The volume of data available on genetic variations has increased considerably with the recent development of high-density, single-nucleotide polymorphism (SNP) arrays. Several software programs have been developed to assist researchers in the analysis of this huge amount of data, but few can rely upon a whole genome variability visualisation system that could help data interpretation.GenotypeColour™ as a rapid user-friendly tool able to upload, visualise and compare the huge amounts of data produced by Affymetrix Human Mapping GeneChips without losing the overall view of the data.We have developed GenotypeColour™ include visualising the entire genome variability in a single screenshot for one or more samples, the simultaneous display of the genotype and Copy Number state for thousands of SNPs, and the comparison of large amounts of samples by producing "consensus" images displaying regions of complete or partial identity. The software is also useful for genotype analysis of trios and to show regions of potential uniparental disomy (UPD). All information can then be exported in a tabular format for analysis with dedicated software. At present, the software can handle data from 10 K, 100 K, 250 K, 5.0 and 6.0 Affymetrix chips.Some features of  and is especially useful for the analysis of multiple samples.We have created a software that offers a new way of displaying and comparing SNP and CNV genomic data. The software is available free at  Data visualisation is important for many scientific disciplines, and this is particularly so in genetics, where the recent development of high-density, single-nucleotide polymorphism (SNP) arrays can provide information on thousands of SNPs and structural variants . AlthougThe CNAG  elaborat® Human Mapping chips, and integrate certain earlier software functions. The visualisation tool translates the genotype and CN information obtained by the Affymetrix GeneChip® Human Mapping chips into a colour code. The software allows the user to look at whole-genome variability by visualising the genotype and normalized quantitative data of one or more individuals for millions of SNPs in different colours in a single window. The entire genome SNPs can be displayed either together or separately for each chromosome.Our goal was to develop a user-friendly visualisation tool that could rapidly upload, visualise and compare the huge amounts of data produced by the Affymetrix GeneChipGenotypeColour™ analyses the data produced by all of the commercially available Affymetrix GeneChip® Human Mapping arrays, including the most recent 6.0 chips.GenotypeColour™ is written in Visual Basic and uses the Access database to store information. If the Microsoft Access application is not installed on a computer, the program can run after the installation of Microsoft Jet 4.0 Database Engine and Microsoft Data Access Components (MDAC) 2.8 SP1, which are free to download from the Microsoft web site [GenotypeColour™runs only under Windows with Framework.net 1.0 and has been tested on the Windows 2000, Windows XP and Vista operating systems. The minimal hardware requirements for analysis of the 250 K arrays (and inferior) are 250 MB of RAM, and a CPU Pentium 4 1.6 GHz, whereas at least 1 GB of RAM is recommended for the analysis of 5.0 and 6.0 chips.web site ,6. At prSoftware performance was tested on a notebook computer with CPU IntelCore2, 1.66 GHz and 1 GB of RAM. Uploading 250 K probes for 20 samples took 2 minutes for the genotype data and another 2 minutes for the CN state information. Visualisation of the data, however, took only 12 seconds.The upload and visualisation of 6.0 data took slightly longer than it did for the 250 K probes, requiring almost 3 minutes for one sample and for CN state uploading, whereas visualisation of the data took approximately 1.5 minutes. However, since the longest time required was to upload the relative information of the probes' positions, the upload of the other samples was much faster. As an example, uploading the genotype data of the other 5 samples took only 3 minutes. Once the data were uploaded, opening the files took only a few seconds.GenotypeColour™ does not elaborate the raw data of the Affymetrix Human mapping chip; instead, it imports the data analysed by the Affymetrix consoles and displays them in a different way. The algorithm needs two input files, one containing genotype information and the other the CN state information. Both files are obtained by Affymetrix GTYPE or Genotyping Console Software programs. GenotypeColour™ is programmed to display the genomic variation in four different ways, as described below.The algorithm reads the genotype SNP calls exported by Affymetrix GTYPE or GenotypeConsole software and assigns a square (down to a single pixel) to each SNP on the computer screen. All SNPs are displayed one after the other horizontally, from the top to the bottom of the page in a left-to-right pattern, starting from the first SNP on chromosome 1 and ending with the last SNP on chromosome X. For the 6.0 data, SNPs on chromosome Y and on mtDNA are displayed after those on chromosome X.The number of SNPs in each line can vary from 1 to a number depending on the software window and the computer screen. It is possible to change the way the data are visualised to generate the most informative image by changing the window size. Each square is displayed with a different customised colour according to the genotype of the SNP shown . In this way, the whole genotype variability of one subject can be visualised on the computer screen at the same time  consecutive SNPs with/without a particular genotype. This list can be exported in an Excel format.The use of customized colours to visualise data is very useful for the detection of particular regions. For example, in Figure The SNP allele frequencies of European, Asian and African populations as reported by the Affymetrix database are memorised in the software for all chips, excluding 6.0. After selecting a population for each subject, the software assigns a colour to each homozygous genotype according to whether it is the most frequent or the rarest homozygote in that population. The percentage of rare homozygotes is also displayed, and this may help the classification of samples.After importing the CN state of each probe from the Affymetrix CNAT4 viewer or Genotyping Console, the information can be displayed immediately with the genotype information. Whereas the genotype information is given by the colour of the square, the CN state  is displayed by changing the height of the squares, thus visualising genotype and CN state simultaneously Figure . ObviousThis option may be useful to test the accuracy of the data. For example, the visualisation of heterozygous SNPs in regions with a CN state equal to 1 could suggest genotype error, CN miscalculation, hybridization of probes to segmental duplicated regions or mosaicism. To visualise CN alterations (CNAs) and the smaller CN variations (CNVs) dispersed within the genome better, it is possible to select the "show genomic variation only" option, which allows the visualisation of regions with CN states other than 2, by masking all other information Figure .In this visualisation, the software allows the user to create a list of chromosomal regions with a CN state other than 2 Figure . The CNVGenotypeColour™ is its sample genotype comparison function. Presently, the software allows the genotype or the CN state of many individuals for thousands of SNPs to be depicted, and it allows comparisons to be shown.The other important feature of When comparing two or more samples, for each SNP the algorithm evaluates whether the samples share the same genotype, only one allele or no allele, and assigns a colour to the corresponding square on the screen. This function allows fast and simple detection of chromosomal regions of complete or partial identity among samples, and allows identification of the origin of chromosomal alteration in parent-child comparison. A list of these regions can be exported in Excel format. Graphical visualisation is combined with information on the percentage of SNPs sharing identical genotypes, only one allele or no allele.One implementation of genotype comparison is trio comparison. When this option is selected, the software displays SNPs with Mendelian segregation and with Mendelian inconsistencies in two different colours.GenotypeColour™ allows the user to compare the CN state of two or more samples. In this case, the algorithm compares the CN state of each SNP in all samples analysed and assigns each SNP a colour depending on the outcome of the comparison. For each SNP, there are six possible outcomes: a diploid state in all samples; deleted or duplicated in all samples; deleted in some samples; duplicated in some samples; and deleted and duplicated in some samples . Both parents were analysed to verify the origin of possible alterations. For the first analysis, both genotype calls were imported from the Affymetrix GTYPE software and the CN state from the Affymetrix CNATviewer. In the proband, the genotype visualisation showed a region of LOH on the small arm of chromosome 8.Figures After uploading the CN state, it became clear that the LOH was due to a deletion that was not present in the parents Figure . The genIn the second example, simultaneous comparisons of different members of the same family were made by assigning three different customised colours to the three possible outcomes . As shown in Figure However, if the coloured pixels corresponding to the "AA-BB comparison" are concentrated in a particular chromosomal region, this indicates the presence of potential uniparental disomy (UPD) or CNV in one of the two samples, as in the case of Figure In addition, the boundaries between these regions correspond to recombination breakpoints. In comparisons between grandchild-grandparent, nephew/niece-uncle or cousins, the dichromatic region intercalated between regions with all three colours identify shared inherited haplotypes. The frequency and extension of these dichromatic regions decrease as a function of the degree of kinship up to at least the third generation.GenotypeColour™ genotype comparison option can be used to detect kinship and shared haplotypes without calculations or elaborations, using only this new graphical analysis of genotype data. This tool is useful also to identify the origin of the chromosomal alterations in parent-child comparisons and for many other applications, such as linkage and association studies, and population evolution.These examples highlight how the GenotypeColour™. Comparison of Figures In the third example, 30 tumour specimens that belong to the DNA collection of 371 primary lung adenocarcinoma analysed by Weir et al.  were comEven if the analysis was done on only 30 out of the 371 samples of the original research , most ofGenotypeColour™ agree with those shown by Weir et al., [The chromosomal distribution of large-scale deletions and amplifications shown by  et al.,  in FigurThe visualisation of the genotype and CN state of all 30 samples allows better definition of breakpoint regions. There is an example in Figure GenotypeColour™ software represents a new and useful tool for the visualisation of genome variability. Indeed, beside its distinctive features, it condenses in a unique program many of the characteristics of other available software. The main properties of GenotypeColour™ are described below.1. Visualisation of the genotypes of thousands of SNPs in a single computer screenshot. dChip has a similar function; however, the zoom resolution that allows the visualisation of all chromosomes does not allow the visualisation of all SNP genotypes. SNPscan, even if it can discriminate between homozygous and heterozygous genotypes, does not allow visualising and discrimination of all SNPs, since many overlap. Programs such as CNAG and CNAT do not display the genotype of each SNP, even if they do highlight LOH regions.x-axis and the CN state and the LOH state on the y-axis. The homozygous/heterozygous state of the probe is inferred by the colour. GenotypeColour™, however, in its maximum zoom-out visualisation, assigns a distinctive pixel to each SNP and depicts them next to one another. This kind of plotting has the advantage of avoiding spatial overlap of probes and improves graphical visualisation. This allows the detection of very small LOH or CNV regions, which can be visualised even better by the "show genomic variation only" option.2. The simultaneous visualisation of genotype and CN state. To our knowledge, the only software allowing simultaneous visualisation of genotype and CN state is SNPscan. In SNPscan, each probe is plotted on a 2D graph with the 23 chromosomes on the 3. Sample comparisons: with our software, a "consensus" image is displayed in which a different colour is assigned to each SNP according to the genotype concordance of the samples compared. From these consensus images, regions of identity/diversity among samples are identified immediately. Moreover, the same procedure can be applied to the CN state and to trio comparisons. The only program that can make paired and unpaired comparisons of genotypes between more than two samples is dChip; however, the algorithm used for the comparison is different from ours and at present it does not perform the CN state or trio comparisons.GenotypeColour™ allows us to export in a table the information of many samples simultaneously (columns) × LOH/CNV regions (rows). This format improves the identification of chromosomal features common to more than one subject, and allows handling of information on CNV and LOH for association studies.4. The export of CNV and LOH information in tabular format. 5. The rapid comparison between control and pathologic tissues to identify CNA in different chromosomal regions. This seems to be especially useful in tumours.6. The visualisation of "no calls" in a different colour can give clear information on the quality of the procedure and on the random distribution and/or clustering of uninformative SNPs.7. Visualisation of population frequencies. This option could be used as a guide to the ancestry of the analysed samples.GenotypeColour™ is to provide a user-friendly tool that can rapidly upload, visualise and compare the huge amounts of data produced by the Affymetrix Human Mapping GeneChips without losing the overall view of the data. Indeed, a general view of results can make biological data easier to understand, followed by analysis with more specialised bioinformatic tools. Only its extensive use by other research groups for specific biological problems will give a more exact view of the potential applications of this new way of visualising DNA genomic data.In conclusion, we have created a program that does not require particular bio-computer notions and is easy to use thanks to its self-explanatory interface and detailed instruction manual. The purpose of Project name: GenotypeColour™Project home page: Operating system(s): Windows XP, Windows 2000, Windows Vista.Programming language: Visual BasicOther requirements: MDAC 2.6 and Microsoft Jet 4.0 Database Engine or Microsoft Access databaseLicense: free non-commercial research use licenseAny restrictions to use by non-academics: Commercial use license can be obtained by contacting the authors.SB conceived the software, carried out its design and planned and supervised the draft of the manuscript. CM participated in the planning and drafted the manuscript with contribution in the software development. SC developed the algorithm following the instructions of SB and CM. All authors have read and approved the final manuscript.


