The domestic cat has offered enormous genomic potential in the veterinary description of over 250 hereditary disease models as well as the occurrence of several deadly feline viruses  that are homologues to human scourges . However, to realize this bio-medical potential, a high density single nucleotide polymorphism (SNP) map is required in order to accomplish disease and phenotype association discovery.To remedy this, we generated 3,178,297 paired fosmid-end Sanger sequence reads from seven cats, and combined these data with the publicly available 2X cat whole genome sequence. All sequence reads were assembled together to form a 3X whole genome assembly allowing the discovery of over three million SNPs. To reduce potential false positive SNPs due to the low coverage assembly, a low upper-limit was placed on sequence coverage and a high lower-limit on the quality of the discrepant bases at a potential variant site. In all domestic cats of different breeds: female Abyssinian, female American shorthair, male Cornish Rex, female European Burmese, female Persian, female Siamese, a male Ragdoll and a female African wildcat were sequenced lightly. We report a total of 964 k common SNPs suitable for a domestic cat SNP genotyping array and an additional 900 k SNPs detected between African wildcat and domestic cats breeds. An empirical sampling of 94 discovered SNPs were tested in the sequenced cats resulting in a SNP validation rate of 99%.These data provide a large collection of mapped feline SNPs across the cat genome that will allow for the development of SNP genotyping platforms for mapping feline diseases. Felis silvestris lybica), recently estimated at approximately 10,000 years ago in the Middle East's Fertile Crescent  2] for c Crescent. In spit Crescent.The availability of a sufficiently dense single-nucleotide polymorphism (SNP) map for a species provides a resource which enables the power of automated high-throughput genotyping to associate regions of the genome to hereditary diseases, quantitative traits, and other phenotypes. High density SNP maps are available for many species including human, mouse, dog, chicken, and rice (for a complete list see ). The caDNA from six domestic cats and one wildcat was collected and isolated using the PAXgene Blood DNA kit as per manufactures instructions  and stored at -80°C until library construction. The domestic cat samples represented six different breeds from pet owners in the Topeka, KS area, and the wildcat sample was from a captive animal residing at the Audubon Nature Institute, LA. Fosmid libraries were prepared in vector pCC2fos from each DNA sample with an average insert size of approximately 37 kb. A total of 3,178,297 paired-end sequencing reads were generated from the seven libraries  which reduced genomic diversity appreciably relative to the wildcat species[SNPs are called using the ssahaSNP method by compat species. DomestiTo validate the accuracy of SNP detection we randomly chose 555 SNPs detected from the fosmid-end sequence of six domestic cats sequenced in this study. We selected SNPs located at least 750 bases away from a sequence contig gap (to make primer design feasible) reducing the number of testable SNPs to 393 of which 348 yielded primers using a primer design package. Two setSequence traces were analyzed using PolyPhred version 6.11-16 and tForty-five detected DIPs from the sequence traces fell within the PCR amplicons. Of these, 43 were empirically validated, one was not tested and one did not validate (a single base insertion). Therefore, the DIP validation rate would be 43/45 = 96%.gDNA concentration was determined using a DyNA Quant 200 fluorometer (Hoefer) and the dsDNA specific dye Hoechst Dye 22358 according to the manufacturer's protocol. The gDNA sample was then tested for functionality in PCR reactions with positive and negative control primers.Pos_For: TGTAAAACGACGGCCAGTATCCCACTGTTAGGAGAACTGCPos_Rev: CAGGAAACAGCTATGACCGGTCAGGAAAGGGACACAGATANegative control primers were the forward and reverse sequencing primers to lac-Z of M13.M13_For: TGTAAAACGACGGCCAGTM13_Rev: CAGGAAACAGCTATGACCTo each gDNA a trace amount of a plasmid with a unique non-feline insert was added. This plasmid was used as a biological barcode. The identifying inserts were amplified and checked using the universal sequencing primers above. The gDNAs were then diluted to a working concentration of 2.5 ng/ul.Primers were obtained from Eurofins MWG Operon in individual tubes and reconstituted to 100 uM in 10 mM TRIS, pH 8.0, 0.1 mM EDTA. The primer pairs were tested at a concentration of 0.16 uM each in 10 ul PCR reactions containing iQ supermix (BioRad) and 5 ng of control feline DNA (Tipper sample used for first round and Speedy used for second round of QC). Cycling conditions were: activate enzyme at 95C for 3 min, followed by 40 cycles of 95C for 15 sec, 60C for 15 sec, 72C for 60 sec, then 72C for 5 min and hold at 10C. A 5 ul aliquot of the PCR reaction was examined by agarose gel to look for multiple or missing bands. The PCR products were then diluted to 0.4 ng/ul and sequenced in 6 ul reactions using M13 Universal forward and reverse primers and BDT version 3.1 (Applied Biosystems) using standard ABI protocols. The reactions were sequenced on 3730 DNA Sequencers (Applied Biosystems). The sequence traces were then individually inspected for quality. Primer pairs that resulted in high-quality traces were passed. Primers not passing this round were retested using one additional control DNA. Primers failing both rounds were not used.PCR amplification of amplimers was performed in 10 ul reactions in 384-well plates. The reaction conditions were as described above.The existing map of 327 k SNPs from the 1.9X Cinnamon assembly includes large homozygous segments covering approximately 60% of the cat genome . FigureA) SNPs excluding those discovered only from Cinnamon and/or Nancy, B) SNPs excluding those only discovered in Cinnamon, and C) all SNPs  loci were used to resolve genomic distinctions among wildcat subspecies effectively[Felis silvestris cafra rather than F.s. libyca. For additional details of this analysis, see Additional file Finally we investigated the geographical origin of the wildcat Nancy. With nearly 1.4 M reads available for this wild cat, this does provide a valuable resource for studying subspecies of The primary goal of this effort is to expand feline SNP resources, empowering future linkage and association studies to map feline disease phenotypes to genomic loci. The usefulness of such a resource is clearly proven in many species, most notably for the human and canine genome-20. A reFelis catus entries in [The value of the sequence generated by this effort will become even greater as the publicly funded effort (see tries in ) to genetries in .All SNPs and DIPs described are freely available through dbSNP  and throJCM drafted the manuscript. SWKA and MFL organized the sample collection. HE, WFD, WT, DJS, AB, and MJR performed the fosmid library construction and sequencing. PC, VD, ACY and NISC performed the PCR primer design and sequencing. VD and CD did the STR typing and analysis to identify the likely origin of the wildcat. LS, NFH and JCM performed the sequence alignment and SNP/DIP detection and validation review. JCM, JAB, SJO, DRS and MSA conceived of the study, and participated in its design and coordination and helped to draft the manuscript. All authors read and approved the final manuscript.Whole genome assembly statistics. Table S1 comparing the assembly statistics for this assembly and the previously published 1.9X assembly.Click here for fileAssembly coverage of ENCODE regions. Table S2 listing whole genome shotgun assembly coverage statistics relative to high quality cat BAC clone assemblies of ENCODE regions for this assembly and the previously published 1.9X assemblyClick here for fileOrder and orientation across ENCODE regions. Plots of assembly order and orientation across all 44 ENCODE regions.Click here for fileDescriptions of how Additional files 3 and 6 were generated. Methods for generating plots of order and orientation across ENCODE regions and the STRUCTURE analysis of Nancy.Click here for filePCR validation results for 94 variants. Table S3 lists the variants by position on the genome assembly, which alleles are expected, and the alleles observed across 8 cats. Pink colored cells indicate the cat(s) from which the alternate allele was discovered in the light whole genome sequence.Click here for fileSTRUCTURE analysis results. Figure S1 shows results of STRUCTURE analysis showing Nancy and representative known-origin wildcat individuals.Click here for file


