencies and divergence values of 34 AIM-SNPs from Spanish and Moroccan training sets, HapMap CEU, six CEPH-HGDP European populations combined and Mozabite. SNPs listed in order of descending power of differentiation for the population comparison analyzed (i.e. training set divergence).(0.11 MB DOC)Click here for additional data file.Table S3http://mathgene.usc.es/snipper/analysispopfile2.html).Case sample genotypes and analyses. AIM-SNP profiles are provided in the required format for analysis with the training sets of (0.05 MB XLS)Click here for additional data file.Table S4Classification error estimation of extended training sets. (0.03 MB DOC)Click here for additional data file.Table S5New ancestry assignment probabilities for case profiles using modified 48-sample training sets (excluding 4 or 12 outlier Moroccan samples) and analyses using expanded Spanish and North African training sets.(0.04 MB DOC)Click here for additional data file.Table S6Classification error estimations for combinations of four autosomal AIM-SNP sets using CEPH-HGDP samples as training sets.(0.03 MB XLS)Click here for additional data file.


In order to identify single nucleotide polymorphisms (SNPs) efficiently from a species with a large genome, SNPs were identified from an expressed sequence tag (EST) database combined with High Resolution Melting (HRM) analysis.Cryptomeria japonica and HRM analysis was used to screen for polymorphisms in these STS markers. STSs were designed in two ways: 1) putative SNP sites were identified by comparing ESTs from specific contigs, then 226 primer pairs designed for the purpose to amplify these SNPs; 2) 348 primer pairs were randomly designed using reads from the 3' end of cDNA. HRM analysis revealed that 325 markers among eight individuals were polymorphic, and that STSs, including putative SNP sites, exhibited higher levels of polymorphism.A total of 574 sequence tagged sites (STSs) were generated from Our results indicate that the combination of SNP screening from an EST database combined with HRM analysis is a highly efficient way to develop SNP markers for expressed genes. This method will contribute to both genetic mapping and the identification of SNPs in non-model organisms. Cryptomeria japonica, is one of the most important commercial tree species in Japan. Recently, linkage maps have been constructed for this species, based on co-dominant markers including RFLP (Restriction Fragment Length Polymorphism), CAPS (Cleaved Amplified Polymorphic Sequences), and SSR (Simple Sequence Repeat) markers [Sugi,  markers -3. AlthoC. japonica has large genome (~10100 Mbp) [100 Mbp) , therefo100 Mbp) -7. Redun100 Mbp)  and Qual100 Mbp) . We usedAfter mining putative SNPs from a database, methods to identify STS polymorphisms are required. HRM analysis is an efficient SNP detection method that identifies differences in the shapes of melting curves between different genotypes using an intercalating dye that binds to the double-stranded DNA . In shorC. japonica were obtained from the NCBI EST database. Putative contaminant DNA  was excluded on the basis of the results of BLAST analysis. Clusters of ESTs were then identified by the CAP3 program; the minimum length and percentage identity of overlap was set to 100 and 95 respectively [C. japonica to genomic sequences of Arabidopsis with option for interspecies alignment. Although C. japonica is phylogenetically distant from Arabidopsis, it was reported that 11.5% of ESTs derived from 5'-terminal sequences of cDNAs exhibited significant similarities to gene coding sequence of Arabidopsis at nucleotide sequence level [Primer design method is illustrated in Figure ectively . For theectively , by compce level . Any detce level  to ampliC. japonica individuals. Six of the individuals were from natural populations, representing the species' natural distribution in Japan: Yakushima, Shimowada, Ishinomaki, Oki, Ajigasawa, and Ashu. Two further sources of parental material were used for mapping populations, YI38 and YI96. These two samples were derived from a local cultivar on Kyushu-island. Since SNP markers detected by QualitySNP may include spurious markers , but those that segregate in an expected manner are likely to be true SNPs, the validity of the HRM markers was tested by segregation analysis using the progeny of YI38 and YI96. DNA of all plant material was extracted using a modified CTAB method [Polymorphisms of the STS markers were screened with eight B method .GXL DNA polymerase (Takara Co. Ltd.), 1 × buffer provided for the GXL DNA polymerase that contained 1.0 mM MgCl2, 0.2 μM of each primer, 0.5 ul DMSO, 0.5 ul of EvaGreen, and 5 ng of template DNA . The PCR amplification was carried out for 10 min at 95°C, followed by 45 cycles of 40 sec at 94°C, 30 sec at 60°C, and 15 sec at 72°C. High resolution Melting Analysis was carried out using a Lightcycler 480 (Roche) according to the manufacturer's instructions. If the amplification product yield was low, we decreased the PCR annealing temperature to 56°C.PCR amplification was performed in 10 μl reaction volumes containing 200 μM of each dNTP, 0.8 units of Cryptomeria japonica were available on the NCBI EST database. After removing possible organelle and bacterial genes, 55530 ESTs were assembled into 10368 contigs and 13783 singlets. In the QualitySNP analysis, contigs with at least four ESTs were considered for SNP screening. Of the 10368 contigs, 3809 (3310919 bp) were screened and 1246 SNPs with high confidence were found in 314 contigs . The overall SNP frequency was one SNP per 2657.2 bp for the 3809 screened contigs.A total of 55634 ESTs of http://www.phrap.org, and PolyBayes. SNP abundance was estimated as one SNP per 660 bp in a set of 940 contigs representing 942216 bp. A similar study was also carried out for white spruce, and 12264 SNPs were found when 6459 contigs of at least two cDNA clones were surveyed with Polybayes [C. japonica appears to be much lower. This may be the result of the different software used, the numbers of ESTs screened, or the number of individuals (or varieties) examined in the EST database. In our study, parameters used in EST clustering and SNP detection were severe to avoid assembling paralogous sequences into contigs; this may have also affected the SNP discovery rate.In the case of maritime pine , SNPs weC. japonica, the frequency of segregation sites occurring in transcribed regions ranged from one segregation site every 89.79 bp to 1047 bp [The predicted SNP distribution was uneven and there was high variation in SNP frequency among the 314 contigs, ranging from one SNP every 3.6 bp to 1967 bp Figure . In a st 1047 bp . SNP freFor comparison, 412 primer pairs were also randomly designed from 3' ESTs (singlet-STS). These primer pairs were designed from 3' ESTs because previous studies have suggested that STSs derived from 3' ESTs are more polymorphic than STSs derived from 5' ESTs .As a consequence, 226 and 348 STSs were successfully amplified from contig-STS and singlet-STS, respectively Table . Single 2 progeny of them. The results revealed that 128 of the 148 STSs (86.5%) exhibited the expected segregation from parental genotypes (data not shown), thus detected polymorphisms are unlikely to have resulted from nucleotide differences in paralogous genes. Higher polymorphism in the contig-STSs and the segregation in expected manner of those HRM markers indicated that QualitySNP detected actual SNPs in the transcribed region.Obtained STSs were then analyzed by HRM analysis. One-hundred and seventy-three of the 226 STSs from the contig-STSs (76.5%) were polymorphic among the eight individuals used in this study Figure ; this waWhen SNPs were screened by CAPS analysis, 72.3% of STSs were polymorphic . TherefoC. japonica.We demonstrated here that using HRM analysis for converting expressed sequences to DNA markers is very useful for genetic mapping. Although there are SNP typing methods that have higher throughput than HRM analysis, HRM analysis can be alternative method for moderate scale genome project in aspects of time and cost. The method described here used mainly publicly available resources, therefore it is easily applicable to non-model organisms with large genome size, such as The authors declare that they have no competing interests.TUI designed the strategy for SNP discovery and HRM analysis, and developed the perl scripts needed in primer design. YT conducted the experiments for HRM analysis. YM conducted the segregation analysis for mapping populations. YT supervised the project. TUI wrote the paper. All authors read and approved the final manuscript.