Canine hip dysplasia (HD) is a common polygenic trait characterized by hip malformation that results in osteoarthritis (OA). The condition in dogs is very similar to developmental dysplasia of the human hip which also leads to OA.A total of 721 dogs, including both an association and linkage population, were genotyped. The association population included 8 pure breeds . The linkage population included Labrador retrievers, Greyhounds, and their crosses. Of these, 366 dogs were genotyped at ∼22,000 single nucleotide polymorphism (SNP) loci and a targeted screen across 8 chromosomes with ∼3,300 SNPs was performed on 551 dogs (196 dogs were common to both sets). A mixed linear model approach was used to perform an association study on this combined association and linkage population. The study identified 4 susceptibility SNPs associated with HD and 2 SNPs associated with hip OA.PTPRD, PARD3B, and COL15A1) reported to be associated with, or expressed in, OA in humans. This suggested that the canine model could provide a unique opportunity to identify genes underlying natural HD and hip OA, which are common and debilitating conditions in both dogs and humans.The identified SNPs included those near known genes ( The high relevance of canine diseases to those in humans, and the intrinsic importance of dogs to humans as special companions with a shared environment, makes the dog an attractive species to discover genes underlying the homologous, relevant diseases in humans. An important shared condition is hip dysplasia (HD) and osteoarthritis (OA), both of which are remarkably similar in their clinical expression and pathogenesis in both man and dog Canine HD, a complex trait, is a major veterinary problem occurring with a frequency up to 75% in mixed and pure breed dogs IGF1 gene responsible for canine body sizeRFGF4 associated with the chondrodystrophoid phenotype Complex traits and diseases of dogs, many of which are models of homologous human conditions, are now exploitable for molecular genetic dissection COL15A1, PARD3B, and PTPRD that have also been linked, or associated, with human OA. Candidate genes were suggested for the other three SNPs.Quantitative trait loci (QTL) influencing canine HD have shown some convergence of QTL location across studies and breeds. The challenge remains to identify the mutations that collectively underlie HD and OA in order to understand the molecular pathogenesis of these diseases and develop marker and gene based tests and novel treatments. To improve the resolution of QTL mapping for HD and OA in dogs, we extended our linkage population  to include six additional breeds to reduce the extent of linkage disequilibrium (LD) between the mutation and SNPs in a genome-wide association study (GWAS). Among the 6 SNPs identified, three of them are adjacent to  and Figure S1). Canine HD was measured as the Norberg angle (NA) on all the breeds and crosses  and OA was measured only in Labrador retrievers, Greyhounds, and their cross breed offspring. The genotypes included 3,324 SNPs from targeted areas on 8 chromosomes  and 21,455 SNPs covering the whole genome .The GWAS on a linkage and association joint population identified 2 loci underlying hip OA and 4 loci underlying HD with plausible positional candidate genes (MC1R locus to be expressed MC1R gene causes yellow coat color in Labrador retrievers homozygous for the MC1R mutation. MC1R is located at 66.692398–66.693344 Mb on canine chromosome CFA05. When GWAS was performed on a binary trait defined as yellow versus the others (black or chocolate), SNP BICF23260560, 2 Kb away from MC1R, had the strongest association (P = 1×10−18). The TYRP1 gene, located at 36.344658–36.363203 Mb on CFA11, causes chocolate coat color in black dogs TYRP1 gene, showed significant association (P = 3×10−7).We tested the power of GWAS and the veracity of the genotype calls by mapping two known genes controlling Mendelian coat color. Among the genotyped dogs were 185 Labrador retrievers of which 101 were black, 78 were yellow, and 6 were chocolate. Black Labrador retrievers are fixed at the K locus enabling the genotype at the −6, and 5×10−5 for HD and hip OA, respectively.For the traits of primary interest, we identified two SNPs associated with hip OA and four SNPs associated with HD in dogs. The associations were determined at the probability threshold at which the declared SNPs could be identified with a distinguishably different distribution compared to the rest of the SNPs PARD3B which is associated with knee OA in humans NRP2 lies at 17.293709–17.405724 Mb and is nearer the associated SNP locus than PARD3B. There may be as yet unknown genes in this interval. We identified a locus strongly associated (P = 4.9×10−8) with hip OA at 48.092910 Mb on CFA17 (REG3A (47.930245–47.932143 Mb), 160 Kb from this locus, and which encodes a pancreatic secretory protein involved in cell proliferation or differentiation One of the associated SNPs, at base pair 17.299306 Mb on CFA37, is near on CFA17 Figure 1COL15A1 contributing to OA in human patients EVC, EVC2PTPRDMAGP1For canine HD, we discovered 4 SNPs; two of these SNPs were on CFA03 and CFA30 in which QTL for HD had been previously identified Many human and canine diseases are thought to have related causes, and localizing disease genes in dogs is likely to be substantially easier than in humans 1 dogs of Greyhound/Labrador retriever breedings were positioned genetically between their respective parental breeds. The backcross of the F1 to Labrador retriever was closer to Labrador retrievers and the backcross of F1 to Greyhound was genetically closer to Greyhound as expected  derived from all the SNP genotypes as covariates to capture false association due to the population structure. This sacrificed statistical power for any mutations with effects confounded with the population structure in this study. The population structure was clearly revealed by the first two PCs derived from each set of SNPs. All the dogs from within a pure breed were clustered together in the scatter plots of the first and second PCs. All the Fexpected Fig. S5.To eliminate the spurious associations due to cryptic relationships among individual dogs, we fitted a random effect for dog groups clustered by kinship among individual dogs Fig. S4). Both sets of SNPs were informative with respective to heterozygosity. The heterozygosities showed a bimodal distribution with two peaks toward 0 and 0.5, respectively. This trend is similar to practical The MAF of the SNPs from each set followed a uniform distribution  reported previously Hip OA is heritable in the Labrador retriever/Greyhound cross breeds Figure S5. The LD, on average, was less than 0.3 (R2) for a pair of SNPs 30 Kb and 10 Kb apart in Labrador retriever and across all breeds respectively . The mean and median marker interval for the genome-wide SNPs was 107 Kb and 70 Kb, respectively. The mean and median marker interval for the 3,324 customized SNPs was 150 Kb and 46 Kb, respectively . This density, in hindsight, especially for the Illumina Beadarray, was not sufficient to take full advantage of the genetic architecture in the whole genome. We expect that the current population would have more statistical power to reveal more SNPs associated with HD and hip OA if more dense markers were applied through new genotyping technology such as application of the version 2 canine Illumina Beadarray or genotyping by sequencing Sample size (dogs with both genotype and phenotype), sample selection from the available population, inclusion of different dogs in the linkage studies, GWAS and fine mapping (each of which only included a subset of common dogs), marker density and distribution, and marker informativeness each both enhanced and limited the statistical power of the current study. The majority of the customized SNPs were focused on the targeted regions. A few of them were spread to cover the remainder of the LOD score profile in our previous linkage studies. The power to detect association for hip OA in our report was a limiting factor as only 99 dogs were available for this analysis and only the Labrador retrievers and the crosses to Greyhounds were included for this phenotype. Within dog breeds on average, LD is extensive (0.5–1.0 Mb), whereas across breeds, the extent of LD is less than 10 Kb The statistical model in which we fitted SNP genotypes across all breeds had the advantage of identifying mutations segregating in all the breeds and in the same linkage phase to the associated SNPs. However, the statistical model had low statistical power for a QTN that was in a different linkage phase from Labrador retrievers which comprised ∼50% of the individuals. An alternative mapping approach would be to treat the SNPs as confounded within breed. This statistical approach may be necessary to discover private SNPs associated with causal genes for a particular breed.EVC and EVC2, near SNPs associated with HD on CFA03, cause Ellis–van Creveld syndrome  PTPRD (protein tyrosine phosphatase receptor type D gene), associated with HD on CFA11, encodes a member of the protein tyrosine phosphate (PTP) family. Protein tyrosine phosphatases (PTPs) are signaling molecules that regulate cell growth, differentiation, mitotic cycle, and oncogenic transformation. PTPRD belongs to the family of type IIa receptor-like protein tyrosine phosphatasesDespite the loss of statistical power in an effort to avoid false positive findings, six SNPs were identified with association. Among the positional candidate genes, NAD) and a gene at ∼57.6 Mb identified as bA113O24.1 which is similar to IGF receptor binding proteins, based on sequence similarity. This is a positional candidate gene because IGF1 is an anabolic growth factor for chondrocytes and IGF1 polymorphisms are responsible for size variation in Portuguese Water dogs and other breeds COL15A1, 1.58 Mb away from SNP BICF2S23432143 on CFA11,was reported as a new positional candidate gene in genome-wide expression profiling in human OA articular cartilageCOL15A1 encodes a protein which