Single nucleotide polymorphisms (SNPs) and small insertions or deletions (indels) are the most common type of polymorphisms and are frequently used for molecular marker development. Such markers have become very popular for all kinds of genetic analysis, including haplotype reconstruction. Haplotypes can be reconstructed for whole chromosomes but also for specific genes, based on the SNPs present. Haplotypes in the latter context represent the different alleles of a gene. The computational approach to SNP mining is becoming increasingly popular because of the continuously increasing number of sequences deposited in databases, which allows a more accurate identification of SNPs. Several software packages have been developed for SNP mining from databases. From these, QualitySNP is the only tool that combines SNP detection with the reconstruction of alleles, which results in a lower number of false positive SNPs and also works much faster than other programs. We have build a web-based SNP discovery and allele detection tool (HaploSNPer) based on QualitySNP.HaploSNPer is a flexible web-based tool for detecting SNPs and alleles in user-specified input sequences from both diploid and polyploid species. It includes BLAST for finding homologous sequences in public EST databases, CAP3 or PHRAP for aligning them, and QualitySNP for discovering reliable allelic sequences and SNPs. All possible and reliable alleles are detected by a mathematical algorithm using potential SNP information. Reliable SNPs are then identified based on the reconstructed alleles and on sequence redundancy..Thorough testing of HaploSNPer  has shown that EST information alone is sufficient for the identification of alleles and that reliable SNPs can be found efficiently. Furthermore, HaploSNPer supplies a user friendly interface for visualization of SNP and alleles. HaploSNPer is available from  Single nucleotide polymorphisms (SNPs) and small insertions or deletions (indels) are the most common type of genetic polymorphisms and well suited for molecular marker development due to their abundance within the genome and their slow mutation rate . SNPs haHaplotypes can also be reconstructed for specific genes, based on the SNP present in the gene . HaplotyIn this paper we describe HaploSNPer, a web-based tool for the reliable detection of alleles and SNPs. It is based on finding homologous sequences in user-specified sequence databases using a user-supplied seed sequence, or on a collection of input sequences. HaploSNPer combines the QualitySNP algorithm with database search and sequence alignment tools into an efficient pipeline.HaploSNPer includes BLASTN of the BLAST package  to find HaploSNPer is a user-friendly and flexible tool Figure , which cIn the QualitySNP algorithm, a potential haplotype is defined as a group of sequences within a cluster that have the same nucleotide at every polymorphic site . For hapClusters containing paralogous sequences can be expected to contain more polymorphisms than clusters with only allelic sequences. A method based on the number and frequency of polymorphisms may therefore separate paralogs from alleles, such as implemented in POLYBAYES . HoweverWith increasing D-value the difference in number of SNPs among haplotypes is larger, so there is a higher probability of including alleles as well as paralogs in the cluster. At lower D-values, the probability that all the potential haplotypes found by QualitySNP are indeed alleles of a gene is higher, so most clusters with low D-values will contain few or no paralogs.E-value of BLASTN is to find significantly similar sequences  reported by [As a second example of the potential of HaploSNPer we analyzed the wheat alpha-gliadin gene sequences described by , to showorted by  for the orted by  were detThrough extensive testing we have shown that HaploSNPer  can efficiently detect reliable SNPs, reconstruct haplotypes and therefore identify different alleles using only EST sequence information. Furthermore, HaploSNPer supplies a user friendly interface for visualization of SNPs and alleles, which supplies the selection of informative SNP and allele-specific markers.Project name: HaploSNPer;;Project home page: Operating system(s): platform independent;Programming languages: C, PHP, C-shellAny restrictions to use by non-academics: noneBV, JL and JT identified the need to develop the program. JT designed the program and wrote the source code. All authors contributed to the overall design and feature requirements, and participated in the drafting of the manuscript and approved the final version.


Whole genome association studies using highly dense single nucleotide polymorphisms (SNPs) are a set of methods to identify DNA markers associated with variation in a particular complex trait of interest. One of the main outcomes from these studies is a subset of statistically significant SNPs. Finding the potential biological functions of such SNPs can be an important step towards further use in human and agricultural populations . The current challenge is that the information holding the clues to SNP functions is distributed across many different databases. Efficient bioinformatics tools are therefore needed to seamlessly integrate up-to-date functional information on SNPs. Many web services have arisen to meet the challenge but most work only within the framework of human medical research. Although we acknowledge the importance of human research, we identify there is a need for SNP annotation tools for other organisms.We introduce an R package called FunctSNP, which is the user interface to custom built species-specific databases. The local relational databases contain SNP data together with functional annotations extracted from online resources. FunctSNP provides a unified bioinformatics resource to link SNPs with functional knowledge . We also introduce dbAutoMaker, a suite of Perl scripts, which can be scheduled to run periodically to automatically create/update the customised SNP databases. We illustrate the use of FunctSNP with a livestock example, but the approach and software tools presented here can be applied also to human and other organisms.Finding the potential functional significance of SNPs is important when further using the outcomes from whole genome association studies. FunctSNP is unique in that it is the only R package that links SNPs to functional annotation. FunctSNP interfaces to local SNP customised databases which can be built for any species contained in the National Center for Biotechnology Information dbSNP database. Whole genome association studies (WGAS) are a set of methods to study common genetic variation across the entire genome of organisms with the purpose of identifying associations between genetic polymorphisms with observable phenotypic variation in traits. Single nucleotide polymorphisms (SNPs) are the most common genetic variant. There are essentially two advances that have allowed WGAS. Firstly, the huge increase in the availability of SNPs. For example, the dbSNP database housed by the National Center for Biotechnology Information (NCBI) contains millions of SNPs for many model species. Secondly, the falling cost and rapid improvements in SNP genotyping technology. One of the main outcomes from WGAS is a subset of SNPs at a specified level of statistical significance  and false discovery rates (FDR). These SNPs are currently used in two conventional, albeit alternative, approaches depending on the requirements of the researcher: 1) a molecular biologist usually searches for candidate genes in the region of the significant SNP and then does functional experiments, which could include targeted sequencing; 2) a geneticist uses significant SNPs as DNA markers for predicting disease risks, phenotypic