Gadus morhua) is a groundfish of great economic value in fisheries and an emerging species in aquaculture. Genetic markers are needed to identify wild stocks in order to ensure sustainable management, and for marker-assisted selection and pedigree determination in aquaculture. Here, we report on the development and evaluation of a large number of Single Nucleotide Polymorphism (SNP) markers from the alignment of Expressed Sequence Tag (EST) sequences in Atlantic cod. We also present basic population parameters of the SNPs in samples of North-East Arctic cod and Norwegian coastal cod obtained from three different localities, and test for SNPs that may have been targeted by natural selection.The Atlantic cod  and Norwegian coastal cod (NCC). The average heterozygosity of the SNPs was 0.25 and the average minor allele frequency was 0.18. The SNP markers presented here are powerful tools for future genetics work related to management and aquaculture. In particular, some SNPs exhibiting high levels of population divergence have potential to significantly enhance studies on the population structure of Atlantic cod. Gadus morhua L) is a well-known teleost inhabiting the Atlantic Ocean from North Carolina to Greenland in the west, and from the Bay of Biscay to the Arctic Ocean in the east. Atlantic cod supports a commercially important fishery [The Atlantic cod ( fishery . However fishery ). The spGenetic markers are imperative for proper management of cod and for cod aquaculture, as well as for ensuring a sustainable coexistence of wild and farmed stocks. There is a need for genetic markers for refining the stock structure and assigning fish to populations ,4. TodayHb-I) alleles [Pan I) locus [b locus [Pan I locus, NEAC predominantly have the Pan IB allele (> 90%), whereas NCC have predominantly the Pan IA allele [The largest population of Atlantic cod at present is the North-East Arctic cod population (NEAC) , located alleles -22, the I) locus ,15, and I) locus ,16. LittI) locus ,23 and mI) locus ,16 or at[b locus . At the Expressed Sequence Tag (EST) sequences are very useful resources in genomics, and can be used for the detection of Single Nucleotide Polymorphism (SNP) markers . SNPs arA total of 17,056 EST sequences were aligned and used to detect SNPs. The ESTs were 5' sequences representing eight different cDNA libraries with five to several hundred animals per library. The average sequence length was 508 ± 137 bp (SD). The alignment of ESTs produced 1186 contigs and 207 single sequences , and are hereafter treated as one population, termed NCC-S. The animals that were genotyped had been preselected from larger samples based on their Pan I genotype. Thus, all NEAC were Pan IBB and all NCC were Pan IAA.Assays were successfully developed for 648 of the 724 putative SNPs. Five hundred and ninety-four of these 648 SNPs were genotyped on a collection of Atlantic cod sampled at four different locations; one site populated primarily by NEAC, one northern (NCC-N) and two southern (NCC-S1 and NCC-S2) sites populated primarily by NCC  at individual markers with large FST-values, indicating that a few high-FST SNPs could be sufficient to substantially improve the accuracy of NEAC/NCC assignments compared to assignment with Pan I alone.Since animals had been assigned to populations based on genotypes at After culling of misclassified animals, the fraction of polymorphic SNPs that were not in Hardy-Weinberg Equilibrium (HWE) (P < 0.05) was 0.10 for NEAC, 0.04 for NCC-N, and 0.09 for NCC-S, i.e. slightly higher than expected by chance. Four SNPs, Gm376_0668, Gm0927_0134, Gm0786_0527, and Gm392_1244, were out of HWE in all three populations, while 12 other SNPs were out of HWE in two populations [see Additional file FST-values were 0.057 and 0.024 for NEAC – NCC-N and NCC-N – NCC-S respectively, both significantly different from zero (P < 0.05). At individual markers, FST-values ranged from 0 to 0.83  on the outlier SNPs themselves or on other SNPs within the same gene, or ii) on other loci in linkage disequilibrium with the outlier SNPs. Given the large number of genes in any higher organism, the even larger number of SNPs, and the limited number of markers tested in this study, selection is quite likely to be acting on genes in LD with the observed genes rather than on the observed genes themselves. Determination of LD levels in Atlantic cod will thus be essential to differentiate these two possibilities. If LD can be found across large genomics regions, the outlier genes/SNPs detected here are not likely to be under direct selection. However, if LD can be found across only minor genomic regions, selection is likely to be acting directly on the genes/SNPs themselves, in which case the data indicate that as much as 11% (26 out of 235) of genes in Atlantic cod are under positive selection.We did not aim to thoroughly investigate the complicated issue of Atlantic cod population structure in this study, given the limited number of populations investigated and the pre-selection of individuals according to their In this study, we have described the identification and characterisation of several hundred potential SNP markers from Atlantic cod, resulting in 318 SNPs that were polymorphic and reliably scored in a sample taken from three different sampling sites. The SNPs have the potential to significantly enhance the resolution of population structure in the Atlantic cod and provide more precise estimates of effective population sizes and rates of gene flow. Even from this preliminary study, it is clear that a significant fraction of loci in Atlantic cod display very large differentiation between North-East Arctic cod and Norwegian coastal cod, and thus are very likely to be affected by directional selection. The SNPs also represent a valuable resource for genetic mapping in Atlantic cod, and may be used for performing genome scans for quantitative trait loci (QTL) for phenotypic traits. Work is presently underway in our laboratory to construct a linkage map containing these SNPs and anchoring microsatellites.EST sequences in Atlantic cod were obtained from eight different cDNA-libraries, made from tissues collected from the head kidney, intestine, liver, pyloric saeca, spleen , and embryos (two libraries). For the embryo libraries, 500 different embryos were used, while for the other libraries, 15 animals were used in total. The animals used for EST sequencing were all Norwegian coastal cod. Samples from individual fish were stored in RNALater (Ambion) prior to use. Total RNA was extracted from each individual separately by the RNA Easy Mini Kit  and an equal amount of RNA from five different individuals was pooled. Enrichment for polyA RNA was conducted using the Oligotex mRNA Midi Kit from Qiagen. Nucleic acid quality was measure using Agilent 2100 Bioanalyzer  and quantity was measure using Nanodrop ND-1000 . Directional cDNA libraries were constructed using 5 μg of polyA enriched RNA using the pBluescript II XR cDNA library construction kit from Stratagene  according to the manufactures recommendations. The quality (size)  and quantity (Nanodrop) of the produced cDNA was assessed prior to ligation. XL10-gold cells were transformed and blue/white screened. Positive (white) colonies were randomly picked and grown overnight in 96 well plates. Plasmids were purified using the Montage Plasmid Miniprep kit . Sequencing was conducted using BigDye chemistry and they were run on an ABI3700 sequencer from Applied Biosystems . Sequencing was done from the 5' end of transcripts.Base-calling from chromatograms was performed using the Phred program . Vector -10. All sequences with a significant BLASTX hit in Swiss-Prot were annotated by annotation transfer, inferring similarity of function from sequence similarity by applying the Gene Ontology (GO) assignments for the UNIPROT database, produced by the GOA project of the European Bioinformatics Institute [In order to annotate the SNP-containing contigs, BLASTX was run against the Protein data base, Swiss-Prot and non-redundant GenBank databases. A significant database hit was defined as having an expectation value  below 1.0 × 10nstitute . The gennstitute  for detanstitute . The seqnstitute .Pan I genotypes [Pan I genotypes, where Pan IAA were regarded as NCC and Pan IBB were regarded as NEAC  from southern and northern locations as well as from North-East Arctic cod (NEAC) Table . All samenotypes . The fisAC Table . Pan I gGenotyping of SNPs was performed using the MassARRAY system from Sequenom . PCR-primers and extension-primers were designed using the software SpectroDESIGNER v3.0 (Sequenom). Multiplexes and primer sequences can be found in Additional file FST values were calculated using the "pairwise FST" function of Arlequin v3.11 [FST values at individual SNPs were calculated using the AMOVA function of the same program. Permutation testing with 1000 iterations was used to calculate p-values for mean and locus-by-locus FST values. Arlequin v3.11 was also used for exact tests of HWE equilibrium , and for assignment of individual genotypes to populations. Outlier SNPs were tested for using the method of Beaumont and Nichols [datacal function. Simulations were then run using this average heterozygosity, 20 000 iterations, and assuming 10 demes, 3 populations, 40 individuals per sample, and a stepwise mutation model . Simulain silico SNP discovery, and annotations. FN performed the library construction and EST sequencing. MD provided and prepared DNA samples, and did Pan I genotyping of samples prior to the study. KTF provided DNA samples, took part in planning of the study, and was project leader. SEF provided DNA samples and guidance for the project. PRB did multiplexing of SNPs and took part in SNP genotyping. SL provided SNP genotyping facilities, and took part in SNP discovery. All authors took part in writing, and approved of, the manuscript.TM inspected contigs for SNPs, coordinated SNP genotyping, did data analysis, and wrote a draft of the manuscript. BH did contig assembly, FST-values, tests for HWE)SNP details. Properties of all 318 SNPs that were polymorphic, segregating, and could be reliably scored Population parameters of outlier SNPs. Properties of high-Click here for file