is prominently expressed in newly-formed blood vessels indicating a possible role in angiogenesis and neovascularization. Femoral caput vascularization must occur before mineralization can proceed in the secondary center of ossification On CFA11, SNP BICF2S23432143 at 57.517597 Mb is near nicotinamide adenine dinucleotide (MFAP1) which encodes the MFAP1 proteinMFAP1, a 57 kDa protein processed to 32 kDa, was immunolocalized to microfibrils in aorta, nuchal ligament, and zonule MFAP1 is located near FBN1 and mutations in both genes are causal for Marfan syndrome. We are especially interested in the microfibrillary composition of the fibrous hip joint capsule as hip laxity is an important contributor, or predisposing factor, to HD. A mutation in intron 30 of FBN2 on CFA11contributed to HD in dogs homozygous for the deletionFBN2 mRNA isolated from their hip capsule increased significantly consistent with developing OA. Those dogs with incipient hip OA had significantly increased FBN2 mRNA in their hip joint capsules compared to those dogs without OA. However, dogs homozygous for the FBN2 deletion haplotype had significantly less FBN2 mRNA in their femoral head articular cartilage. This gene expression analysis was confounded by the incipient hip OA in HD dogs but the current data warrant further exploration for its role in HD.An interesting positional candidate gene for HD near SNP BIC2P799261 on CFA30 is microfibril-associated protein 1  was originally identified as a pancreatitis associated protein (PAP) released by the acini during acute pancreatitis. It is a secreted C-type lectin, reported to be upregulated in primary hepatocellular carcinomas REG3A may be involved in cell recognition and adhesion and in the protection of cells from oxidative stress-induced apoptosisREG3A possesses anti-inflammatory properties and is a signaling molecule in the MAPK pathway. This association offers a potential mechanism by which chondrocytes might regulate their response to injury and thereby influence the progression of hip OA.SNP BICF2G630205523 on CFA17 was strongly associated with hip OA suggesting that the nearest gene PARD3B (par-3 partitioning defective 3 homolog B) gene In a GWAS for knee OA in women, SNP rs1207421, significantly associated with OA, mapped to HSA2q33(chr2:205976100) located in the FBN2 expression in hip tissues with incipient OA as a result of prior HD. Our data suggested that the rapidity of the articular response to injury seems to also be governed genetically and independently of the primary trait.The identification of functional mutations in these candidate genes associated with canine HD or hip OA further suggest that the dog is suited for comparative mapping of homologous complex traits and diseases in humans Figure S1 and Table S1). The linkage population consisted of two breeds (dysplastic Labrador retriever and non dysplastic Greyhound) and their crosses. The association population was comprised of eight breeds .A total of 721 dogs from the 2,716 dogs in the HD archive Figure S2 and S3. The NA was treated as a continuous trait in the statistical modeling. The worst hip (the minimum) between measurements on left and right from each dog was used in the statistical analyses.The pelves of these dogs were radiographed following physical maturity (i.e. older than 8 months of age) Figure 2Table S4). The coat color was recorded for the 185 Labrador retriever dogs. Cornell Institutional Animal Care and Use Committee approved all of the studies  and 2006-0187 (Hip Dysplasia and OA Genetics).The presence or absence of hip OA was determined on dysplastic Labrador retrievers and their crosses to unaffected Greyhounds. Hip OA was defined radiographically, as osteophytes on the acetabular rim and periphery of the femoral head and neck or at autopsy, as fibrillation and erosion of the articular cartilage of the femoral head and acetabulum, increased synovial fluid volume, and hypertrophy of the teres ligament and fibrous joint capsule http://www.broadinstitute.org/science/projects/mammals-models/dog/dog-snps-canfam-20). The custom SNPs were chosen to maximize the number of Labrador retriever origin SNPs and intragenic SNPs. The physical order of the custom SNPs selected were alternated by breed to span the QTL linkage interval defined by the LOD score profile . The QTL intervals for chromosome 05, 18, and 19, were linked to hip OA The 721 dogs were genotyped on two different SNP platforms. One was the Illumina CanineSNP20 Bead array containing ∼ 22,000 loci distributed across the entire canine genome. The other array was customized with ∼3,500 SNPs chosen from the regions in eight chromosomes  harboring QTLs for HD and some for hip OA identified in previous studies We selected one SNP every ∼45 Kb in the following locations on the canine genome: CFA03, between 55–75 Mb; CFA11, between 4–31 and 38–65 Mb; CFA 29, between 9–31 Mb; CFA30, between 13–39 Mb. Each of these chromosomes including CFA11 and 29 harbored at least 1 QTL contributing to canine HD. In aggregate, these regions comprise 122 Mb. From the Broad canine SNP data base, the most valid SNPs  were chosen. Where ever possible, we included SNPs derived from Labrador retrievers as Labrador retrievers or their crosses comprised the most common breeds among our genotypes dogs. We attempted to select at least one SNP within the boundaries of each gene in our target regions. Additionally, among the 902 genes in our target regions, we selected SNPs within 534 of the 902 genes in our target regions ; the remainder had no intragenic SNPs in the Broad database.In total, we selected 2,799 SNPs across the four chromosomes described above. By breed, these SNPs represent: 31% boxer, 26% poodle, 5% Labrador retriever (enriched from 1% in the Broad database), 4% German shepherd, and the remainder miscellaneous breeds .To fine map the hip OA locus, we selected 592 SNPs on 4 chromosomes at 4 loci: CFA02 between 10–60 Mb, CFA05 between 40–70 Mb, CFA18 between 3–30 Mb, and CFA19 between 3–15 Mb. These correspond to regions where our microsatellite screen showed association with hip OA. On average, we selected 1 SNP every 181 Kb. These SNPs arose from 13% Labradors, 11% Boxers, 8% Malamutes, 8% Portuguese water dogs, and 60% other breeds. Of the 861 genes in these regions, we had SNPs within 377 genes or 44% of them. The SNPs were selected from the Broad Harvard/MIT web site. The customized genotyping used Molecular Inversion Probe Technology S3). DNA was extracted from whole blood collected in Na-EDTA using either phenol-chloroform or the Puregene (Gentra) protocol http://genome.ucsc.edu/cgi-bin/hgGateway).There were 366 and 551 dogs genotyped with the Illumina array and the customized array, respectively. Genotypes were obtained on 196 dogs across both arrays. All the genotyped dogs had at least one phenotype for HD. However, only 99 dogs were phenotyped for OA. The 48 dogs with OA phenotype that were genotyped on the customized array were included in the dogs that were genotyped on the Illumina array . There were 111 SNPs in common between the two sets of SNP arrarys. The concordance rate was 99.999% of the 111 common SNPs genotyped on 196 dogs.The majority (92.3%) of the Illumina SNPs had call rate of 100%. There was 99.46% of SNPs with call rate above 95%. For the customized SNP array, 84.9% of SNPs had a call rate above 95%. SNPs with missing calls above 45% were removed. We also removed SNPs with minor allele frequency (MAF) below 1% Figure S5), we did not include breed as a co-factor in the model. The random effect was the groups clustered based on the kinship among individual dogs. The kinship among individuals was derived from all the SNPs. Individuals were clustered into groups which contained 3 individual dogs on average based on the optimization from our previous study A compressed mixed linear model was used for the association analysis. This approach is superior to other approaches with respect to minimizing false positive finding due to population structure and cryptic relationships accompanied by increased statistical power P values resulting from the F test were used to indicate the association strength. The majority of the SNPs from the two sets were analyzed independently. The 111 common SNPs underwent a joint analysis by combining all 721 dogs together. For the 196 dogs genotyped with both SNP sets, there were two sets of PCs and kinships. We used the SNPs from the Illumina array in the joint analysis. As the PCs derived from the two DNA arrays do not exactly reflect the same genetic components, we fitted PC nested within the two SNP sets. For the gap in kinship between the dogs genotyped on only one of the arrays, we used their pedigree based kinship. We used the visual approach P values for the association tests to those expected under the null distribution using Quantile-Quantile (QQ) plots.The genotype of each SNP was treated as a fixed effect, one SNP at a time. The Figure S1The linkage and association joint population.(0.08 MB PDF)Click here for additional data file.Figure S2Distribution of phenotyped dogs on Norberg Angle measurements.(0.03 MB PDF)Click here for additional data file.Figure S3The chromosome coverage of the customized SNP array.(0.03 MB PDF)Click here for additional data file.Figure S4The properties of single nucleotide polymorphisms (SNPs)(0.06 MB PDF)Click here for additional data file.Figure S5Population structure of linkage and association joint population.(0.18 MB PDF)Click here for additional data file.Table S1Number of dogs categorized by breed, phenotype and SNP platforms.(0.01 MB PDF)Click here for additional data file.Table S2Distribution of Norberg Angle measurements (number of dogs).(0.01 MB PDF)Click here for additional data file.Table S3Number of dogs with Norberg Angle measurements categorized by study population and SNP array.(0.01 MB PDF)Click here for additional data file.Table S4Total number of dogs with hip osteoarthritis _OA_ records categorized by study population and SNP array.(0.01 MB PDF)Click here for additional data file.