performance or breeding value in individuals and may use this information in associated trait selection (marker-assisted selection/gene-assisted selection).One of the major challenges for a researcher using WGAS is minimising false positive rates while maintaining the power to identify true positive associations . WGAS reknown biological effect or linkage to a causal SNP; 4) a SNP not associated with the complex trait . Since the type of significant SNP following WGAS is not distinguishable to the researcher, there is a potential to perform functional experiments based on a false-positive SNP and/or use a SNP as a marker that is neither the causal variant nor in LD with the causal variant.In WGAS, association does not imply causation. The significant SNPs derived from WGAS can be generally classified into 4 types: 1) a causal SNP that contributes to variation in the complex trait; 2) a SNP with no known biological effect but in LD to an untagged SNP (not genotyped) that contributes to variation in the complex trait; 3) a SNP with an association only - no Currently, the only approach to determine if a WGAS derived significant SNP is a true positive and in LD with the causal variant is to replicate the association findings in an independent population of adequate size . Hence, http://www.r-project.org/.Table For many of the bioinformatics tools, third party applications are required for upstream and downstream research analyses, which in most cases require data format changes. The R environment has a global data currency of vectors and data frames so there are few data conversion concerns between R packages. For instance, there are two R packages called GenABEL  and SNPaA primary goal of FunctSNP is to help screen and select the WGAS significant SNPs which are more likely to be reliable as DNA markers. WGAS makes the direct association between SNP variation and phenotype variation. Whereas in reality SNP variation affects phenotype variation through the effects it has on multiple intervening biological pathways and networks . FunctSNThe biological information required for SNP annotation is publically available in online databases. Although most of these online resources provide cross-links to other databases, it is not straightforward to extract the relevant research data in one query. Furthermore, even though the databases contain well-annotated data, finding and managing the data of interest among the large assortment of data can be a challenge. Our solution to superfluous and disseminated data was to link SNPs with functional knowledge within species-specific local relational databases.http://www.sqlite.org/. It stores data in a file that is platform independent, provides batch mode functionality, and is the preferred RDMS for R. The data contained in the online resources is dynamic, so we have developed a suite of Perl scripts, collectively called dbAutoMaker, to provide a framework in which to automate the process of creating a local database with data extracted from any number of resources. Figure We have made available 5 species-specific databases for cattle, chicken, sheep, pig, and human. Each database contains the most pertinent SNP related data extracted from 7 online resources Table . We use http://www.csiro.au/science/dbAutoMaker.html].The core of dbAutoMaker is a species-specific configuration file in an INI file format. Although we have demonstrated dbAutoMaker using mainly livestock species, it can be configured by the user to create a SNP database for any species contained in NCBI's dbSNP [for more detail refer to dbAutoMaker User Guide on Figure http://www.csiro.au/science/FunctSNP.html. The user can directly query the database using Structured Query Language (SQL) through the SQLite program. However, we recommend FunctSNP as the database user interface.The SQLite databases can be downloaded from Homo sapiens database is information rich in comparison to all other species. Bos taurus and Gallus gallus databases are poor in comparison to Homo sapiens but substantially richer compared to Sus scrofa and Ovis aries. The table in Additional file The quality of results that can be expected from FunctSNP is reliant on the amount and reliability of information extracted from the online resources. For example, based on the amount of data available, the FunctSNP provides its utility through R functions. So far, 20 R functions Table  have beeThe FunctSNP functions provide access to the biological information contained within species-specific databases. Information such as: SNP chromosomal location, exon/intron status, synonymous/non-synonymous effect, SNPs in Quantitative Trait Loci (QTL) regions, biological pathways, GO terms, and protein products for related genes. Multiple databases (one for each species) can be queried in the same R session.Central to the SNP databases are the SNP ID (NCBI's dbSNP rs# cluster ID) and gene ID (NCBI's gene ID). Consequently, the majority of FunctSNP functions accept, as input, a vector of either SNP IDs or gene IDs by specifying what type of IDs are being passed to the function using id.type. For example:snps <- getSNPsThe getSNPs  function returns SNP information  for each SNP ID in the ids vector.snps <- getSNPsall SNPs residing within each of the genes specified by their gene IDs in the ids vector.This time, the getSNPs  function returns SNP information for As a general guideline to the user in prioritising the SNPs most likely to have significant functional roles, all SNPs in the database have been scored. The total score is comprised of a simple sum of two components. The first component is a score according to the SNP location and the type of amino acid alteration it may cause. We use NCBI's dbSNP function classification for SNPs on genes. The table in Additional file There are R functions to: find the nearest genes to each SNP ID entered, using a chromosomal base pairs search distance - getGenesByDist; find the nearest high scoring SNP to each SNP ID, or chromosomal location entered - getHighScoreSNP; find homologous genes across all species or a specified species for each SNP ID, or gene ID entered - getHomolo; find the quantitative trait loci (QTL) region associated to a trait (as annotated within QTLdb database) for each SNP ID or gene ID entered - getTraits; and extract SNP ID for each chromosomal location entered - getSNPID. There are also R functions to download the latest customised SNP database for a particular species  - downloadDB; and a function to invoke dbAutoMaker for the user to make their own databases - makeDB.To test FunctSNP, we used 10 k SNP genotype data from a yet to be published livestock WGAS. From this 10 k SNP data, 165 SNPs had been identified as being significantly associated with a particular trait of economic interest. These SNPs were sufficiently quality controlled and accepted as reliable prior to using FunctSNP.The aim of the test was to rank the 165 SNPs according to their likelihood of being reliable DNA markers. Our premise for reliable DNA markers is that WGAS derived SNPs should be either the causal variant or close to a SNP that is the causal variant. In FunctSNP, whether a SNP is a potential causal variant is determined by its assigned functional role and its links to biological information . The SNPs in the database have been scored in accordance with this premise. Although, high scoring SNPs in FunctSNP are the most likely to have significant functional roles, the expertise of the researcher is required to make that important mental link between the biological information provided and its relevance to the trait of interest. We also use the general rule that the closer a WGAS derived SNP is to a high scoring SNP the more likely the SNPs will be in LD. However, the researcher needs to be aware that this rule does not always hold true as there are blocks of SNPs along the chromosome with higher and lower degrees of LD. For example, for the most part the closer 2 SNPs are together the stronger the LD is going to be, nevertheless, it is possible that the 2 SNPs are located on different blocks of LD.The test involved 4 different runs based on the following criteria that determined the data input to the FunctSNP functions: 1) using