The Soybean Consensus Map 4.0 facilitated the anchoring of 95.6% of the soybean whole genome sequence developed by the Joint Genome Institute, Department of Energy, but its marker density was only sufficient to properly orient 66% of the sequence scaffolds. The discovery and genetic mapping of more single nucleotide polymorphism (SNP) markers were needed to anchor and orient the remaining genome sequence. To that end, next generation sequencing and high-throughput genotyping were combined to obtain a much higher resolution genetic map that could be used to anchor and orient most of the remaining sequence and to help validate the integrity of the existing scaffold builds.A total of 7,108 to 25,047 predicted SNPs were discovered using a reduced representation library that was subsequently sequenced by the Illumina sequence-by-synthesis method on the clonal single molecule array platform. Using multiple SNP prediction methods, the validation rate of these SNPs ranged from 79% to 92.5%. A high resolution genetic map using 444 recombinant inbred lines was created with 1,790 SNP markers. Of the 1,790 mapped SNP markers, 1,240 markers had been selectively chosen to target existing unanchored or un-oriented sequence scaffolds, thereby increasing the amount of anchored sequence to 97%.We have demonstrated how next generation sequencing was combined with high-throughput SNP detection assays to quickly discover large numbers of SNPs. Those SNPs were then used to create a high resolution genetic map that assisted in the assembly of scaffolds from the 8× whole genome shotgun sequences into pseudomolecules corresponding to chromosomes of the organism. This 6.5× assembly contained a total of 3,118 scaffolds totaling 993.5 Mb of sequence. Using the soybean Consensus Map 4.0, which contains a total of 5,500 markers  has nucleotide diversity (θ) of about 0.001 .Seeds of the ue of 40 . The Illrd column of the BLAT-output PSL format), no "N" bases in the alignment, and no gaps present in the query sequence and no gaps in the reference sequence, 3) alignments that had any conflicting nucleotides were eliminated, 4) SNPs where both flanking 25 nt regions were repetitive were eliminated , 5) SNPs had to be supported by at least two short sequence reads, 6) filter out any remaining sequence containing an N. In addition, Genome Analyzer sequence reads with matches to chloroplast sequence [GenBank:NC_007942] or Nicotiana mitochondrion sequence [GenBank:NC_006581] were discarded.Initially the GMAP method was used for SNP discovery from the sequence produced from the reduced representation library with the Illumina Genome Analyzer. GMAP software  was usedvalidation using multiply-supported SNPs (VMS); one for validation using singly-supported SNPs (VSS); and a production run. The runs for validation used the same filtering steps described below for the production run, except where noted. The following constraints were used in the filtering for the identification of putative SNPs using Maq software: 1) Occurrences of two or more SNPs within a 25-base window were eliminated, 2) SNPs with ambiguous consensus bases  were eliminated, 3) a minimum consensus-base quality score of 20 for VMS and VSS (27 for the production run) was required, 4) an average read-copy of 1.00 i.e., the SNP maps to one place in the Williams 82 reference genome, 5) a minimum Maq mapping score of 30 was required, and 6) with VMS a minimum of two short sequence reads were required to support each putative SNP. In addition, Genome Analyzer reads with matches to chloroplast sequence [GenBank:NC_007942] or Nicotiana mitochondrion sequence [GenBank:NC_006581] were discarded. Additionally, we removed SNPs from repetitive regions, using the following protocols. For VMS and VSS, extract the 121 nt region centered on the SNP and exclude the SNP if this region contained an "N". If either 25-mer flanking a SNP hit any other place in the genome with at most two mismatches using an ungapped BLAST it was considered repetitive; if both were repetitive, the SNP was excluded. For the production run, extract a 601 nt region centered on the SNP and exclude the SNP if this region contained an "N". If any 25-mer in the 300 nt up- or downstream region hit five or more other positions in the genome with at most two mismatches using an ungapped BLAST, it was considered repetitive; SNPs whose flanking regions were more than 1/3 repetitive were also excluded.While the above work was in progress, the Maq mapping and assembly software  was testBased upon the underlying Williams 82 preliminary 4× scaffold assembly, polymerase chain reaction (PCR) primers were designed using Primer 3 software . The pri5-derived recombinant inbred lines (RIL) were created at the Beltsville Agricultural Research Center, Beltsville, MD, USA, from a cross made in 2004 between the soybean cultivar Williams 82 [2 seeds were planted in the field at Beltsville, MD the summer of 2005. Approximately 10 to 15 F3 seeds were harvested from each of 1,690 individual F2 plants. In the summer of 2006, five F3 seeds harvested from each of 1,000 randomly selected F2 plants were planted in hill plots and subsequently thinned to one F3 plant two weeks after planting. A total of 10 to 15 F4 seeds were harvested from each of 932 F3 plants. In the winter of 2006-2007, three F4 seeds from 600 randomly selected F3 plants were planted in the greenhouse for the next generation advance and subsequently thinned to one plant after two weeks. Ten to fifteen F5 seeds were harvested from each of 583 F4 plants. In the summer of 2007, five F5 seeds from each of the 583 F4 plants were planted in hill plots and thinned to one F5 plant two weeks after planting. DNA was isolated from 470 RILs randomly selected from the 583 F5 plants by taking leaf tissue from the single F5 plant from which the RIL was derived. The Qiagen DNeasy 96 Plant DNA extraction kit  as per the manufacturer's protocol was used to obtain purified DNA for the GoldenGate Assay analysis. All seeds were harvested from each F5 plant to create an F5-derived line. To help minimize segregation distortion in creating the population, seeds in all generations were harvested as soon as individual plants became mature to avoid loss of seeds due to shattering.Four hundred and seventy Fliams 82  and the A total of 1,536 SNPs were selected to develop a 1,536 GoldenGate assay [Additional file For GoldenGate assay design all SNPs were required to have a designability rank score >0.4, which is predictive of a moderate to high success rate for the conversion of the SNP into a working GoldenGate assay. The 1,536 chosen SNPs made up the custom Oligo Pool All (OPA) which was given the name SoyOPA-4. To help anchor and align the genetic map created by the SNPs on SoyOPA-4, the previously designed and tested SoyOPA-3  was testJoinMap 3.0 software  was usedDLH designed and oversaw the study, developed the mapping population, designed the GoldenGate OPA, performed genotyping analysis of the GoldenGate assay, and drafted the manuscript. SBC, NW and QS performed the SNP discovery, designed the GoldenGate OPA, and performed additional analysis of the short read data. SBC also assisted in preparing the manuscript. QS created the high resolution linkage map. ECF created the reduced representation library, performed sequencing of the validation sets, helped to develop the mapping population, and assisted in preparation of the manuscript. RCS helped conceive the study. JES assisted in the analysis of the genetic map data and in preparing the manuscript. ADF and GDM assisted in an initial analysis of the genome analyzer short reads. PBC designed and oversaw the study, developed the mapping population, and assisted in preparing the manuscript. All authors read and approved the final manuscript.SoyOPA-4 SNPs. The 1,536 SNPs along with the flanking sequence to design the GoldenGate assay SoyOPA-4.Click here for fileWilliams 82 × PI 468916 genetic map. The high-resolution Williams 82 × PI 468916 genetic map created with 1,790 SNPs genotyped on 268 or 444 recombinant inbred lines (RILs). Also shown is the high-resolution map resulting when only using the 1,254 SNPs genotyped with SoyOPA-4 on 444 RILs and the Consensus Map 4.0 position for any SNPs in common between the two genetic maps.Click here for fileWilliams 82 × PI 468916 comparison to the soybean Consensus Map 4.0. The charts of the 20 linkage groups from the Consensus Map 4.0 (left chart) compared to the same 20 linkage groups produced from the 444 recombinant inbred lines of Williams 82 × PI 468916 (right chart).Click here for file