With the exception of APOE ε4 allele, the common genetic risk factors for sporadic Alzheimer's Disease (AD) are unknown.http://www.loni.ucla.edu/ADNI/Data/). Two analyses were completed: a standard case-control analysis, and a novel approach using hippocampal atrophy measured on MRI as an objectively defined, quantitative phenotype. A General Linear Model was applied to identify SNPs for which there was an interaction between the genotype and diagnosis on the quantitative trait. The case-control analysis identified APOE and a new risk gene, TOMM40 , at a genome-wide significance level of≤10−6 (10−11 for a haplotype). TOMM40 risk alleles were approximately twice as frequent in AD subjects as controls. The quantitative trait analysis identified 21 genes or chromosomal areas with at least one SNP with a p-value≤10−6, which can be considered potential “new” candidate loci to explore in the etiology of sporadic AD. These candidates included EFNA5, CAND1, MAGI2, ARSB, and PRUNE2, genes involved in the regulation of protein degradation, apoptosis, neuronal loss and neurodevelopment. Thus, we identified common genetic variants associated with the increased risk of developing AD in the ADNI cohort, and present publicly available genome-wide data. Supportive evidence based on case-control studies and biological plausibility by gene annotation is provided. Currently no available sample with both imaging and genetic data is available for replication.We completed a genome-wide association study on 381 participants in the ADNI (Alzheimer's Disease Neuroimaging Initiative) study. Samples were genotyped using the Illumina Human610-Quad BeadChip. 516,645 unique Single Nucleotide Polymorphisms (SNPs) were included in the analysis following quality control measures. The genotype data and raw genetic data are freely available for download (LONI, Using hippocampal atrophy as a quantitative phenotype in a genome-wide scan, we have identified candidate risk genes for sporadic Alzheimer's disease that merit further investigation. Age is a major risk factor for Alzheimer's disease (AD), and with both life expectancy and population increases, its incidence is expected to double in the next two decades The recent availability of high-throughput genotyping has allowed the simultaneous measurement of hundreds of thousands to more than one million SNPs (Single Nucleotide Polymorphisms), which has made genome-wide association studies (GWAS) feasible. A major challenge in case-control GWAS designs in which allele and genotype frequencies are compared between AD and control patients is achieving sufficient statistical power. Such categorical approaches require approximately 6,000 cases and controls to obtain 85% power to detect a 30% difference (odds-ratio of 1.3) with a minor allele frequency of 0.15 11C PIB) and blood and CNS biomarkers. Major goals of ADNI are to make this wealth of data available to the general scientific community and to improve methods for clinical trials by optimizing methods for imaging that are associated with clinical symptoms and the conversion of MCI to AD. The clinical and imaging measures from this longitudinal study have previously been made publicly available; the genome-wide genotyping results for 793 subjects from the Illumina Human610-Quad BeadChip  as performed by The Translational Genomics Research Institute , are now also publicly available.We present the results of a GWAS in the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort, a longitudinal multi-site observational study including AD, mild cognitive impairment (MCI), and elderly individuals with normal cognition In this initial analysis of the AD and the healthy control subsets of the data, we first performed a case control analysis and found APOE as well as an associated gene TOMM40, that provides additional risk for developing AD.in vivo. We used differences in the total grey matter concentration of the hippocampal area,  in AD subjects vs normal aged-matched controls in our model, since MRI studies have suggested that reductions in hippocampus over time can be particularly useful in predicting AD before the onset of clinical symptoms, and in assessing the efficacy of pharmacological treatment in clinical trials To complement the case-control/candidate gene approach to discover new risk genes for AD we performed a QT (quantitative trait) analysis. Our method uses the hippocampal grey matter density from neuroimaging as the quantitative trait, and examines which SNPs (as proxies for genes) influence the quantitative trait differently for AD and healthy controls. The concentration of grey matter in the hippocampus For the purpose of this analysis we used ADNI subject data that was previously collected across 50 sites. Study subjects gave written informed consent at the time of enrollment for imaging and genetic sample collection and completed questionnaires approved by each participating sites' Intitutional Review Board (IRB).www.loni.ucla.edu/ADNI). The ADNI was launched in 2003 by the National Institute on Aging (NIA), the National Institute of Biomedical Imaging and Bioengineering (NIBIB), the Food and Drug Administration (FDA), private pharmaceutical companies and non-profit organizations, as a $60 million, 5-year public-private partnership. The primary goal of ADNI has been to test whether serial magnetic resonance imaging (MRI), positron emission tomography (PET), other biological markers, and clinical and neuropsychological assessment can be combined to measure the progression of mild cognitive impairment (MCI) and early Alzheimer's disease (AD). Determination of sensitive and specific markers of very early AD progression is intended to aid researchers and clinicians to develop new treatments and monitor their effectiveness, as well as lessen the time and cost of clinical trials.Data used in the preparation of this article were obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database  and blood and CNS biomarkers. Brain imaging, biological samples, and clinical assessments are longitudinally collected for a target of 200 healthy controls, 400 MCI, and 200 AD subjects. For the AD sample, the study focuses on identification of sporadic cases of mild AD, subjects between the ages of 55–90, with an MMSE score of 20–26 inclusive and meeting NINCDS/ADRDA criteria for probable AD All subjects were part of the Alzheimer's disease Neuroimaging Initiative (ADNI), a longitudinal multi-site observational study including AD, mild cognitive impairment (MCI), and elderly individuals with normal cognition assessing clinical and cognitive measures, MRI and PET scans , data from a total of 172 AD subjects and 209 healthy controls were included in this analysis.The data collection methods have been described in more detail in Jack et al., 2008 The analyzed datasets included the T1-weighted structural MRI scans from all available subjects. Following the segmentation and quality assurance defined below, a final dataset of 229 healthy controls and 194 AD subjects provided grey matter concentration estimates of the hippocampus, using a voxel-based morphometry approach.The MRI images were converted from DICOM format into NIFTI format and re-oriented into MNI coordinate conventions using FSL tools Datasets with an extreme value of computed total brain density or the ratio of grey/white matter density were inspected, leading to the exclusion of several datasets in which segmentation was unsuccessful. Often, these were cases with large ventricles and/or diffuse white matter hypointensities. When there were multiple datasets per subject, the best segmentation for each subject was selected, blind to diagnosis or gender. The datasets of all subjects whose segmentation was successful were used to construct custom templates for the class prior probabilities. These custom templates were used in a second run to segment all datasets. To allow a comparison with the previous run, the same datasets were selected for further evaluation. Again, a quality check was performed using the total brain density and the ratio of grey/white matter density. The total grey matter was generally higher in this segmentation, which is very much expected with a better-matched template.The right and left hippocampal regions were defined using the AAL brain atlas Blood samples were obtained from each participant and sent to Pfizer for DNA extraction. All DNA extraction and genotyping was done blinded to group assignment. Genotyping on the blinded DNA was performed by TGen using the Illumina Human610-Quad BeadChip.www.loni.ucla.edu/ADNI/Data). The raw genotypic data were imported in a genome-wide data management system, i.e. SNPLims Approximately 200 ng of DNA was used to genotype each subject sample according to the manufacturer's protocol . After amplification, fragmentation and hybridization, the specifically hybridized DNA was fluorescently labeled by a single base extension reaction and detected using a BeadArray scanner. Non-specifically hybridized fragments were removed by washing while remaining specifically hybridized DNA were processed for the single base extension reaction, stained and imaged on an Illumina Bead Array Reader. Normalized bead intensity data obtained for each sample were loaded into the Illumina BeadStudio 3.2 software which generated SNP genotypes from fluorescent intensities using the manufacturer's default cluster settings. The data analyzed with BeadStudio are publicly available on the LONI website (http://pngu.mgh.harvard.edu/purcell/plink/), release v1.05. DNA samples with a call rate<95% were excluded. The average call rate in the remaining samples was 99.4%. From a total of 600,470 SNPs in autosomal chromosomes , the intensity-only probes from chromosomes 1–22 were excluded , and 5,592 SNPs were excluded by the PLINK algorithm. SNPs were included in the imaging genetics analysis if their minor allele frequency (MAF) was>.05, were present for>90% of the subjects, and did not depart from Hardy-Weinberg equilibrium for the controls using a threshold of 10−3. (Some SNPs were excluded for more than one category). The final number of SNPs included in the analyses is 516,645.The following Quality Control (QC) procedures were implemented on the genome-wide data. We performed all data analysis and QC using the PLINK software package  distribution between healthy controls and AD patients.Based on the significance of our finding regarding APOE ε4 in our sample and recent interest in this region we expanded our genotyping in the APOE region (∼300-Kb on chromosome 19). To increase the map density in the APOE region, we 1) merged the results of the genotyping (performed with the APOE-specific genotyping) of SNPs rs429358 and rs7412 and 2) performed imputation. To impute the region, the set “hapmap_r23” was downloaded from the HapMap site. This dataset includes 270 subjects from each one of the 4 populations studied by HapMap, i.e. CEU, YRB and CHB+JPT for 267 SNPs All autosomal SNPs that passed quality control checks were tested for quantitative trait association interaction using the general linear model as implemented in PLINK v1.5. The statistical model is based on comparing the differential effects of SNP association by diagnosis on the brain imaging quantitative trait, i.e. the interaction term.a priori chose the additive model as it is the most general, both reflecting the additive contribution to risks for complex diseases Based on the demographic analysis of our sample and the case-control allelic association test for APOE ε4, we covaried for age and gender and for the number of APOE ε4 alleles in the model. Out of the possible 4 models to choose from  we i represents the coefficients, SNP represents the genotype of each marker tested, APOE ε4 represents the allele dose of ε4, gender and diagnosis were coded as dichotomous variables, and age is the age of the subject in years.The general linear model used to determine the interaction term was:−6 or smaller provides statistical evidence for a main effect associating a given SNP with a quantitative trait. There is a lack of