all 165 WGAS derived significant SNPs; 2) using only significant SNPs with a score greater than 7; 3) using SNPs with a score greater than 11 from the database that are within 10,000 base pairs from the 165 significant SNPs; and 4) using SNPs from the database with a score greater than 24 that are located on genes within 10,000 base pairs from the 165 significant SNPs. The threshold score used is at the discretion of the user. SNPs with a score equal or greater than 20 are considered highly likely to have significant functional roles. The base pairs search distance is also subjective. Knowledge of the LD patterns for the research species will need to govern the distance used.http://www.csiro.au/science/FunctSNP.html). For example, GO IDs e.g., GO: 0001875, can be input under "Search the Gene Ontology Database" at http://www.geneontology.org/; KEGG pathway names e.g., path:bta04620, input "bta" under "Select Prefix" and "04620" under "Enter Keywords" at http://www.genome.jp/kegg/pathway.html; and UniProt IDs e.g., Q4GZT3, input under UniProt homepage at http://www.uniprot.org/.Table The purpose of run 3 was to find the highest scoring SNPs in the database residing in close proximity to the significant SNPs. We found 15 SNPs located on 15 different genes and 8 of these SNPs were considered to have significant functional roles. Similarly, the purpose of run 4 was to find genes in close proximity to the significant SNPs and then find the highest scoring SNPs residing on these genes. We found 6 genes in close proximity that contained 11 SNPs with significant functional roles. Based on the results of the 4 tests we were able to rank the 165 SNPs according to their likelihood of being reliable DNA markers and concluded that the top 8 were realistic candidate DNA markers. In particular, 2 WGAS derived SNPs were identified to be in close proximity to 2 genes influencing biological pathways that literature have linked to the WGAS trait of interest. See additional file As part of the ongoing development, we will monitor web resources for new and improved sources of data that can be incorporated into FunctSNP. The design of dbAutoMaker allows data to be extracted from new online resources with minimal modification to dbAutoMaker's configuration file.FunctSNP and our approach for identifying SNP markers currently make the assumption that all causal SNPs are located on genes. There are now many suggestions that non coding SNPs  could sTo take full advantage of WGAS, we need to make that essential link between the outcomes from WGAS and the information that exists about the function of genes and pathways. Whilst there are many emerging bioinformatics tools to make this link, we have developed a tool called FunctSNP which we believe is unique and addresses some of the shortcomings of existing tools. Firstly, FunctSNP is the only R package designed specifically for SNP annotation. There are considerable advantages over a web interface in integrating FunctSNP within the R statistical computing environment offering high quality graphics and data visualization tools. Operating in the same R environment, FunctSNP has notable complementary upstream and downstream research R packages. Secondly, the purpose built database for FunctSNP can include information about SNPs in other organisms besides human. We have developed an application called dbAutoMaker for a researcher to automatically build their own SNP database for any species contained in NCBI's dbSNP. We have scheduled dbAutoMaker to build databases for cattle, chicken, pig, sheep, and human on a monthly basis. These databases can easily be downloaded whenever required directly from the web or using a FunctSNP function.We conclude that FunctSNP is a post-WGAS tool that provides an opportunity to screen and select for SNPs that have a higher likelihood to be related to variation in a particular complex trait of interest.The FunctSNP R package is available from CRAN:http://cran.r-project.org/package=FunctSNP. In FunctSNP there are no new classes, no overloading, and default storage modes for R objects are used.http://www.csiro.au/science/FunctSNP.html and http://www.csiro.au/science/dbAutoMaker.htmlThe software for dbAutoMaker is implemented in Perl and supported on Linux and MS Windows. Databases and source code are freely available from: HNK conceived this systems genetics project, and developed and obtained the CSIRO research grant for the project. SJG, CG and NSW developed FunctSNP. SJG developed dbAutoMaker. CG and HNK supervised the project. All 4 authors wrote the paper, read and approved the final manuscript.Table - Summary of the number of entries in the customised species-specific SNP databases.Click here for fileTable - FunctSNP SNP prioritisation.Click here for fileFunctSNP commands used in testing program.Click here for file


Plasmodium falciparum. One of the major obstacles to the wider implementation of these tools is the absence of practical methods enabling high throughput analysis. Here a new Zip-code array is described, called FlexiChip, linked to a dedicated software program, which largely overcomes this problem.A number of molecular tools have been developed to monitor the emergence and spread of anti-malarial drug resistance to Previously published microarray probes detecting single-nucleotide polymorphisms (SNP) associated with parasite resistance to anti-malarial drugs  were adapted for a universal microarray FlexiChip format. To evaluate the overall sensitivity of the FlexiChip package (microarray + software), the results of FlexiChip were compared to ResMalChip microarray, using the same extension probes and with the same PCR products. In both cases, sequence results were used as gold standard to calculate sensitivity and specificity. FlexiChip results obtained with a set of field isolates were then compared to those assessed in an independent reference laboratory.The FlexiChip package gave results identical to the ResMalChip results in 92.7% of samples  and had a sensitivity of 95.88% and a specificity of 97.68% compared to the sequencing as the reference method. Moreover the method performed well compared to the results obtained in the reference laboratories, with 99.7% of identical results .P. falciparum drug resistance markers with greater cost effectiveness and the possibility for high throughput analysis. The FlexiChip package is a promising tool for use in poor resource settings of malaria endemic countries.Microarrays could be employed to monitor  Plasmodium falciparum, which is responsible for the vast majority of malaria deaths , i = 1,2The remaining of the base-calling algorithm was then identical for the datasets of both chips. Conditional posterior probabilities P(ω1/x) and P(ω2/x) were computed according to the Bayes theorem: P/"red" (ω2)/rejection (ω0)/weak signal/background (bg) classes according to the following decision rules:Each spot on the array was first classified within one of the "green" (ω1/x) > P(ω2/x) and P(ω1/x) > Cr and IS > IT and bg = FALSE → d(x) = ω1• P(ω2/x) > P(ω1/x) and P(ω2/x) > Cr and IS > IT and bg = FALSE → d(x) = ω2• P(ωi/x)) ≤ Cr, i = 1,2 and IS > IT and bg = FALSE → d(x) = ω0• max(P(ωS > IT and background = TRUE → d(x) = bg• IS ≤ IT → d(x) = weak signal• Ix is the log ratio associated with the spot, d(x) is the decision associated with x, ISR and ISG are respectively the "red" and "green" intensities measured on the spot, and IS = max is the maximum of both intensities for this spot.where A final decision was taken for each SNP on the basis of its three replicate spots as follows: if at least two of the three replicates were belonging to the same class the SNP was associated with this class, otherwise it was declared "indeterminate" and no further interpretation was performed.1 or ω2, allele identification came straight from table Pfdhfr gene from this sample was identified as mutant at position 16. The second scenario refers to SNPs that had only two known alleles but were represented by two different probes on the slide, in order to strengthen the identification process. Then, if one of the probes was classified as "weak signal" or "bg", the other probe result was taken into account. If both probe signals