Boophilus  microplus (cattle tick) impact negatively on animal production systems. Host resistance to tick infestation has a low to moderate heritability in the range 0.13 - 0.64 in Australia. Previous studies identified a QTL on bovine chromosome 10 (BTA10) linked to tick burden in cattle.Infestations on cattle by the ectoparasite ITGA11  gene. Initially, we genotyped 1,055 dairy cattle for the 17 SNP, and then genotyped 557 Brahman and 216 Tropical Composite beef cattle for 11 of the 17 SNP. In total, 7 of the SNP were significantly (P < 0.05) associated with tick burden tested in any of the samples. One SNP, ss161109814, was significantly (P < 0.05) associated with tick burden in both the taurine and the Brahman sample, but the favourable allele was different. Haplotypes for three and for 10 SNP were more significantly (P < 0.001) associated with tick burden than SNP analysed individually. Some of the common haplotypes with the largest sample sizes explained between 1.3% and 1.5% of the residual variance in tick burden.To confirm these associations, we collected genotypes of 17 SNP from BTA10, including three obtained by sequencing part of the ITGA11 gene. The presence of a significant association in such widely divergent animals suggests that further SNP discovery in this region to detect causal mutations would be warranted.These analyses confirm the location of a QTL affecting tick burden on BTA10 and position it close to the  Tick infestation has a detrimental impact on animal production and ticks are one of the main vectors of pathogenic micro-organisms of veterinary and zoonotic importance ,2. deCas2h) estimates of tick burdens due to the ixodid tick Boophilus  microplus in Australia range from 2 h= 0.13 - 0.64 . The linkage disequilibrium (LD) between SNP was estimated using Haploview 4.1 [The SNP genotypes were tested for Hardy-Weinberg equilibrium (HWE) within breed type using PLINK 1.05 [, http://view 4.1  for DTE,view 4.1  implemenview 4.1 ,42. We aAssociation between each SNP or haplotype was evaluated by regression of the residual tick count or score on the number of copies of a reference allele. Allele associations were performed one SNP at a time. Significance was evaluated using a t-test of the slope of the regression over its standard error for each marker individually. For each haplotype, the individuals were scored for the number of copies of the haplotype they possessed, each haplotype was considered an independent event, analogous to analyses of DNA microsatellites , and theLRPN and WB planned the experiments and wrote the manuscript. LRPN analyzed the genotypic data, RJB and BEH collected DNA samples. LRPN, RJB and BEH genotyped the animals. LRPN identified SNP by DNA sequencing, KCP defined the beef cattle phenotypes. All authors read and approved the final manuscript.ITGA11 geneDescription of discovered SNP in the . Table describing the discovered SNP Btau 4.0 positions and dbSNP ss numbers.Click here for fileITGA11 gene regionLinkage disequilibrium (LD) between markers at the . Linkage disequilibrium (LD) between markers at the ITGA11 gene region.Click here for fileEleven loci haplotype association with tick burden: interpolated missing data. Eleven loci haplotype association with tick burden using the interpolated missing data.Click here for fileDescription of the haplotypes reconstructed using 10 loci and the 3-locus sliding window. Description of the haplotypes reconstructed using 10 loci and the 3-locus sliding window.Click here for file


Genome-wide association studies prove to be a powerful approach to identify the genetic basis of different human diseases. We studied the relationship between seven diseases characterized in a previous genome-wide association study by the Wellcome Trust Case Control Consortium. Instead of doing a horizontal association of SNPs to diseases, we did a vertical analysis of disease associations by comparing the genetic similarities of diseases. Our analysis was carried out at four levels – the nucleotide level (SNPs), the gene level, the protein level (through protein-protein interaction network), and the phenotype level.Our results show that Crohn's disease, rheumatoid arthritis, and type 1 diabetes share evidence of genetic associations at all levels of analysis, offering strong molecular support for the current grouping of the diseases. On the other hand, coronary artery disease, hypertension, and type 2 diabetes, despite being considered as a natural group with potential aetiological overlap, do not show any evidence of shared genetic basis at all levels.Our study is a first attempt on mining of GWA data to examine genetic associations between different diseases. The positive result is apparently not a coincidence and hence demonstrates the promising use of our approach. Human genomes differ only in about 0.1% from each other, but this small genomic difference contains the key difference that can determine a person's susceptibility to diseases. In order to identify the genomic basis of certain diseases, genome-wide association (GWA) studies, an approach to find genetic variations (e.g. single nucleotide polymorphisms – SNPs) associated with a particular disease have become increasingly popular and useful. With completion of the Human Genome Project and HapMap Project and availability of dense genotyping chips and assembly of large and well-characterized clinical samples [In this study, we focused on understanding the genetic basis of associations between seven common human diseases, using the data generated by a recent extensive GWA study undertaken in the British population . The stuIn this study, we took advantage of the GWA data of the seven diseases to examine whether different diseases share some level of commonality in genotypes. Our goals are to: (1) fish out sets of SNPs associated with the seven diseases in the WTCCC study and analyze whether there are overlaps between different sets of SNPs that correspond to different diseases, (2) analyze commonalities between genes associated with the SNPs in these diseases, (3) construct protein-protein interaction networks for the sets of genes and explore common features of the networks across the diseases, and (4) analyze the phenotypic similarities between the diseases.-4 because these SNPs are weakly associated with the diseases and are more likely owing to some statistical noise than to real biological significance. The SNPs with P-value = 10-4 were clustered into blocks. There are around 100 clusters (blocks) of SNPs for each disease, with blocks that are on the same chromosome at least 1 MB (mega base pairs) apart from each other.The GWA study by the WTCCC produced a list of SNPs that are associated with each of the seven diseases. The confidence of association of a SNP with a specific disease is represented by the SNP's P-value. The lower the P-value is, the more likely that the SNP is associated with the disease. Similar to the WTCCC study , we discIn order to measure the degree of commonalities between SNP blocks of different diseases, we employed two different metrics, the Jaccard index and the total length measurement. For both metrics, the larger the metric is, the more similar two cluster patterns are to each other. First, figure Second, we also used the total length to measure similarities between two sets of clusters. Figure Therefore, results on both measurements of the similarities in SNP blocks for disease pairs show that the grouping of CD, RA, and T1D has a strong genetic basis, whereas the grouping of CAD, HT, and T2D has little genetic evidence. Within the green group , it is unclear which two of them have a higher degree of commonality. For example, the Jocobian value between CD and RA is the highest within the three diseases, whereas the total length measurement between RA and T1D is the largest.Since strong correlations in SNP blocks are observed between CD, RA, and T1D, one would wonder whether paired blocks/clusters between these three diseases have highly similar distribution patterns. We plotted three sets of paired clusters between these three diseases in figure As some of the SNP clusters reside in genes that may have different biological functions ,5, we caThe number of candidate genes shared between diseases