consensus on how to identify a genome-wide significance threshold. Reaching a consensus definition for an appropriate statistical threshold for the interaction term in a GWAS is even more complex. For the purposes of presenting the initial analyses of our data and considering the small sample size, the number of variables analyzed and the complexity of the model, we chose a conservative hypothesis for a threshold of 10−6 for our interaction term. In keeping with recommendations for presenting a GWAS analysis Our focus is on the SNP×diagnosis interaction. There is no accepted method for determining a statistical threshold for a quantitative trait interaction in a context of a genome-wide association study (GWAS). Given 516,645 SNPs and the sample size of 381 subjects, any result at 10http://www.genome.duke.edu/centers/pg2/downloads/wgaviewer.php) http://www.ensembl.org/) The Linkage Disequilibrium (LD) maps of each chromosomal region harboring the most significant SNPs were created using a window size of±200 Kb. D' values were calculated using Haploview 4.1 2 (1 d.f.) = 62.3, p-value = 2.98×10−15). Of the two SNPs comprising the APOE genotype, rs429358 SNP was significant at p-value = 2.30×10−16 and rs7412 at p-value = 5.00×10−4 (see −7) located in intron 2 of TOMM40 gene. The haplotype association via in silico reconstruction of proximal SNPs increases the importance of the findings at this locus. One of the haplotypes constructed with rs2075650 and the two flanking SNPs rs11556505 (TOMM40) and rs429358 (APOE) has greater significance  than the univariate p-value for the TOMM40 SNP.The APOE ε4 alleles were overrepresented in the AD cases as expected . The LD pattern of this region allows the identification of a 61-Kbp LD block that harbors the 5′ end of the EFNA5 gene. Eleven out of the 16 SNPs in this genomic area are nominally significant (10−6<p<.05) providing additional support for the involvement of EFNA5 in AD. Six more nominally significant SNPs (10−4<p<10−3) are located in a ∼31-Kbp LD block on the same chromosome, 64 Kbp upstream the gene and containing the pseudogene AC024587.5 characterized by 2 isoforms partially overlapping in the genome. The two isoforms may have different functions according to the biotype description: the first one is a scRNA pseudogene and the second is miscellaneous pseudogene.Two of the most significant SNPs, rs10074258 and rs12654281, are both related to the gene EFNA5  SNPs are located in the same 41-Kbp LD block that includes 15 SNPs in introns 6, exon 7 and the downstream region of the gene. PRUNE2 has two partially overlapping isoforms.SNP rs10781380 is in the gene PRUNE2, which spans a total of 117-Kbp and includes 44 SNPs. An additional six nominally significant (10−6. This SNP is a non-synonymous coding polymorphism (I509T or I474T) mapping C20orf132 that shows 4 different isoforms and encodes for the hypothetical protein LOC140699. The C20orf132 gene spans a total of ∼78-Kbp and 7 SNPs were genotyped successfully in our sample. The most significant SNP  is responsible for a threonine to isoleucine substitution. Three additional SNPs, rs8120307, rs1780682 and rs1744760 are nominally significant (10−5<p<10−3) and are located in intron 1 and 8 and exon 19 respectively. SNP rs1744760 (Q836R)  is another non-synonymous (arginine-glutamine) coding SNP. This gene is located in a ∼230-kbp genomic region characterized by a high degree of LD that contains several overlapping different genes: RBL1 (2 isoforms), RPS3AP3, RPS27AP3, RP3-343K2.4 and RPN2 (7 isoforms). This genomic region includes 10 SNPs beside the 7 located in C20orf132. SNP rs6031882  is located in intron 1 of RPN2. Two out of these total 10 SNPs are also nominally significant (10−5<p<10−3): rs1892205 and rs3897903 are located in intron 2 and 12 of RBL1.SNP rs8115854 is associated at the GWAS threshold significance level of 10−6) is a non-synonymous coding polymorphism (E88K) responsible for a glutamic acid (E) by lysine (K) substitution in the ZBP1 gene that encodes a Z-DNA binding protein. This SNP is located in a 26-Kbp LD block that encompasses only the 5′ upstream region of the gene and where 7 out of 10 SNPs are nominally significant (10−4<p<.05).SNP rs2073145  is located ∼8 kbp distal from the upstream of the pseudogene RP11-232A1.1 on chromosome 9. Its 27-Kbp LD block also contains 5 out of 8 SNPs nominally significant (10−5<p<10−3). The genomic region downstream from the pseudogene is characterized by a high degree of LD. The QC filters did not allow the calculation of the statistics for most of the SNPs that are included in this ∼73-Kbp LD block.SNP rs10867752  is located 34-Kbp from the 5′ end of CAND1 gene on a large LD block that also includes the gene itself. Thus, a synonymous-coding SNP rs1060350 (L1183L) of CAND1  might acquire relevance.SNP rs1082714 . SNP rs1364705 is related to the gene MAL2 and SNP rs11525066 to the gene MAGI2.−6) identifies a locus of interest in an apparently conserved region of ∼190-Kbp in chromosome 5 containing several different pseudogenes and genes: AC104108.3, SCAMP1 (3 isoforms), LHFPL2 (2 isoforms). The most significant SNP is located ∼14-Kbp from AC104108.3 but is also in strong LD (D' = 1) with the first typed SNP located at the upstream of the SCAMP1 gene. This genomic region includes 13 other SNPs out of 33 that are nominally significant (10−5<p<10−3) beside the top SNP.The four complex regions are characterized by the overlap of many different genes, isoforms of the same genes or pseudogenes. SNP rs6881634  is located in a 35-Kbp genomic region of chromosome 14 characterized by a very low degree of LD that harbors two isoforms of the same gene AL079307.7: a retrotransposed gene and a pseudogene. Due to the low degree of LD and the few SNPs typed that passed QC, it is not possible to link this SNP to any annotated genes. The genomic area surrounding this locus seems conserved based on the LD pattern, though, and might be of interest as it contains 2 additional isoforms of the same olfactory receptor pseudogene (OR7E) with different functions (i.e. rRNA and snRNA) overlapping with the gene FRMD6.SNP rs11626056  on chromosome 1 is located ∼7-Kbp from the locus AL157404.18, hypothetically coding a transcription regulatory misc RNA. This misc RNA is located in a conserved region characterized by high LD that contains several additional genes and their different isoforms: S100A5 (3), S100A4 (4), S100A3 (2) and S100A2 (5). None of the SNPs belonging to the S100A family genes passed QC filters.SNP rs4845552  on chromosome 12 is intergenic and identifies a locus of interest encompassing several different pseudogenes and genes: ARID2 (2 isoforms), SFRS2IP (4 isoforms) and the pseudogene AC009464.8. The most significant SNP is located ∼100-Kbp downstream SFRS2IP in a genomic region characterized by a high degree of LD. In the same region (from 44802396 bp to 44420742 bp) 22 out of 32 SNPs are nominally significant (10−5<p<10−3) beside the top SNP.SNP rs1373549 , ARSB (arylsulfatase B), MAGI2 , PRUNE2 (prune homolog 2), and CAND1 (cullin-associated and neddylation-dissociated 1) genes. These genes are associated with the aforementioned biochemical processes, namely ubiquination , apoptosis , oxidative necrosis (ARSB), hippocampal development (EFNA5), and dementia, . The presence of the highly conserved BCH domain (BNIP2 and Cdc42GAP homology) in PRUNE2 which is involved in a variety of protein-protein interactions and the presence of the BNIP-2 domain, a substrate for caspase cleavage suggest a role for PRUNE2 in apoptosis MAGI2, together with ATN1 (atrophin 1), has been implicated in Dentato-Rubral and PallidoLuysian Atrophy (DRPLA), a disease with a syndrome of myoclonic epilepsy, dementia, ataxia, and choreoathetosis The gene EFNA5 is highly expressed in the human brain and hippocampus. Ephrin-A5 is a member of the Ephrin (Eph) superfamily implicated in mediating developmental events in the nervous system N-acetylgalactosamine-4-sulfate in dermatan and chondroitin sulfate. A mutation in this enzyme leads to the development of the lysosome storage disease called mucopolysaccharidosis VI β peptide (A\) is toxic to neuronal cells by inducing oxidative stress ARSB (Arylsulfatase B) is a lysosomal enzyme that removes the sulfate ester group of −6.Our results highlight the variety of data that can be obtained from these different analytic approaches. Previous GWASs in AD using a categorical approach have not been able to consistently identify risk genes in AD other than APOE It is important to emphasize that the SNPs from the Illumina Human610-Quad BeadChip are tagging SNPs and as such represents “flags” for a possible location of a susceptibility locus to AD, and rarely – if ever – are the causal SNPs. The advantage of a tagging SNPs approach is that it maps an average 10 to max 20 kilobases in which a causal DNA variant may be found, thus indicating an area of focus for gene sequencing and subsequent studies of molecular mechanisms. The genes that we identified by surrogate SNPs are candidates that require additional confirmation in independent samples. Our method using brain imaging as a quantitative trait is fundamentally different from case-control approaches as it begins with brain imaging as a dependent variable and then identifies as genes that affect the brain imaging differences between AD and healthy controls. This is a very different contrast than comparing frequencies of genotypes across cases and controls and therefore is not expected to identify the same results as a QT approach. Interestingly, some of our findings are located in putative hot spots regions for AD, previously identified by other investigators using case-control, linkage and other methods. These overlapping hot spots include regions on chromosomes 5 The sample only contained mild AD patients, a relatively narrow range of illness, and therefore is not fully representative of the disease. Also, the ADNI sample was not collected under an epidemiological ascertainment strategy and the sample size itself is small, further limiting its generalizability. The small sample size, however, was somewhat mitigated by the four to eightfold increase in statistical power of a quantitative trait design over a categorical case-control analysis Hippocampal volume was chosen as our quantitative phenotype because of its sensitivity to the changes of early AD disease http://www.loni.ucla.edu/ADNI/Data/) for combining with other datasets and for other analytical approaches by the scientific community.This QT study is a cross-sectional analysis of baseline hippocampal grey matter density with common genetic variants as measured by SNP genotypes in the ADNI sample. A QT analysis provides a powerful strategy for gene discovery in the context of a genome-wide survey. ADNI provides longitudinal clinical, cognitive, biomarker and imaging data which will facilitate the search for genes that are associated with conversion from normal to MCI and from MCI to AD, as well as the rates of change within and between these categories. The goal of this preliminary analysis was to identify unanticipated risk genes for the development of mild AD as represented by hippocampal atrophy. Elucidating the roles of PRUNE2, MAGI2, ARSB and EFNA5 in the regulation of neurodevelopment, protein degradation, apoptosis and neuronal loss could enhance our understanding of hippocampal atrophy and AD. This method of gene discovery complements other established strategies such as case-control designs with large sample sizes, family-based design, and targeted candidate gene approaches. To address the concerns of multiple testing and false positive findings, all genotyped data are publicly available Click here for additional data file.Figure S2QT analysis of SNPs associated with genes or chromosomal regions as reported in (0.85 MB TIF)Click here for additional data file.Figure S3QT analysis of SNPs associated with genes or chromosomal regions as reported in (0.71 MB TIF)Click here for additional data file.Figure S4QT analysis of SNPs associated with genes or chromosomal