were valid (d(x) = ω1 or d(x) = ω2), the coherence between the probes was checked and in case of conflicting results the SNP was declared "indeterminate". The last scenario refers to the situation where more than two different alleles for a given SNP exist. Thus, three or four different alleles must be discriminated with two colours only. For these particular SNPs, two different probes were designed and the corresponding targets were labelled with two different combinations of Cy3 and Cy5 labelled ddNTP's, as explained in the experimental protocol section. For example, the position 108 on gene Pfdhfr is represented by two probes on the array, RES108 and RES108B. The first probe allows to distinguish between the wild type allele and either mutantA or mutantB. The second probe makes the difference between the mutantA and either wild type or mutantB. In such a case, allele identification was resolved according to the combination of both probe results. If one or both probe signals were classified "weak signal", "bg" or "indeterminate", the SNP was declared "indeterminate", otherwise it was determined according to Table Allele identification was done using a pre-defined table that describes the expected signal for each allele of each SNP  and Multiscreen MAVN45 kit system (Millipore). Sequencing reactions were performed on both strands using internal primers and ABI Prism BigDye Terminator chemistry. Sequencing reactions were run on ABI Prism 3100 Genetic Analyzer (Applied Biosystems) at the Plate-Forme Génomique of Institut Pasteur in Paris, and analysed with Seqscape software v.2.0. (Applied Biosystems).A set of samples was sequenced for P. falciparum isolates from Cambodia were tested blindly in Mahidol Oxford Research Unit according to their own protocols. Briefly five SNPs of Pfmdr1  and one SNP of Pfcrt genes (position 76) were screened with restriction length polymorphism methods [Pfserca/Pfatpase6 gene was sequenced (4068 bp). Results were compared to the four SNPs tested with FlexiChip.Fifty position 6 were sc® Pro software were analysed. They included data from 10520 SNPs corresponding to 263 samples tested for 40 positions on five genes. The best compromise between the number of ambiguity rejections and the number of misclassifications was obtained with an optimal rejection threshold of Cr = 0.2. On the 10520 SNPs data handled by this algorithm, 1396 (13.3%) were classified as "weak signal" and 905 (8.6%) were rejected for ambiguity . Among the 1642 SNPs data which could be compared to the sequence, 218 (13.3%) were classified as "weak signal" and 109 (6.6%) "indeterminate" (inconsistency between replicates or ambiguity). Compared to sequencing, considered the "gold standard", a good agreement was found with 96.63% and 95.74% for sensitivity and specificity respectively.Twenty five gpr files generated by the Axon GenePixr was also 0.2. On 5000 SNPs analysed by the algorithm, 332 (6.6%) were classified as "weak signal" and 222 (4.4%) "indeterminate", i.e. two to three times less than with the ResMalChip array. Among the 1215 SNPs data for which the sequences were available, 28 (2.3%) and 38 (3.1%) were respectively considered as "weak signal" and "indeterminate". Sensitivity and specificity were 95.88% and 97.68% respectively.Six gpr files were analysed corresponding to 5000 SNPs data from 125 samples  tested for 40 positions on five genes. The optimal rejection threshold value Cdhfr, crt, atpase and mdr1 gene. The main discrepancies were observed for the dhps gene.A total of 3,078 SNP data corresponding to 81 samples and 38 positions on five genes were available in both ResMalChip and FlexiChip datasets. Among them, 2195 SNPs data were interpretable  with both techniques. An identical diagnosis was found for 2,034 (92.7%) of the SNP data (kappa coefficient 0.8491 with a standard error of 0.0213). When the results on a gene-by-gene basis were considered  were classified as "weak signal" or "indeterminate". Among the 366 remaining SNP data, results were identical with both techniques in 365 cases .dhfr/dhps (sulphadoxine-pyrimethamine) and pfcrt (chloroquine) mutations, mdr1  and cytochrome b (atovaquone). The microarray method described in this paper enables to implement molecular monitoring on a large scale because of the possibility to automatically analyse and interpret the results.Molecular tools are essential for monitoring emergence and spread of anti-malarial drug resistance and are part of strategies described by the World Wide Anti-malarial Resistance Network (WWARN) consortium ,23. Corr0. Thus, this class would be used to detect mixed infections instead of indeterminate ones. However, this mathematical property of the model could not be fully validated for several reasons. First, in the current study some SNPs showed no polymorphism in the processed samples. Indeed, field samples were sequenced for 20 SNPs out of 40 that were genotyped on the array. Among these 20 sequenced SNPs, only five showed polymorphism, with only one having both alleles in  equal amount. Therefore, any dye bias on the signals measured on FlexiChip cannot be excluded, it would prevent mixed signals to behave as expected by the mathematical model. Second, the gold standard used to compare FlexiChip results with is sequencing. This method may not be the best one in the case of mixed alleles because chromatograms may be difficult to interpret, leading to erroneous sequences. The parameters of the mathematical model are derived on an array-by-array basis in order to adapt to possible technical variabilities between arrays. So they depend also on the proportion of single and mixed infections that are hybridized on the array. As most of the field samples analysed in this study were not polymorphic, the model behaviour in the case of a majority of mixed alleles cannot be predicted. But it is doubtless that it will have to be adapted to match the data distribution in that particular case. Finally, the actual design of FlexiChip makes it non exhaustive, as the use of two colours only for most of the monitored SNPs makes it unable to detect all the mutations The use of two mix combinations for the SNP located at position 108 on the pfdhfr gene led to a good classification rate of 100%. It is clear that extending the concept to the whole set of SNPs would increase the reliability of the base calling process, even in the case of mixed infections. Nevertheless, ResMalChip microarray has already been used in an environment of complex malaria infections like [The aim of this project was to evaluate the flexible microarray under practical conditions using field isolates, in which multiple infections are frequently observed. Without any dye bias on the array, spots associated with mixed alleles should exhibit a "yellow" signal corresponding to a mix between red and green signals. In the framework of the proposed mathematical model, these spots should then fall in the intermediate class ωons like .Combined with the FlexiChip microarray, the software provided a sensitivity and specificity of 95.88% and 97.68% respectively when compared to sequencing as the reference method. Moreover, the method performed well when compared to results obtained in a reference laboratory, with 99.7% concordance (kappa coefficient 0.9923 with a standard error of 0.0523).The proposed package can be useful for epidemiological surveys and can give information on the dynamics of emergence and spread of genetic markers in time and/or in space. However, the method cannot be used as an immediate diagnostic tool for individual samples, because the format requires a high number of samples tested at one time to be cost effective.In contrast to previous methods, FlexiChip is no longer dedicated to a single set of genes and/or organisms. Thanks to its flexibility, integration of new SNPs linked to anti-malarial drug resistance is made simpler and adjunction of species identification is now possible. It is easy to adapt to other loci and in particular for SNP detection of other organisms like HIV or Multi Drug Resistant Tuberculosis strains. Moreover, FlexiChip package is ready for use and adaptable to large scale studies to validate new molecular marker candidates.One of the