can indicate association of diseases. The more candidate genes shared by two diseases, the more closely related between the two diseases . Thus, w2 KB upstream regions of genes tend to be enriched with regulatory elements . SNPs inAnalysis of SNPs can show us the low level of association between primary sequences and diseases. But in order to understand the disease pathogenesis from a functional point of view, we need to study SNPs (and thereof genes) at the protein level because the majority of genes must be translated into proteins to participate in protein-protein interaction networks or pathways and to perform their biological functions . MappingUsing the STRING database, we constructed seven disease networks. Figure Shared hub protein analysis reveals two findings Table . First, Therefore, our results show that it is possible to identify the underlying molecular mechanism that might contribute to the commonality between different diseases, either in terms of pathogenesis or disease phenotypes, by tracing shared hub proteins between diseases.All three levels of analysis of the seven diseases show consistent results that support the grouping of CD, RA, and T1D, but not the grouping of CAD, HT, and T2D. In order to further verify our results, we compared the phenotypes of the seven diseases to see how they are related at the phenotypical level using MimMiner. MimMiner is a phenotype comparison tool that uses the Online Mendelian Inheritance in Man (OMIM) database and various text mining algorithms to classify phenotypes . The MimWe analyzed associations between GWA studies of seven diseases. Our study was carried out at four levels – analysis at the nucleotide level (SNPs), analysis at the gene level, analysis at the protein level (protein-protein interaction network), and analysis at the phenotype level. For one group of diseases , strong associations are found across all levels of analysis. In particular, our results indicate that within this group, RA and T1D are more strongly associated than they are to CD, suggesting RA and T1D are originated by highly similar molecular mechanisms, which can shed light on further exploration of these diseases. For another group of diseases , no genetic association is found at all levels of analysis. The negative result could be due to inappropriate grouping of the diseases  or that our analysis might be too primitive to recover the hidden genetic associations for this group. However, given our rather thorough analysis to uncover the relationships among these diseases and the strong confirmation of the grouping of CD, RA, and T1D, we think that the latter is highly unlikely.Our study is a first attempt on mining of GWA data to examine genetic associations between different diseases. The positive result is apparently not a coincidence and hence demonstrates the promising use of our approach. With the increasing amount of GWA data, it becomes increasingly important to examine whether there are any hidden relationships between different diseases, especially the diseases without apparent phenotypic commonalities (e.g. similar disease symptoms). At the same time, many diseases have highly similar symptoms such that it remains controversial whether they are different diseases or actually one disease with different names. For example, there has been a long-standing controversy whether schizophrenia and bipolar are one or two disease traits . It is cIn order to explore the relationship between SNPs of different diseases, we clustered SNPs into blocks and examined whether there are commonalities between SNP blocks of different diseases. The motivation for clustering SNPs into regions is that neighboring regions of the genome tend to have similar expressions and likely function together, by analyzing the SNPs on the level of clusters and genomic regions rather than specific points, we are more likely to identify patterns of a larger scale. Specifically, we examined the distribution of the distances between all of the two nearest SNPs on the same chromosome, and found that using the 1 MB as a cutoff value for clustering SNPs into blocks (i.e. the distance between SNP blocks is greater than 1 MB) achieves a good balance between the total number of blocks produced and the cutoff distance. We also tried several hierarchical clustering methods and found that the results are highly similar to our straightforward clustering method (i.e. using a cutoff). Therefore, for the remainder of our work, we used the SNP blocks produced by the straightforward clustering method.To quantify the degree of commonalities between two diseases in terms of SNP blocks, we compared the distribution of the set of SNP blocks belonging to two diseases. We observed that some SNP clusters of different diseases share the same region or are physically very close to each other. We considered these SNP blocks to be paired blocks. Therefore, we can compare cluster patterns of different diseases by examining the prevalence of paired clusters. Figure We also use the total length to measure the similarity between clusters. The total length was calculated between each pair of sets of clusters, which is the sum of all paired clusters' lengths for two diseases Figure . This va-4. We have two reasons for selecting this value. (1) SNPs with P-values less than this value are at least not insignificant. This P-value is the default threshold value used by the WTCCC for distinguishing moderate and significant SNPs from insignificant ones [-4, the number of genes found for some diseases such as HT and T2D drops to 0, suggesting that the threshold values of lower magnitude than 10-4 are too stringent.When analyzing disease association at the SNP level, we in fact compared the distribution of SNP clusters between different diseases. At the level of genes, our task was to convert the SNP clusters to genes. Naturally, the first question was what kind of SNP clusters should be used. The confidence level of a SNP cluster was taken into account in our experiment. We selected SNP clusters by a threshold of P-value. Specifically, all SNPs having a P-value less than this threshold, i.e. with stronger relation to an individual disease above a lower bound, should be included into the clusters to be further converted into genes. We did several tests on different thresholds varying in a large range and set it to 10ant ones . (2) UndFirst, from the seven sets of genes that are associated with the diseases, we translated the gene IDs into the ENSEMBL peptide IDs using the Biomart tool . Second,We used the human gene annotation in the UCSC's genome database for mapping SNP clusters to genes. This database contains adequate information about human genes and their locations along chromosomes. Before starting the conversion, the algorithm still remains unfixed for one issue. The straightforward method should work like the follows: if one SNP cluster shares a part of its region along the chromosome with a gene (or genes), the gene (genes) should be considered related to the cluster and added to the converted gene list. However this may not be a sufficiently sound criterion. In our experiment, we believe that we should take into account the regulatory effect of an SNP cluster from the upstream of a gene. This means that a cluster located upstream of a gene but having no shared regions sometimes can have a major effect on the gene, for example as a gene expression enhancer. In this case, when converting a cluster of SNPs to genes, not only genes having overlapping regions with the SNP cluster, but also genes located downstream of the cluster with no overlap, should be included into the converted gene list.We calculated the distances of all the SNP clusters to their nearest genes along chromosomes. Figure Since sometimes the regulatory region of a gene can be distantly located in the upstream region of the actual gene, it is hard to decide what distances should be used to convert SNP clusters into genes. We therefore used two extreme values for the distance allowed for a SNP block away from a gene: 0 and the maximum distance, to see the effect of distance on the conversion of SNP clusters to genes.SNPs in the regulatory regions of genes can have a major effect of the patterns of gene expression. In order to examine whether there are SNPs falling in regulatory regions of genes, we obtained more than 100,000 computationally identified transcriptional regulatory modules within the human genome from the PReMod database and wrote a C++ program to identify the SNPs that are within the transcriptional regulatory modules. The following describes the simple algorithm:if Extracting 2 KB upstream regions of genes. then   if there are SNPs in the regions then      if any TFBSs are inside the regions then                     Print result.end if         else                  "No result"end if      end if   The authors declare that they have no competing interests.WH, PW, ZL designed the study, analyzed the data, and wrote the paper, LZ designed the study and wrote the paper.