regions as reported in (0.87 MB TIF)Click here for additional data file.Figure S5QT analysis of SNPs associated with genes or chromosomal regions as reported in (0.95 MB TIF)Click here for additional data file.Figure S6QT analysis of SNPs associated with genes or chromosomal regions as reported in (0.85 MB TIF)Click here for additional data file.Figure S7QT analysis of SNPs associated with genes or chromosomal regions as reported in (0.87 MB TIF)Click here for additional data file.Figure S8QT analysis of SNPs associated with genes or chromosomal regions as reported in (0.90 MB TIF)Click here for additional data file.Figure S9QT analysis of SNPs associated with genes or chromosomal regions as reported in (0.82 MB TIF)Click here for additional data file.Figure S10QT analysis of SNPs associated with genes or chromosomal regions as reported in (0.57 MB TIF)Click here for additional data file.Figure S11QT analysis of SNPs associated with genes or chromosomal regions as reported in (1.01 MB TIF)Click here for additional data file.Figure S12QT analysis of SNPs associated with genes or chromosomal regions as reported in (0.78 MB TIF)Click here for additional data file.Figure S13QT analysis of SNPs associated with genes or chromosomal regions as reported in (0.95 MB TIF)Click here for additional data file.Figure S14QT analysis of SNPs associated with genes or chromosomal regions as reported in (1.20 MB TIF)Click here for additional data file.Figure S15QT analysis of SNPs associated with genes or chromosomal regions as reported in (0.82 MB TIF)Click here for additional data file.Figure S16QT analysis of SNPs associated with genes or chromosomal regions as reported in (0.93 MB TIF)Click here for additional data file.Figure S17QT analysis of SNPs associated with genes or chromosomal regions as reported in (0.79 MB TIF)Click here for additional data file.Figure S18QT analysis of SNPs associated with genes or chromosomal regions as reported in (0.98 MB TIF)Click here for additional data file.Figure S19QT analysis of SNPs associated with genes or chromosomal regions as reported in (0.90 MB TIF)Click here for additional data file.Figure S20QT analysis of SNPs associated with genes or chromosomal regions as reported in (0.90 MB TIF)Click here for additional data file.Table S1Quantitative trait analysis for the interaction between left and right hippocampi for the SNPs and related genes or chromosomal regions listed in (0.03 MB XLS)Click here for additional data file.Table S2Quantitative trait analysis for the interaction between left and right hippocampi for the SNPs and related genes or chromosomal regions listed in (0.04 MB XLS)Click here for additional data file.Table S3Quantitative trait analysis for the interaction between left and right hippocampi for the SNPs and related genes or chromosomal regions listed in (0.03 MB XLS)Click here for additional data file.Table S4Quantitative trait analysis for the interaction between left and right hippocampi for the SNPs and related genes or chromosomal regions listed in (0.04 MB XLS)Click here for additional data file.Table S5Quantitative trait analysis for the interaction between left and right hippocampi for the SNPs and related genes or chromosomal regions listed in (0.04 MB XLS)Click here for additional data file.Table S6Quantitative trait analysis for the interaction between left and right hippocampi for the SNPs and related genes or chromosomal regions listed in (0.04 MB XLS)Click here for additional data file.Table S7Quantitative trait analysis for the interaction between left and right hippocampi for the SNPs and related genes or chromosomal regions listed in (0.04 MB XLS)Click here for additional data file.Table S8Quantitative trait analysis for the interaction between left and right hippocampi for the SNPs and related genes or chromosomal regions listed in (0.04 MB XLS)Click here for additional data file.Table S9Quantitative trait analysis for the interaction between left and right hippocampi for the SNPs and related genes or chromosomal regions listed in (0.04 MB XLS)Click here for additional data file.Table S10Quantitative trait analysis for the interaction between left and right hippocampi for the SNPs and related genes or chromosomal regions listed in (0.03 MB XLS)Click here for additional data file.Table S11Quantitative trait analysis for the interaction between left and right hippocampi for the SNPs and related genes or chromosomal regions listed in (0.04 MB XLS)Click here for additional data file.Table S12Quantitative trait analysis for the interaction between left and right hippocampi for the SNPs and related genes or chromosomal regions listed in (0.04 MB XLS)Click here for additional data file.Table S13Quantitative trait analysis for the interaction between left and right hippocampi for the SNPs and related genes or chromosomal regions listed in (0.04 MB XLS)Click here for additional data file.Table S14Quantitative trait analysis for the interaction between left and right hippocampi for the SNPs and related genes or chromosomal regions listed in (0.04 MB XLS)Click here for additional data file.Table S15Quantitative trait analysis for the interaction between left and right hippocampi for the SNPs and related genes or chromosomal regions listed in (0.04 MB XLS)Click here for additional data file.Table S16Quantitative trait analysis for the interaction between left and right hippocampi for the SNPs and related genes or chromosomal regions listed in (0.04 MB XLS)Click here for additional data file.Table S17Quantitative trait analysis for the interaction between left and right hippocampi for the SNPs and related genes or chromosomal regions listed in (0.04 MB XLS)Click here for additional data file.Table S18Quantitative trait analysis for the interaction between left and right hippocampi for the SNPs and related genes or chromosomal regions listed in (0.04 MB XLS)Click here for additional data file.Table S19Quantitative trait analysis for the interaction between left and right hippocampi for the SNPs and related genes or chromosomal regions listed in (0.04 MB XLS)Click here for additional data file.Table S20Quantitative trait analysis for the interaction between left and right hippocampi for the SNPs and related genes or chromosomal regions listed in (0.04 MB XLS)Click here for additional data file.


Recent studies have shown genetic variation is the basis of the genome-wide disease association research. However, due to the high cost on genotyping large number of single nucleotide polymorphisms (SNPs), it is essential to choose a small subset of informative SNPs (tagSNPs), which are able to capture most variation in a population, to represent the rest SNPs. Several methods have been proposed to find the minimum set of tagSNPs, but most of them still have some disadvantages such as information loss and block-partition limit.This paper proposes a new hybrid method named CGTS which combines the ideas of the clustering and the graph algorithms to select tagSNPs on genotype data. This method aims to maximize the number of the discarding nontagSNPs in the given set. CGTS integrates the information of the LD association and the genotype diversity using the site graphs, discards redundant SNPs using the algorithm based on these graph structures. The clustering algorithm is used to reduce the running time of CGTS. The efficiency of the algorithm and quality of solutions are evaluated on biological data and the comparisons with three popular selecting methods are shown in the paper.Our theoretical analysis and experimental results show that our algorithm CGTS is not only more efficient than other methods but also can be get higher accuracy in tagSNP selection. Recent studies show that the abundance of single nucleotide polymorphisms (SNPs) and haplotypes can provide the most complete information for genome-wide association studies. Through the analysis of SNPs and haplotypes, most of the genetic variations among different people can be discovered. However, due to the excessive SNPs, which are about 10 million in the human genome , it is cr2) between each other  ∈ {0,1,2}, where 0 and 1 are homozygous types which represent the major and minor alleles, and 2 indicates the heterozygous type. A set G of genotype sequences distributes in the genome region of given populations follow a functions P(gi). P(gi) is the frequency of gi, where gi is a genotype in G. Like the haplotype, genotype sequences only have a few common patterns in a given genome region and these patterns can be distinguished by the tagSNPs. According to the observation made by several biological studies  ≠ M. In addition, the average LD value of si with its relative sites is highest in the related site set.We are given  studies ,14, tagSG, all SNPs are distributed randomly as discrete sites in the space. If these sites are connected according to the genotype diversity information and the length of edge is set based on the LD value of two sites, the SNPs can form a graph. Through the analysis of several biological data, we discovered that the genetic-relative sites are bunched up in the graph and there are usually more sites around the real tagSNPs than the others except some special tagSNPs which have no association with any sites. That means it is feasible to construct a site graph to describe the SNP attributes and use a graph algorithm on it to find tagSNPs.To achieve this goal, the main problem is how to represent the two features of tagSNPs and associate them to find the real tagSNPs. For a given genotype set G, the SNP set is S (|S| = m), and the LD value matrix is R. Each SNP has some information of the genotype diversity. Therefore, for each site si, we define a diversity set Xi = {si. xr| xr = r, r ∈ }, for j = 0,1,2, n is the size of the input genotype set size, r is the sequence number of a genotype in given set, xr ∈ r-th genotype have the value j at i-th SNP. If Xi has two Φ subsets, which means the SNP has only one value at all genotypes, we assume this site has no diversity information, and delete it before the graph construction. If two diversity sets Xa = Xb, we assume these two sites have the same diversity information, and these sites can combine to one site in the graph for their similar information. However, it is too complex to combine the whole SNP diversity sets and their LD associations in one graph. Thus, the Xi is divided to three subsets S. In each graph, only the information of one subset is represented. The selection results for three graphs are combined to get the final tagSNPs. For a given S and the diversity subset si, j ∈ {0, 1, 2}, the site graph Gj can be constructed as following steps:For a given genotype set V: each site vi of V represents a SNP si of S. Each vi corresponds to the diversity subset si. Each vi has a site weight wsi defined by the number of the sites similar to si. For two SNPs sa and sb, sa is similar to sb in graph Gj when wsi is computed from two sets: the sites satisfy Xa = Xb and the sites satisfy Xa ≠ Xb and (1) The site set E: tagSNPs can identify all or at least most of the common genotype patterns. The diversity information is represented by Xi. The associated SNPs have their Xi overlap with each other. The overlapping of Xi causes the redundancy. TagSNPs are selected to delete these redundancies. The directed edges between vi are identified to describe these overlapping relationships. diversity subsets of sites a, b. If e from a to b. If e from b to a. If e between a and b. If (2) The edge set W: for edge e between sites a and b, the edge weight wab = R . R is the LD value r2 of the SNP a and the SNP b. The LD value r2 is usually chosen to evaluate the correlation between two SNPs , the running time is less than expectation.For a SNP subset which has the site set size SNPs: single nucleotide polymorphisms; LD: linkage disequilibrium; KNN: K nearest neighbour.The authors declare that they have no competing interests.JW proposed the idea, designed the experiments and wrote the paper under the supervision of MG and YW. All authors read and approved the final manuscript.