major obstacles for implementation of molecular monitoring of resistance lies in the absence of practical tools for high throughput analysis. Universal microarrays such as FlexiChip could help to change this, as they are adapted to processing of numerous samples and easily adaptable to new markers. Furthermore, they are well suited for molecular biology laboratories from endemic countries, which need a robust and simple tool that could be easily adapted to a specific epidemiological situation.The authors declare that they have no competing interests.NS, AC, JYC, OMP, HPB and FA were involved in the conception and design of the microarray, MAD developed the software, NS, NK, OS, SC, PL, CB, MI, AMD, DS managed experimental procedure and performed field and laboratory work, NS, MAD, CR and FA participated in the statistical analysis, NS, MAD, CR, JYC and FA drafted and critically revised the manuscript. All authors read and approved the manuscript.List of cZip Codes spotted on FlexiChip. Table S1: List of cZip Codes spotted on FlexiChip. *: discarded from the analysis (number 49 and 61)Click here for fileScheme of the spotted oligonucleotides and expected hybridization. Table S2: Scheme of the spotted oligonucleotides and expected hybridization. A) Spotting pattern of cZip on FlexiChip . B) Example of a FlexiChip within SNPs associated with parasite resistance to anti-malarial drugs used Click here for file


In single-cell human genome analysis using whole-genome amplified product, a strong amplification bias involving allele dropout and preferential amplification hampers the quality of results. Using an oligonucleotide single nucleotide polymorphism (SNP) array, we systematically examined the nature of this amplification bias, including frequency, degree, and preference for genomic location, and we assessed the effects of this amplification bias on subsequent genotype and chromosomal copy number analyses.We found a large variability in amplification bias among the amplified products obtained by multiple displacement amplification (MDA), and this bias had a severe effect on the genotype and chromosomal copy number analyses. We established optimal experimental conditions for pre-screening for high-quality amplified products, processing array data, and analyzing chromosomal structural alterations. Using this optimized protocol, we successfully detected previously unidentified chromosomal structural alterations in single cells from a lymphoblastoid cell line. These alterations were subsequently confirmed by karyotype analysis. In addition, we successfully obtained reproducible chromosomal copy number profiles of single cells from the cell line with a complex karyotype, indicating the applicability and potential of our optimized workflow.Our results suggest that the quality of amplification products should be critically assessed before using them for genomic analyses. The method of MDA-based whole-genome amplification followed by SNP array analysis described here will be useful for exploring chromosomal alterations in single cells. Single-cell human genome analysis is critically important for basic and medical genetics. Somatic genomic differences are found in both normal cellular differentiation, as seen in the immune system, and in the progression of diseases such as cancer. In addition, genomic differences within neurons of an individual, as seen in aneuploidy, have also been proposed to contribute to neuronal complexity Technical difficulties have hampered genomic studies using single cells because many analytic techniques require considerable amounts of genomic DNA (gDNA). A number of whole-genome amplification (WGA) methods Recently, multiple displacement amplification (MDA)-based WGA with a phi29 DNA polymerase, which yields less-biased and longer (>10 kb) amplified products Here we performed a high-density oligonucleotide SNP array analysis of the MDA-based S-WGA products from a lymphoblastoid cell line (LCL) and a cell line having a complex karyotype. We examined the nature of the amplification bias using genome-wide genotype information, and assessed its effect on the CCN analysis. We also developed a SNP array data analysis approach to discriminate CCN alterations from amplification bias artifacts, which will be useful for exploring the chromosomal alterations in single cells.We performed MDA-based S-WGA in 40 single cells obtained from one LCL Figure 1Among the 40 S-WGA products genotyped, we selected a total of 12 products for the SNP array analysis. We found that the rate of successfully genotyped alleles  on the array was modestly correlated with the concordance rate of heterozygous SNPs by the Taqman assay, if both PA and AB were considered to be successfully genotyped as heterozygous , or located elsewhere. We found that SNPs close to centromeres or telomeres showed significantly lower genotype concordance between non-amplified gDNA and S-WGA products, compared with those located in other genomic regions Figure 4We then examined whether genomic location influences the amplification bias using heterozygous SNPs in the non-amplified gDNA for analysis. In contrast to the homozygous SNPs, we observed that the heterozygous SNPs that showed discordant genotypes between non-amplified gDNA and S-WGA products were dispersed throughout the genome (We then tested whether genome GC content (GC%) influences amplification bias. To this end, heterozygous SNPs in the non-amplified gDNA were used for analysis  for further analysis. We then divided these SNPs into two groups: SNPs showing biased AD of one allele  and other SNPs . We found that a total of 74 SNPs showed a biased AD of one allele, and 80 SNPs showed non-biased AD. We did not observe the preference for genomic location for the SNPs showing the biased AD of one allele (data not shown). However, these SNPs showed statistically higher GC% (40.2%) compared with other SNPs . These results suggest that genome GC% affects both the occurrence of AD and the preference of AD between two alleles to some extent.Accuracy of the CCN analysis, assessed by the standard deviation of the signal log2 ratio, was also strongly dependent on the call rate on the array  4.1 software. The optimal genomic smoothing size in the CCN analysis  is generally dependent on the type of analysis. We needed a genomic smoothing size of 2–3 Mb with about 80% of the call rate on the array to obtain consistent data with non-amplified gDNA .Because the amplification bias was seen, more or less, at every heterozygous SNP . In addiSelecting the S-WGA products showing a concordance rate greater than 60% by Taqman assay of the heterozygous SNPs for the subsequent SNP array analysis generally resulted in call rates of at least 80% Figure 3We found that genomic location of the homozygous SNPs that showed discordant genotypes in the S-WGA products was mainly localized to the regions close to centromeres or telomeres . Whenever possible, all equipment was UV irradiated for 30 min before the experiment began. Single cells were retrieved by mouth-controlled pipetting with a fine hand-drawn microcapillary tube under a stereoscopic microscope.Two protocols (protocol 1 and 2) for S-WGA using a GenomiPhi V2 kit  were provided by the manufacturer. They were similar to the method previously reported Despite extensive efforts, we sometimes observed amplified products from negative control samples . Although they could not be distinguished from amplified products derived from single-cell samples by the electrophoresis or DNA quantification assays, a subsequent Taqman genotyping assay revealed no signals from them at all 23 SNPs (see below). Therefore, we concluded that amplification did not originate from human DNA, but originated from the primers included in the kit, or contamination of bacterial DNA included in the kit. Similar observations were also reported by others In obtaining the S-WGA products in the LCL, either protocol 1 or 2 was used. We did not find considerable differences between the two protocols with regard to the yield , Taqman genotyping assay and SNP array results Figure 2A total of 23 SNPs  per SNP were covered by at least two subjects.In manual determination of the genotypes of the S-WGA