With the conditional approach, SNPs that reach Bonferroni-adjusted significance at the first stage are tested in pairwise combinations with all SNPs in the data set. We compared the performance of those strategies by using Replicate 1 of the simulated data set of the Genetic Analysis Workshop 15 Problem 3. Most interactions detected resulted from SNP pairs within 1000 kb of each other. The remaining were false positives involving SNPs with excessively strong marginal signals. Our results highlight the need to account for locus proximity in the evaluation of interaction effects and emphasize the importance of marginal signal strength in logistic regression-based interaction modeling. We found that modeling additive genetic effects alone was sufficient to capture underlying dominance interaction effects in the data.Large genetic association studies based on hundreds of thousands of single-nucleotide polymorphisms (SNPs) are a popular option for the study of complex diseases. The evaluation of gene × gene interactions in such studies is a sensible method of capturing important genetic effects. The number of tests required to consider all pairs of SNPs, however, can lead to a computational burden, and efficient strategies to reduce the number of tests performed are desirable. In this study, we compare two-stage strategies for pairwise SNP interactions testing. Those approaches rely on the selection of SNPs based on the single-locus test results obtained at the first stage. In the  Genetic association is an increasingly popular method to identify genetic determinants of common diseases. Traditional single-locus association tests evaluate the marginal effects of each marker. It is to be expected, however, that the genetic susceptibility of complex traits would result from the interplay of several factors, including gene × gene interactions. As such, analytical approaches that consider single-nucleotide polymorphism (SNP) interaction effects have the potential to provide more power, especially when susceptibility genes have small or undetectable marginal effects.n) of SNPs in a study, following n(n - 1)/2, and testing for the entire probability space for SNP × SNP interaction can become computationally unfeasible or cumbersome. Some promising two-stage approaches have been proposed , and the p-value of the interaction coefficient iaa was used in the additive-only model [Eq. (4)]. Bonferroni correction was used by considering the total number of valid interaction term tests. Valid interaction tests refer to those for which the problem of quasi-separation does not occur when using the logistic regression model. We also used an interaction model in which the additive effects are considered without covariates (results not shown), in this case, the p-values of the one interaction term (iaa) was used.SNPs were selected in the first stage for marginal significance levels up to 0.1 and 0.05 for the simultaneous design and up to the Bonferroni adjusted threshold for the conditional method. In the second stage, the p = 0.1, and 930 SNPs below the threshold of p = 0.05. The tests for all possible pairwise combinations involved (1361 × 1360)/2 = 925,480 interaction tests for the threshold of 0.1 and (930 × 929)/2 = 431,985 tests for the 0.05 threshold. For the conditional design, Bonferroni correction left 443 significant SNPs: 428 of which are located on chromosome 11, 14 SNPs on chromosome 6, and 1 SNP on chromosome 18. These results correspond to the detectable marginal SNP effects simulated in the data set ], we obtained 1361 SNPs that fell below the threshold of ], we obtp = 0.1, and 894 SNPs below the threshold of p = 0.05. Using the global Bonferroni correction, 398 SNPs were found significant, of which, 395 were on chromosome 11 and 3 SNPs were on chromosome 6.For the logistic model with adjustment for sex, smoking, and DR alleles [Eq. (2)], we considered only the additive effects. 1319 SNPs fell below the threshold of idd) (Table iaa). The same model detected 57 interactions with the iaa term, and the logistic model for additive effects-only (not shown) detected 7 more interactions, due to the reduction in the multiple testing correction. Based on this result, we proceeded to conduct the analysis with adjustment for covariates by using only the additive interaction model [Eq. (4)]. The use of either thresholds at p = 0.05 or p = 0.1 for the simultaneous design provided exactly the same interaction results for the full model without covariates [Eq. (3)], but a few more were found with the additive model with covariates [Eq. (4)] when using the threshold of p = 0.1.When using the full model with dominance and additive genetic terms [Eq. (3)] with the simultaneous design, five significant interactions were found by the dominance interaction term (d) Table , but allp-values < 10-200), which we believe to be responsible for generating false-positive interaction signals ] reveals that 129 different SNP × SNP interactions were found with either the conditional or simultaneous design. After eliminating the 52 interaction results between SNP < 1000 kb, 77 interactions remained, 75 of which include a chromosome 11 SNP around Locus F, 6 of those are proximity interactions on chromosome 11, and 69 are false positive between chromosome 11 and 1. This chromosome 11 region had excessively high marginal signals . According to the simulation schema of Problem 3, a simulated genetic interaction effect between Locus A on chromosome 16 and Locus C on chromosome 6 was modulated by the DR alleles. In Stage 1, we were able to detect the simulated Loci C on chromosome 6, D on chromosome 6, and E on chromosome 18, which passed Bonferroni correction in both logistic regression models with adjustment for covariates and without adjustment for covariates ] and a p-value of 0.0001 with the additive model without adjusting for covariates, which was insufficient to be detected after multiple testing adjustment. We mention here that we used only the first replicate of GAW15 Problem 3 simulated data due to computational constraints, but the analysis of more replicates may have provided different results.The simultaneous method optimizes the search for interaction effects between SNPs that provide some indication of marginal associations. This situation is most appropriate for disease models with additive polygenic effects. The advantage of the conditional design, on the other hand, is that it can capture interactions with loci that have no marginal effects, and as such is expected to be valid over a wider range of disease models. With our data, neither approach was able to capture the real underlying interaction between Loci A and C. Posterior testing for interaction between these loci revealed a An important advantage of the conditional design in a real data set, is that Step 1 should provide only a small number of globally significant SNPs , which are then tested in pairwise combination with the full complement of SNPs in the study. As such, the conditional design should generate fewer interaction tests than the simultaneous design. In the GAW15 simulated data set, however, over 400 SNPs remained significant after adjustment for multiple testing in Stage 1 and caused a special situation in which the conditional design exploded into many interaction tests and lead to the physical proximity interaction effects that we detected with both approaches. It is not clear why there were so many significant results on chromosome 11 and it would be interesting to validate those results by testing the other replicates. The use of logistic regression for the detection of interactions has the disadvantage of performing poorly with high dimensionality, which can lead to false positives and decreased power. In the present study for instance, the conditional approach with additive and dominance effects required the estimation of 16,279,364 parameters from 32,154,500 observations. Another potential problem with logistic regression is the phenomenon of the separation or quasi-separation of data. This occurs in the fitting process of the logistic model when the likelihood converges while at least one parameter estimate diverges to infinity. Quasi-complete separation of data, however, was only observed in 0.3% of the tests that we performed with the additive model with covariates in this study. High correlations between predictors, referred to as multicollinearity, may also decrease the power of the logistic regression. This may occur with SNPs that are in high LD. Multicollinearity due to linkage disequilibrium could, however, be addressed statistically by combining SNPs that are in high LD into haplotypes.iaa). We conclude from our results that using a model with only the additive genetic effects of two SNPs should capture most of the underlying interactions and will reduce the multiple testing otherwise imposed by the four interaction terms. A large portion of the interactions that we detected resulted from SNP pairs that were within 1000 kb of each other. Our results clearly highlight the need to include an additional step or procedure to account for locus proximity in the evaluation of interaction effects. All remaining interactions without exception were false positives that included a SNP with exceptionally high marginal signals from chromosome 11 or chromosome 6  are likely to be responsible for the false-positive signals. This finding draws attention to the importance of directing further work toward the evaluation of the impact of strong marginal SNPs in interaction modeling.The significant interactions found by the full logistic regression model with both dominance and additive effects [Eq. (3)] were all captured by the additive effect coefficient (The author(s) declare that they have no competing interests.