C. briggsae serves as a useful model organism for comparative analysis of developmental and behavioral processes. The amenability of C. briggsae to genetic manipulations and the availability of its genome sequence have prompted researchers to study evolutionary changes in gene function and signaling pathways. These studies rely on the availability of forward genetic tools such as mutants and mapping markers.The nematode C. briggsae strains AF16 and HK104. These include 1,363 SNPs that change restriction enzyme recognition sites (snip-SNPs) and 638 indels that range between 7 bp and 2 kb. We established bulk segregant and single animal-based PCR assay conditions and used these to test 107 polymorphisms. A total of 75 polymorphisms, consisting of 14 snip-SNPs and 61 indels, were experimentally confirmed with an overall success rate of 83%. The utility of polymorphisms in genetic studies was demonstrated by successful mapping of 12 mutations, including 5 that were localized to sub-chromosomal regions. Our mapping experiments have also revealed one case of a misassembled contig on chromosome 3.We have computationally identified more than 30,000 polymorphisms (SNPs and indels) in C. briggsae wild-type strains and demonstrate their use in mapping mutations. We also show that molecular markers can be useful tools to improve the C. briggsae genome sequence assembly. Our polymorphism resource promises to accelerate genetic and functional studies of C. briggsae genes.We report a comprehensive set of polymorphisms in  Caenorhabditis elegans (a nematode) and its congener, C. briggsae, are particularly suitable for such investigations. Their many experimental advantages include rapid growth, small size, transparency, ease of culture and genetic manipulations, and the availability of fully sequenced genomes [Comparative analysis of developmental and behavioral processes in closely related species is a powerful approach to understand the mechanisms of evolution. It facilitates identification of molecular components that are conserved over millions of years due to their role in specifying common features as well as those that are variable because they confer species-specific features. The model organism  genomes -3.C. briggsae is phenotypically almost indistinguishable from C. elegans and has a similar (hermaphroditic) reproductive mode. The last common ancestor of these two species lived about 30 million years ago [C. elegans genome aligns with the C. briggsae genome assembly [C. briggsae genes  with reciprocal orthologs in C. elegans [C. elegans-C. briggsae comparative genomic and genetic studies promise powerful new tools for the identification of genes and pathways and the study of both conservation and divergence.ears ago , and desassembly . This in elegans . Thus C.C. elegans, C. briggsae has six chromosomes that display extensive conservation of synteny, but not exact colinearity relative to C. elegans [C. briggsae shares many of the experimental advantages of C. elegans, it has the further advantage of increased natural variability for single nucleotide polymorphisms (SNPs) and insertion-deletions (indels) [Like  elegans . While C(indels) ,8. This C. briggsae employed cross-species transgene rescue of C. elegans mutants  and  and C. bts (e.g. ). Howeve screens -20  to furtThe indels were placed into three classes: small (7-49 bp), medium , and large (>2 kb). We have focused on medium and small indels , which offer the greatest utility as genetic markers. In the case of SNPs , we found that 4,700 modify restriction enzyme sites (termed snip-SNPs) and therefore can be easily detected as restriction fragment length polymorphisms (RFLPs). We established assay conditions for bulk segregant analysis (BSA) and used these to experimentally validate 14 snip-SNPs, 28 medium and 32 small indels. The validated polymorphisms were used to genetically map known mutations causing visible phenotypes thus demonstrating the effectiveness of the polymorphisms in linkage mapping studies. We also developed single animal-based PCR assay to determine map distance. Five mutations were successfully localized to sub-chromosomal regions by 3 or more indels, greatly facilitating the search for each candidate gene. These results demonstrate the utility of our mapping toolkit in genetic linkage and gene identification studies.C. briggsae strains by aligning paired shotgun sequence reads to the AF16-based reference sequence . These tructure , SNP denin silico digests of the resulting amplicons to infer the banding patterns for each strain. RFLPs not easily distinguishable on a gel, or SNPs on ultracontigs not yet included in the genetic map, were removed. Finally, we used assembly AGP information and BLAST alignment to obtain coordinates for each snip-SNP on the cb3 sequence assembly. Our set contained 1,987 predicted RFLP assays from 1,362 snip-SNPs  positioned on both the genetic and physical maps  genotyping. To make this a practical resource, we limited the analysis to 30 restriction enzymes from REBASE  that areHindIII, DraI and SalI snip-SNPs for validation in AF16 and HK104 parental DNA  based on tructure , VT847 wparse_indel utility to extract 7,530 candidate indels  for the HK104 strain. Most insertions and deletions detected by ssahaSNP were single base pair events; the largest was 49 bp. To identify larger insertion/deletion events, we developed a customized algorithm called BreakPointRead that detects indels based on BLAST alignments between read sequences and the reference genome. First, the algorithm identifies "breakpoint reads" with alignment gaps of 10 bp or larger compared to the reference sequence. Next, it analyzes the gap size and alignment orientations to infer the nature  and approximate size of sequence variation. When we applied BreakPointRead to the HK104 sequence traces, it identified 689 breakpoint reads suggestive of 635 underlying variants. We filtered the results to obtain insertion/deletion events between 50 and 2,000 bp. The resulting set contained 212 predicted indels (144 deletions and 68 insertions), the largest of which was a 1,707 bp deletion on chromosome IV  assays for medium indels to facilitate high-throughput gene mapping. Of the 212 putative indels, we selected 40 for validation in AF16 and HK104 parental DNA Figure . Two othin silico predictions, or misassembly), and 7 showed PCR products that were inconsistent and unreliable  indels in AF16 and HK104 that were identified by ssahaSNP. To allow for gel resolution, we excluded indels smaller than 7 bp. This resulted in 436 assays that had 7-49 bp band size differences between AF16 and HK104 .Altogether we experimentally confirmed 75 polymorphisms  on all six chromosomes  and unc(sy5415), all other loci were uniquely assigned to linkage groups by standard 2-factor mapping using known mutations that serve as reference (Table dpy(s1272), unc(s1270), and unc(sa997) are reference markers for LGIII, LGIV, and LGV, respectively. The remaining autosomal loci are linked to C. elegans orthologs Cbr-lin-11 (LGI) and Cbr-unc-4 (LGII). Not only did the polymorphism-based mapping agree with phenotypic marker-based mapping (see dpy(sy5001) and dpy(sy5148) in Figure lin(bh25)   (see Methods). As expected, for unlinked loci the ULV was one. In the case of dpy(sy5148) the ULV for chromosome 2 indel (bhP21) was 2.7 suggesting a strong linkage  that were previously isolated in different laboratories . The ULVs for dpy(sy5001) suggest weak linkages to indels cb-m204 (left arm) and cb-m136 (right arm) and tight linkage to the middle indel cb-m197  (data not shown).In addition to linking mutations to chromosomes we also investigated whether polymorphisms could be used in more precise mapping i.e., placing mutations in specific chromosomal regions . We reasoned that by narrowing down genetic intervals of mutations it should be possible to identify potential candidates, including 7 Figure . Similardpy(sy5001), we also mapped 4 autosomal mutations to sub-chromosomal regions using small indels. The results showed that dpy(s1272) is most strongly linked to bhP18, unc(sa972) to bhP14 and bhP18, and lin(bh20) to bhP40   appears to be located on the right arm of chromosome 4 (closer to the middle) since it shows strongest linkage to bhP9  Figure . The unc9 Figure .C. briggsae sequence assembly 'cb3' had placed the bhP18 contig fpc4010 on the right arm of chromosome 3 (~36.5 mu). We found that this location was inconsistent with ULVs for dpy(s1272) and unc(sa972) since both mutations are also linked to the left arm indel bhP14  and found that the region corresponding to bhP18 is misassembled. Therefore, we have assigned bhP18, as well as dpy(s1272) and unc(sa972), to the very end of the left arm of chromosome 3. Additional mapping using polymorphisms and visible markers will resolve the extent of misassembly.The 4 Figure . This sulin(sy5353) and an Unc mutation unc(sy5506). The lin(sy5353) mutation is located on chromosome 1 since it is strongly linked to three small indels bhP1, bhP7 and bhP42 . From a cross between lin(sy5353) and HK104 we picked 23 F2 mutant animals (46 chromosomes) and analyzed their DNA for the presence of bhP1. A single recombinant chromosome was recovered suggesting a recombination frequency of ~2% between lin(sy5353) and bhP1. In the case of unc(sy5506) mutation, located on chromosome X, we analyzed 20 F2 mutant animals for the presence of indel bhP26. A total of 4 recombinant chromosomes were recovered  to determine recombination distances between polymorphisms and mutations. For this we used a multivulva mutation