products, we arbitrarily defined the following four types of thresholds, based on the fluorescence intensities (FI) of the control samples. The positive allele A threshold was defined as follows: –3 * . Above this threshold, allele A in the S-WGA product was considered to be amplified. The negative allele A threshold was defined as follows: +5* . If the FI of allele A in the S-WGA product was below this threshold, allele A was considered to have dropped out of the amplification. The positive and negative allele B thresholds were determined the same way. Based on these thresholds, genotyping of the heterozygous SNPs in the S-WGA products were classified into 4 categories: AB , PA , AD , or failed . An example of manual determination of genotyping is shown in Array data was deposited in the gene expression omnibus database (accession no. GSE8567). An Affymetrix 50KXba chip, which contains the probe sets for about 59,000 SNPs, was used for SNP typing. The SNP array data of the non-amplified gDNA of the LCL and the WGA product using 10 ng of LCL gDNA, were previously reported CCN analysis was performed using the CNAT4.1 (Affymetrix). Typically, we performed unpaired sample analysis, in which one S-WGA SNP array dataset was used as the sample and eleven SNP array datasets from female subjects were used as reference. Reference samples were obtained in an Asian population used in the HapMap project. Median scaling implemented in the software was used for per-chip normalization.Table S1(0.03 MB XLS)Click here for additional data file.Figure S1Amplification bias in heterozygous SNPs revealed by the Taqman genotyping assay. The relative fluorescence intensities of alleles A and B are shown.(1.47 MB EPS)Click here for additional data file.Figure S2(A) Genomic location of the SNPs showing inconsistent genotypes or no calls in the S-WGA products compared with non-amplified gDNA. Left, homozygous SNPs; right, heterozygous SNPs. Among the 12 profiled LCL SNP array datasets, the 2 with the low quality  were removed from this analysis. Red and green bars on the chromosomes indicate the SNPs showing low (30% or less) and high (70% and above) consistency, respectively. Arrowheads indicate centromeres. Note that SNPs were not located in the p arms of several chromosomes in the Affymetrix 50K Xba SNP array. (B) Statistical analysis of the concordance and signal log2 ratio. Concordance rates of homozygous SNPs, or signal log2 ratio of the SNPs, in the telomeres or centromere were compared with those in the rest of the chromosome. Telomere SNPs included all SNPs within 10Mb of the end. Centromere SNPs included all flanking SNPs within the span 10Mb to the left and right of the centromere. P values by the Student's t-test are given. (C) Genome GC content and genotype concordance of heterozygous SNPs.(2.98 MB EPS)Click here for additional data file.Figure S3Karyotype of the CMK11-5. The CMK lines were established from a patient with Down syndrome, and showed hypo-tetraploidy. The CMK 11-5 was established from the CMK. The composite karyotype of 10 cells of the CMK11-5 was as follows: 83-90 <4n>, XYY, add(X)(p22.1)(4.68 MB EPS)Click here for additional data file.


Bos Taurus genome, the present study is to perform GWAS to identify genes affecting milk production traits using current state-of-the-art SNP genotyping technology, i.e., the Illumina BovineSNP50 BeadChip. In the analyses, the five most commonly evaluated milk production traits are involved, including milk yield (MY), milk fat yield (FY), milk protein yield (PY), milk fat percentage (FP) and milk protein percentage (PP). Estimated breeding values (EBVs) of 2,093 daughters from 14 paternal half-sib families are considered as phenotypes within the framework of a daughter design. Association tests between each trait and the 54K SNPs are achieved via two different analysis approaches, a paternal transmission disequilibrium test (TDT)-based approach (L1-TDT) and a mixed model based regression analysis (MMRA). In total, 105 SNPs were detected to be significantly associated genome-wise with one or multiple milk production traits. Of the 105 SNPs, 38 were commonly detected by both methods, while four and 63 were solely detected by L1-TDT and MMRA, respectively. The majority (86 out of 105) of the significant SNPs is located within the reported QTL regions and some are within or close to the reported candidate genes. In particular, two SNPs, ARS-BFGL-NGS-4939 and BFGL-NGS-118998, are located close to the DGAT1 gene (160bp apart) and within the GHR gene, respectively. Our findings herein not only provide confirmatory evidences for previously findings, but also explore a suite of novel SNPs associated with milk production traits, and thus form a solid basis for eventually unraveling the causal mutations for milk production traits in dairy cattle.Genome-wide association studies (GWAS) based on high throughput SNP genotyping technologies open a broad avenue for exploring genes associated with milk production traits in dairy cattle. Motivated by pinpointing novel quantitative trait nucleotide (QTN) across  Over the last decades, advances in DNA-based marker technology make it possible to identify genome regions  underlying complex traits such as milk yield in dairy cattle. Instead of traditional animal breeding programmes solely relying on phenotype and pedigree information, the incorporation of detected QTL into genetic evaluation provides a great potential to enhance selection accuracies, hence expediting the genetic improvement of animal productivity.http://www.animalgenome.org/QTLdb/cattle.html, May 22, 2010). The limitations of QTL mapping using linkage analysis (LA) and/or linkage disequilibrium (LD) i.e., the DGAT1 gene GHR gene In dairy cattle, since the seminal work on QTL mapping by Georges el al With the advent of genome-wide panels of single nucleotide polymorphisms (SNPs), SNPs have been widely used for the detection and localization of QTL for complex traits in many species Motivated by searching for novel casual variants for milk production traits beyond previous findings via traditional linkage studies, the present study is to perform GWAS to detect potential casual genetic variants for milk production traits, using the Illumina BovineSNP50 BeadChip. The identified SNP loci may be considered as preliminary foundation for further replication studies and eventually unraveling the causal mutations for milk production traits in dairy cattle.The blood samples were collected along with the regular quarantine inspection of the farms, so no ethical approval was required for this study.http://www.cdn.ca). The descriptive statistics of these EBVs for the five traits as well as the average reliabilities of EBVs of the 2,093 daughters are presented in A daughter design was employed in this study. In total 2,093 daughters as well as their 14 corresponding sires were collected to construct the study population. The numbers of daughters of the 14 sires range from 83 to 358 daughters with an average of 150. These daughters were from 15 Holstein cattle farms in Beijing, China, where regular and standard performance testing  has been conducted since 1999. The official up to date estimated breeding values (EBVs) of five milk production traits, including milk yield (MY), fat yield (FY), protein yield (PY), fat percentage (FP), and protein percentage (PP) were used as phenotypes in this study. These EBVs were obtained based on a multiple trait random regression test-day model DNA was extracted from blood sample of the daughters and semen sample of the sires using the routine procedures. DNA was quantified and genotyped using the Illumina BovineSNP50 BeadChip containing 54001 SNPs, which is a multi-sample genotyping panel powered by Illumina's Infinium® II Assay. Features of the Illumina BovineSNP50 BeadChip have been detailed previously To assess the technical reliability of the genotyping panel, a randomly selected DNA sample was genotyped twice and over 99% identity of called genotypes (two mismatches) was obtained. This demonstrates the technically robust feature of the 50K SNP BeadChip panel employed herein.The quality control procedure can be largely split into two categories, including