The method was applied to data on 116 SNPs and 189 genes on chromosome 11, for which Morley et al.  had previously reported linkage. We were able to confirm the association of the expression of HSD17B12 with a SNP in the same region reported by Morley et al., and also detected a SNP that appeared to affect the expression of many genes on this chromosome. The approach appears to be a promising way to address the huge multiple comparisons problem for relating genome-wide genotype × expression data.We describe a hierarchical Bayes model for the influence of constitutional genotypes from a linkage scan on the expression of a large number of genes. The model comprises linear regression models for the means in relation to genotypes and for the covariances between pairs of related individuals in relation to their identity-by-descent estimates. The matrices of regression coefficients for all possible pairs of single-nucleotide polymorphisms (SNPs) by all possible expressed genes are in turn modeled as a mixture of null values and a normal distribution of non-null values, with probabilities and means given by a third-level model of SNP and trait random effects and a spatial regression on the distance between the SNP and the expressed gene. The latter provides a way of testing for  Recent advances in genomic technology now allow genotyping of hundreds of thousands of single-nucleotide polymorphisms (SNPs) and measurement of the expression of tens of thousands of genes on single microarrays or chips at a manageable cost. Extensive literature on the analysis of gene expression data has evolved over the last five years, and since the advent of ultra-high-volume genotyping platforms, genome-wide association and linkage scans using SNPs have also become feasible. The multiple comparisons problem is central to the analysis of either type of high-volume data. In 2001, Jansen and Nap , , , etc. The updates of the X values are based on a Metropolis-Hastings procedure with a random walk proposal. The sequence was started ten times from several initial points chosen from an overdispersed prior around rough estimates. Half of the initial samples are discarded and the second half is kept. The number of kept samples, L = 4000, is chosen to be large enough so that for all parameters of interest the variance between sequences VB is comparable to that within sequence VW, R < 1.10:We fitted the model using a Markov-chain Monte Carlo (MCMC) approach, implemented in Matlab. Updates of all parameters except the location parameters The rationale behind this convergence monitoring procedure is described and justified by Gelman et al. .cis and trans linkages at this chromosome. The final data set thus had 116 SNPs and 189 expressed genes. IBD status was estimated from the complete two-generation pedigrees (excluding grandparents) by a program written by JM based on the Lander-Green algorithm  results are summarized in the right panel of Figure ] resultsWe have introduced a novel hierarchical Bayes model for genetic control of gene expression. Our approach to dealing with the multiple comparisons problem is to represent the matrices of all possible SNP × expressed gene association or linkage coefficients in terms of row and column random effects, along with a spatial regression on the distance between the two. Although this allows inference on specific pairs, we have greater interest in the variances of the row and column effects, which reflect systematic tendencies for SNPs to affect variable numbers of phenotypes and for phenotypes to be differentially expressed. Our mixture model also supports the possibility that the vast majority of such associations or linkages would be truly null, and allows separate estimation of both the probability and magnitude of non-null tests. So far we have not imposed any relationship between the parameters of the association (means) and linkage (covariance) models, but one might contemplate using the broad regions where linkage is seen for a particular phenotype as a prior for testing single-SNP associations with that phenotype.The strongest gene-expression × SNP association reported by Cheung et al.  on chromm, n, sample size, and number of MCMC samples. Generating 4000 MCMC samples required 6 hours on a 2.2 GHz single-processor machine. However, one outstanding methodological challenge that would have to be addressed before the approach could be applied to dense SNP associations would be how to deal with the multicollinearity problem; for this reason, we chose to restrict this analysis to only a subset of SNPs that were not in strong LD with each other.We chose to restrict these analyses to a subset of genes and SNPs on a single chromosome to test the feasibility of the method. In principle the approach could be applied on a genome-wide scale, since the computation time increases linearly with The author(s) declare that they have no competing interests.


With the advancement of genotyping technologies, whole genome and high-density SNP markers have been widely used for genotyping of mapping populations and for characterization of germplasm lines in many crops. Before conducting SNP data analysis, it is necessary to check the individuals to ensure the integrity of lines for further data analysis.5-derived soybean population of ‘5601T’ x PI 157440 that was genotyped with 1,536 SNPs to illustrate the procedure and its application.We have developed an R package to conduct a parent-offspring test of individuals which are genotyped with a fixed set of SNP markers for further genetic studies. The program uses monomorphic SNP loci between parents and their progeny genotypes to calculate the similarity between each offspring and their parents. Based on the similarity of parents and individual offspring, the users can determine the threshold level for the individuals to be included for further data analysis. We used an FParentOffspring’ coupled with the available SNP genotyping platforms could be used to detect the possible variants in a specific cross, as well as the potential errors in sample handling and genotyping processes. It can be used in any crop which is genotyped with a fixed set of SNP markers.The R package ‘ Single nucleotide polymorphism (SNP) genotyping platforms including Invader® assay, single base extension (SBE), oligonucleotide ligation assay (OLA) SNPlex™ system, and the Illumina GoldenGate™ and Infinium™ assays  have beeWith such advanced genotyping technologies, massive amount of SNP data including both polymorphic and monomorphic loci have been generated. Typically, the monomorphic loci are excluded from the data set before further analyses. For example, genotyping a bi-parental population in soybean using an USLP 1.0 panel of 1,536 SNP loci will result in around 1,000 monomorphic loci, which could be used to test if all progeny are truly derived from same parents. Here we developed an R package to calculate the similarity between each offspring and its parents using monomorphic SNP loci. This program can be used by researchers to quality control the progeny and determine which offspring needs to be excluded from further analysis.To calculate the similarity of each offspring to its parents utilizing the parental monomorphic loci at each monomorphic SNP locus, allele calls in each offspring are assigned with numbers 0, 1, and 2 by comparing the parental genotype. A score of “2” is assigned when an offspring has same alleles to the both parents; a score of “1” indicates that the offspring possesses one parental allele and one non-parental allele; and a score of “0” indicates that the offspring possesses different alleles than their parents. An example of the assignments is presented in Table The similarity between an offspring and its parents is calculated based on the all monomorphic loci as follow:http://cran.r-project.org/web/packages/ParentOffspring/index.html). The output files of this program include both monomorphic and polymorphic SNP marker data set for further analyses.Where S = the similarity between an offspring and its parents, a = the number of markers with a score of 2, and b = the number of markers with a score of 1 and c = the number of markers with a score of 0. The R package can be found in following link  under field conditions of UGA Plant Sciences Farm during 2010 in a randomized complete block design with two replications. Based on leaf severity symptoms, the lines were visually rated for bacterial pustule reaction on a plot basis with a scale of 1 to 5, where plots with no symptoms were rated as 1 (Resistant) and plots with severe symptoms as 5 (Susceptible). Associations of the SNP markers with the bacterial pustule rates were tested using single-factor analysis in SAS 9.3 [An Fennessee  and PI 1ennessee . The F1 t method . The F5 Of 1,536 SNP markers in 5601T × PI 157440 population, 542 SNPs (37%) were polymorphic and 938 SNPs monomorphic which accounted for 63% of the total SNP markers. These monomorphic SNPs were distributed eventually on all chromosomes : Windows, Mac OS, LinuxProgramming language: ROther requirements: R version 2.15.1 or higherLicense: GPL-2 | GPL-3Any restrictions to use by non-academics: Non.QTL: Quantitative trait loci; SNP: Single nucleotide polymorphism.The authors declare that they have no competing interests.HA generated the experimental data, ran the R package and wrote the MS. HA, PJ and ZL developed the procedure, implemented the R package, and evaluated the program. HRB generated the mapping population. All authors participated in the design of the study, edited and approved the final manuscript.