linsy353 and aC. briggsae genome and experimentally validated a small set of these to facilitate mapping of mutations. Comparison of AF16 (reference strain) to four other isolates  revealed that HK104 is most polymorphic since it has the highest density of SNPs among all the strains. Altogether we identified ~31,300 polymorphisms  between AF16 and HK104 that promise to be a valuable resource for mutation mapping and genome evolution studies. Roughly 20% of the SNPs are predicted to alter restriction enzyme sites (snip-SNPs) that could be detected by PCR followed by restriction digestion and agarose gel electrophoresis.We took a bioinformatics approach to identify polymorphisms in the in silico predictions  that were experimentally tested, 66  showed DNA fragments identical (or close) to  elegans  showed t elegans  are truelin(bh25) and unc(sy5415), for which no prior genetic linkage data was available. Five mutations were also localized to sub-chromosomal regions. Thus our mapping resource can be used to rapidly map new mutations in C. briggsae. It is also relatively easy to validate additional polymorphisms if one needs a greater resolution. It should be pointed out that Hillier et al. [C. briggsae genome sequence assembly  of the genome united into six chromosomes [fpc4184 in the vicinity of fpc3441 (chromosome 1) based on the recombination distance of 5% between bhP42 (fpc4184) and bhP1 (fpc3441)  and unc(sa972). Additional snip-SNPs and indels could further refine the locations of these contigs.In addition to mapping mutations, SNPs and indels could also be used to improve the genetic linkage map of omosomes . The remC. briggsae isolates that were used for polymorphism discovery are HK104, HK105, VT847, and PB800. The HK104 strain was used in all mapping experiments. Various mutations used in this study are: csp-1(sa972), dpy(nm4), dpy(s1272), dpy(sy5001), dpy(sy5148), lev(sy5440), lin(bh20), lin(bh25), lin(sy5353), unc(sa997), unc(sy5415), unc(sy5422), and unc(sy5506).All strains were maintained at 22°C. The general methods of culturing nematodes are previously described . AF16 isC. briggsae repeat library.SNP discovery was performed on 13,632 shotgun sequence traces from strains HK104, VT847, HK105, and PB800. The ssahaSNP program (version SSAHA2)  was usedThe HK104 SNPs were positioned on the cb25 sequence assembly during SNP discovery. To position them on the newer cb3 sequence assembly, which is by chromosome, we obtained the assembly AGP files from Wormbase. SNP positions were inferred based on the coordinates and orientation of their cb25 ultracontig. SNPs on cb3-unmapped ultracontigs were mapped by WU-BLAST v2.0  alignment of their flanking sequences. Some 699 SNPs could not be positioned on the cb3 assembly by either method.parse_indel utility of ssahaSNP. In the HK104 set, the largest indel event identified by ssahaSNP was 49 bp. To identify larger insertion/deletion variants we implemented BreakPointRead, a custom algorithm that detects structural variations  spanned by individual sequence traces. Traces were aligned to the reference genome (cb25) using WU-BLAST v2.0 , and screened for alignments with "gaps" of > = 10 bp. The alignment patterns of such "breakpoint reads" were used to infer the type and size of polymorphism. Predicted insertions and deletions were set aside for assay development.Candidate AF16-HK104 indels were extracted from HK104 sequence traces using the bdP polymorphisms described in this study (snip-SNP bdP3 and medium indels bdP1 and bdP4) were identified in the laboratory of SEB. The snip-SNP bdP3 was earlier used in a study involving ray pattern variation in C. briggsae [The briggsae .Bio::Restriction::Analysis library of BioPerl [primer3 program [In silico fragment analysis of the PCR products was performed to predict band sizes for AF16 and HK104; assays with more than 4 bands in either strain were removed.SNPs were screened for substitutions that altered the recognition sequence of restriction enzymes using the  BioPerl . The ana BioPerl  known to program . In siliIn the case of small indels (7-49 bp), primers were selected to generate AF16 amplicon sizes within the range of 200 and 400 bp. For medium indels , primers flanking each indel and specifying an AF16 amplicon size of 300-800 bp were selected.In all experiments the genomic DNA from F2 worms  was used as a PCR template. In some control experiments genomic DNA from F1 heterozygous animals was also used. PCR results that gave rise to unexpected or no products were repeated at least twice. In some cases we also tested different annealing temperatures. Those that consistently failed were termed as "PCR failure".2, 10× Buffer , and 4.2% DMSO. Amplification was carried out in a Perkin-Elmer 384 PCR plate containing 12 μl of 1× PCR mixture. The 384-well plate was sealed with Microseal A (MJ Research) before carrying out the PCR. After initial heat denaturation step (95°C - 2 min.) we used a fixed 35 cycles PCR  and a final extension (68°C for 10 min.). PCR products were analyzed on 10% polyacrylamide gels that consisted of 33.3% of 29:1 acrylamide:bis (Biorad), 10% of 10×TBE, 15% of glycerol, 40.2% H20, 3.4 × 10-2% TEMED (Int'l Biotech), and 1.42% of 10%APS. Gels were stained with SYBR green (Invitrogen) and inspected over a UV light box at 254 nm.For RFLP and medium indel assays, 10 ng dry PCR primers  were resuspended into 40 μM in a 96-well format. Our PCR mixture consisted of 300 pg genomic DNA, 0.1 μM PCR of each up and down primer, 0.02 U Platinum Taq (Invitrogen), 83.3 μM per base dNTPs, 2.92 μM MgClTo detect small indels (<50 bp) we used a standard 35 cycles PCR  for all amplifications. For BSA-based mapping the genomic DNA was prepared from 25 adults in a 10 μl lysis buffer . The mixture was frozen at -80°C (at least 30 min) and then placed in a thermal cycler for 1 hr incubation at 60°C followed by 15 min heat inactivation at 95°C. The resulting genomic DNA was diluted to 25 μl using sterile distilled water and stored at -20°C. The PCR mixture (25 μl) contained 1 μl genomic DNA, 1 μl each of up and down primers, 1 μl dNTPs, 2.5 μl NEB ThermoPol 10× PCR buffer, 0.2 μl NEB Taq Enzyme, and 18.3 μl sterile distilled PCR grade water. For SRA-based mapping, single worms were placed in 5 μl lysis buffer and processed as above. The lysed samples were used as DNA templates in PCR experiments. The amplified products were first analyzed on 1% agarose gel . Successful amplifications were subsequently examined on a 4% high-resolution agarose gel  to determine the presence of indels.We picked 12 strains for linkage mapping studies Table . The mutTo determine the linkage of a mutation to a chromosome, we initially relied on the visual inspection of DNA band intensities on Ethidium bromide-stained agarose gels. Subsequently, in indel-based mapping experiments, we calculated linkages as unitless values (ULVs) for an unbiased analysis. The mean intensities of DNA bands were measured by NIH ImageJ software  using MC. briggsae SNP Research Facility website [The genetic positions of snip-SNPs and indels in this study correspond to nearest SNPs that were experimentally validated . These 'verified' SNPs  were genotyped in RILs derived from two independent crosses between AF16, HK104 and VT847 (AF16 × HK104 and AF16 × VT847). The details are available on the  website .C. briggsae resource website (see "Polymorphism" link) [C. briggsae SNP Research Facility website [All identified snip-SNPs, indels and PCR primers, confirmed or otherwise, are accessible via the m" link) . The web website . The con website .BSA: Bulk segregant analysis; Indel: Insertion-deletion; PLP: PCR fragment length polymorphism; RIL: Recombinant inbred line; RFLP: Restriction fragment length polymorphism; SNP: Single nucleotide polymorphism; SRA: Single recombinant analysis; ULV: Unitless linkage value.C. briggsae Advisory Group  planned the project and collected the genetic and financial resources to perform the work. DCK and RDM carried out bioinformatics searches. DCK designed snip-SNP and PLP assays. JES, BT and KH performed PCR experiments to validate polymorphisms and map mutations. BT and BPG calculated ULVs for mutations linked to medium and small indels. bdP polymorphisms were identified and validated in the laboratory of SEB. BPG wrote the paper with input from ESH, HMC, SEB, DCK, and JES. All authors read and approved the final manuscript.The C. briggsaeList of SNPs in . An Excel file containing 23,829 computationally identified SNPs in AF16 and HK104.Click here for fileList of snip-SNPs and RFLP assays. An Excel file containing 1988 SNPs that alter restriction enzyme site (snip-SNPs) along with PCR primers and predicted digestion patterns in AF16 and HK104.Click here for fileList of medium indels and PLP assays. An Excel file containing 214 medium indels along with PCR primers and predicted DNA fragments in AF16 and HK104.Click here for fileList of small indels and PLP assays. The Excel file containing 436 small indels along with PCR primers and predicted DNA fragments in AF16 and HK104.Click here for fileunc(sy5506) mutation by single recombinant analysis (SRA)Mapping of . Twenty single F2 mutant animals were individually examined by PCR for the presence of indel bhP26. Four of these  were found to be recombinants, as judged by the presence of two bands on the agarose gel (corresponding to AF16 and HK104 DNA).Click here for fileC. briggsae snip-SNPs identified prior to this work. An Excel file containing nine C. briggsae snip-SNPs that were validated by Hillier et al. [r et al. .Click here for file