CSN1S1, CSN2 and CSN1S2 genes and another one consisting of the CSN3 gene. Highly significant associations with both protein and milk yield were found for both single SNPs and haplotypes within the CSN1S1-CSN2-CSN1S2 haplotype block. In contrast, no significant association was found for single SNPs or haplotypes within the CSN3 block. Our results point towards CSN2 and CSN1S2 as the most likely loci harbouring the underlying causative DNA variation. In our study, the most significant results were found for the SNP CSN2_67 with the C allele consistently associated with both higher protein and milk yields. CSN2_67 calls a C to an A substitution at codon 67 in β-casein gene resulting in histidine replacing proline in the amino acid sequence. This polymorphism determines the protein variants A1/B  versus A2/A3 . Other studies have suggested that a high consumption of A1/B milk may affect human health by increasing the risk of diabetes and heart diseases. Altogether these results argue for an increase in the frequency of the CSN2_67 C allele or haplotypes containing this allele in the Norwegian Red cattle population by selective breeding.A high resolution SNP map was constructed for the bovine casein region to identify haplotype structures and study associations with milk traits in Norwegian Red cattle. Our analyses suggest separation of the casein cluster into two haplotype blocks, one consisting of the  Two distinct regions on this chromosome affect milk traits . One QTL affecting protein and fat percentage has been positioned in a narrow region of 420 kb  were available from the national genetic evaluation carried out by GENO Breeding and AI Association, and evaluated using a BLUP animal model .CSN1S1, CSN2, CSN1S2 and CSN3), nine SNPs were available from [For the initial QTL scan, we used a map consisting of 399 SNPs covering the entire BTA6 . To fineble from , whereasble from . Physicable from . The aveiG)) was calculated as well as a model fitting only background genes (LogL(0)) using the ASREML package [A combined linkage and linkage disequilibrium (LDLA) method  was used package . Our tes package . Accordi package . Then, tDYDi = μ + si + xib + ai + ei where DYDi is performance of son i, μ is the overall mean, si is a fixed effect of sire of son i, xi is 0 if son i is homozygous 1 1 (e.g. AA); 1 if son i is heterozygous 1 2 (e.g. AT or TA); or 2 if son i is homozygous 2 2 (e.g. TT), b is the effect of the SNP, ai is a polygenic effect of son i, and ei is a residual effect. For each single marker, the log-likelihood of a model containing the SNP effect (LogL(H1)) was calculated as well as a model without this SNP effect (LogL(H0)) using the ASREML package [DYDs of the sons were used as performance information in the analyses. The model fitted to the performance information for each trait and each SNP was:  package . Our tesAdditionally, multiple SNP association tests were carried out for the most significant markers from the single SNP association test. The tests were implemented by fitting a fixed effect of the SNP in the above-mentioned model and repeating the analyses for the most significant SNPs in turn. Test statistics for the analyses were as described above.2) between markers.An analysis package, CRIHAP, was developed for determining haplotypic phases and imputing missing genotypes for all individuals . The programs are based on both linkage and linkage disequilibrium information generated by the CRI-MAP 2.4  and PHASCSN1S1, CSN2 and CSN1S2 for which we found highly significant brackets or single SNPs associated with protein yield. A script was made to deduce maternal and paternal haplotypes for all individuals and different haplotype blocks using haplotypic phases from the CRIHAP program package. As for the single SNP analyses, DYDs of the sons were used as performance information in the analyses. The model fitted to the DYDs, for each trait and each haplotype, was DYDi = μ + si + xib + ai + ei where DYDi is the performance of son i, μ is the overall mean, si is a fixed effect of sire of son i, xi is a row-vector indicating which haplotypes and how many copies are carried by the son; and b is a column indicating the random effects of the haplotypes; ai is a random polygenic effect of son i, and ei is a residual effect. The test statistic (LogL difference) was found as previously described for the single SNP association test. Phenotypic standard deviations for protein and milk yield were 36.75 kg and 1137.79 kg, respectively. These deviations were used to scale the haplotype effects into phenotypic standard deviations for each of the traits for a standardised presentation.Haplotype blocks were constructed for the casein loci et al. [http://cilit.umb.no/maps/. The analysis reveals highly significant results  mainly in two different regions. Milk yield, protein yield and especially fat and protein percentages show highly significant results in the region between approximately 25 and 45 Mb. This QTL, previously fine-mapped in Norwegian Red cattle [ABCG2 gene [BTA6-02720 and CSN1S1-Prom_175 (LogL difference = 19.5), but several additional significant results appear for numerous marker brackets in CSN2 and CSN1S2. No significant result was found for marker brackets in the CSN3 gene. The interval between CSN1S1_192 and CSN1S1-BMC_17969 was the only one with significant LogL difference for protein percentage (LogL difference = 5.6).Results of the initial QTL scan for milk yield, protein yield, protein percentage, fat yield and fat percentage  are shown in Figure et al.  or http:CSN2 and CSN1S2 for both protein yield (PY) and milk yield (MY) , in addition to CSN1S2-BMC_17192 for MY (LogL difference = 15.8).Data was also analysed for association between single SNPs and DYDs for protein yield and milk yield. Highly significant results were found for a number of SNPs in CSN2-BMC_9215 and CSN2_67. These two SNPs are in complete LD with each other and both removed almost all peaks for other markers in the region. The result for CSN_67 is presented in Figure CSN3 gene and DYDs for PY.In most cases when fitting an effect of the most significant SNPs in a multiple SNP association test it highly reduced LogL differences for the other SNPs in the region. The most striking results were found for SNPs CSN1S1, seven haplotypes for CSN2 and six haplotypes for CSN1S2  and four haplotypes that tend to be positive  for CSN2   Figure . In contCSN2 and CSN1S2   [s1, β and αs2) have originated from a common ancestral gene via intergenic and intragenic duplications [Our analysis of a dense SNP map in the casein region using the LDLA methodology revealed a high number of significant marker brackets for protein yield especially in ) Figure . The twoe et al.  have reps et al.  have alsand αs2) . The calications  and sharications , whereasications . This evCSN3 region, whereas a large number of significant associations were detected between SNPs within CSN2 and CSN1S2, and protein and milk yields. The most significant results were found for CSN2_67, CSN2-BMC_9215 and CSN1S2-BMC_17192. When fitting CSN2_67 as fixed effect in a multiple SNP association test it removed almost all peaks for other markers in the region , a peptide with opioid-like effect, which may play an unclear role in the development of some human diseases . It hasiew, see , ischaemiew, see , sudden iew, see , the aggiew, see ), and maiew, see ,40 in huCSN1S1, CSN2 and CSN1S2 genes and investigate associations between haplotypes and DYDs for protein yield and milk yield. Analysis for CSN2 reveals two haplotypes (2 and 5) that associate with low protein yield values whereas four haplotypes  seem to be associated with higher PY levels  whereas three haplotypes  tend to be associated with a lower protein yield . The main reason is probably that CSN2 haplotypes 1 (positive for protein yield) and 2 (negative for protein yield) combine into one frequent haplotype in CSN1S1.For the d Figure . CSN1S2 CSN1S1-CSN2-CSN1S2, we detected four haplotypes that associate with reduced milk and protein production . Interestingly, all of these haplotypes contain the A-allele of CSN2_67 (the A1/B variant), in addition to the G-allele of CSN2-BMC_9215 , marker allele frequencies, predicted physical distances between markers, and surrounding sequence of each SNP.Click here for fileCSN1S1-CSN2-CSN1S2 region (marker 4 to 41), their haplotype number and frequencies in 1143 Norwegian Red bulls (sires and sons)Haplotypes covering the Click here for file


While this procedure does guarantee control of false positives, it seems counterintuitive to the purpose of phase I, which is to generate hypotheses based on promising candidates. Researchers have generally combined data from the discovery phase I and other phases and used ‘genome-wide thresholds’ based on assuming all SNPs are independent. Linkage disequilibrium (LD) makes it problematic to differentiate a real signal from highly correlated proxy signals. Most published GWAS do not examine SNP interactions due to: (a) the high computational complexity of computing p-values for the interaction terms, and (b) the typically low power to detect significant interactions. It is plausible that more information should be extracted if: (i) higher order interactions are fitted, (ii) highly selected cases and controls are used in phase I, (iii) large replication studies are used, especially if involving existing GWAS data, (iv) the non-independence of SNPs is taken into account using, e.g. BEAGLE CALL or haplotype analyses, (v) focus is on candidate gene pathways, and/or functional SNPs, and (vi) rarer and more SNPs, such as is available from the Illumina 5M SNP chip, are used. We will illustrate these ideas using data from a GWAS of early-onset breast cancers, enriched for those with a family history, and a GWAS using extremes sample of extremes for mammographic density. We will also discuss the design of a large international breast cancer GWAS using the Illumina 5M SNP chip, phase I cases enriched for family history, population-based phase II cases and controls, population-based family study of candidate SNPs, and GxG analyses using ‘massively parallel’ super computing.The first wave of cancer genome-wide association studies (GWAS) have revealed tens of independent loci marked by common variants of unknown or likely no functional significance that explain about 5-10% of familial risk for the particular disease. The approach taken to date has been conservative, and only a fraction of information has yet to be extracted from these expensive enterprises. For example, the Bonferroni procedure for selecting candidate phase II SNPs ignores many SNPs that happen to fail an extremely low