individual exclusion and SNP removal, as follows:Firstly, an individual would be excluded from the analyses if it had more than 10% missing genotypes or its SNP genotypes had a Mendelian error rate above 2%. For the second criterion, for each sire-daughter pair, we randomly choose 10,000 genotyped SNP loci for which both the sire and the daughter are homozygotes. A Mendelian error happens herein if the two homozygotes are different in the context that the maternal genotype is unavailable. Accordingly, if more than 200 out of 10,000 SNP have Mendelian errors, the daughter will be removed from the sample.P value lower than 10−6, or (4) its minor genotype frequency was less than five individuals.Secondly, a SNP would be removed if (1) its call rate was less than 90%, or (2) its minor allele frequency (MAF) was less than 3%, or (3) it was severely depart from Hardy Weinberg Equilibrium (HWE) with a P<10−06), and 1,073 SNPs with minor genotype frequency <5 individuals. Eventually, 40,220 SNPs (74.5%) passed these quality control filters. The distribution of the remaining SNPs after filtering and the average distances between adjacent SNPs on each chromosome are given in After the quality control procedures, 73 daughters with >10% missing genotypes and 205 daughters with Mendelian error rate above 2% were excluded, leading to 1,815 daughters remaining for the association analysis. On the other hand, we removed 1,218 SNPs with <90% genotype call rate, 11,008 SNPs with a MAF <0.03, 482 SNPs with extreme value of HWE statistics , and ije is the residual error. For each SNP, β is estimated via a weighted least squares analysis with the weights equal to 1/ijREL, where ijREL is the reliability of the EBV of daughter j in family i. The association between the SNP and the trait is tested via the F-test.L1-TDT is a TDT-based association procedure y is the vector of EBVs of all daughters, b is the regression coefficient of EBV on SNP genotypes, x is the vector of the SNP genotype indicators which takes values 0, 1 or 2 corresponding to the three genotypes 11, 12 and 22 , a is the vector of the residual polygenetic effects with A is the additive genetic relationship matrix and W is a diagonal matrix with the diagonal elements equal to 1/RELij and b and the corresponding sampling variance df = 1 is constructed to examine whether the SNP is associated with the trait.Similar to the studies of We employed Fortran 95 to code the computing programs for L1-TDT and MMRA and they are available upon request.P value <0.05/N, here N is the number of SNP loci tested in analyses.For both analyses, the Bonferroni method was adopted to adjust for multiple testing from the number of SNP loci detected. We declared a significant SNP at the genome-wise significance level if a raw Confounding due to population stratification has been considered as a major plague to the validity of genetic association studies P values (in terms of −log(p)) of all tested SNPs for the five investigated traits are shown in P values, are given in The profiles of As seen from As presented in As shown in From As given by 2χ statistics generated from the association tests across the SNPs tested show no evidence of overall systematic bias. That is, the observed 2χ statistics of the significant SNPs are above the expected 2χ statistics, which are largely at the adjusted genome-wide significance level. The profiles of the Q-Q plots clearly show that the significant SNPs identified by MMRA are unlikely threaten by potential population stratification.The “Q-Q” plots for the test statistics of MMRA are shown in i.e., family-based (L1-TDT) and population-based (MMRA) approaches, respectively, both of which have been widely employed in GWAS. Comparisons between the two methods have been well conducted by many investigators In this study, we performed a GWA study for five milk production traits using a daughter design in Chinese Holstein population. To our knowledge, this is one of the first GWA studies for milk production traits using the Illumina BovineSNP50 BeadChip. Two statistical methods, L1-TDT and MMRA, were implemented to analyze association between SNPs and phenotypes. These two methods belong to two distinct analytical approaches, DGAT1 geneennewitzet al.DGAT1 gene, and possible epistatic effects arising from them may be an additional source of genetic variation for milk production traits. Indeed, Kaupeet al.CYP11B1 gene located at ∼1.33Mbp has significant effects on MY, PY, FP and PP, and the allele substitution effects of CYP11B1 and DGAT1 together explained more variation in milk production traits than DGAT1 alone. In our study, an apparent feature of our findings is that a large proportion of the significant SNPs (61 out of 105) are located on BTA14. Of the 61 SNPs, 59 are located within the reported QTL regions. In particular, all segments on BTA14 which harbor multiple SNPs for the five traits also harbor the DGAT1 gene, and the four segments for MY, PY, FP and PP also harbor the CYP11B1 gene. Within these segments, 13 SNPs are located very close (within 1Mb) to the DGAT1 gene with the closest one (ARS-BFGL-NGS-4939) only 160bp away from it and 14 SNPs very close to the CYP11B1 gene with the closest one (Hapmap25486-BTC-072553 ) only 8,693bp away from it.BTA14 has been received wide attention by many investigators. Apart from a large number of QTL reported on BAT14 GHR gene, which is also generally accepted as a functional causal gene affecting milk yield and components ABCG2 gene LGB gene In addition to the SNPs on BTA14, most (27 out of 44) of the significant SNPs on other chromosomes are also located within the reported QTL regions. Further, some SNPs are also within or close (within 1Mb) to the reported candidate genes , the SNPs ARS-BFGL-NGS-94706 and ARS-BFGL-NGS-34135 on BTA14 , the SNPs Hapmap30383-BTC-005848, Hapmap30646-BTC-002054, ARS-BFGL-NGS-100480, and UA-IFASA-6329 on BTA14 , the SNP UA-IFASA-6878 on BTA14 , the SNPs Hapmap52798-ss46526455, BFGL-NGS-110563, Hapmap25486-BTC-072553, and Hapmap30086-BTC-002066 on BTA14 (for MY and FP), the SNPs ARS-BFGL-NGS-91705 on BTA1, Hapmap38643-BTA-95454 on BTA3, BFGL-NGS-110018 on BTA5, and Hapmap50053-BTA-61516 on BTA26 (for MY and PY), the SNPs Hapmap30381-BTC-005750 on BTA14 (for FY and FP), and the SNP Hapmap27703-BTC-053907 on BTA14 (for FP and PP). This could be explained by pleiotropic effects of these SNPs on multiple milk production traits, leading to genetic correlations among them and there were similar result in many prior studies e.g., GWAS. Also, we individually perform GWAS for each of five milk production traits. This is the most conventional strategy for current GWAS. However, the five milk production traits considered here are generally regarded as correlated and thus should share common environmental/genetic factors. A multiple traits instead of single trait analysis may be a promising way to take correlations among these traits into consideration. Multivariate analyses have been widely adopted in linkage studies In this study, we performed GWAS in the way of SNP by SNP individually via regressing the observations of a single trait on either the genotypes of a SNP (MMRA) or the allele transmission patterns of a SNP from bulls to corresponding half-sib offspring (L1-TDT). Previous studies have shown that single marker tests provide similar or greater power than haplotype-based approaches In our study, the EBVs of daughters were used as phenotypes for association analysis. Besides EBVs, yield deviation (YD) and de-regressed EBVs of individuals are also commonly used as phenotypic observations in GWAS as well as in LA and LA/LD analyses for milk production traits. Comparison among these three kinds of phenotypes with respect to their influence on QTL mapping In all, the present study revealed 105 genome-wise significant SNPs for milk production traits in Chinese dairy cattle population using two different association analysis approaches (L1-TDT and MMRA). Most of these SNPs (86 out of 105) are located within the previously reported QTL regions, and some within or close to the reported candidate genes. The general consistence of the significant SNPs detected herein with the reported QTL and candidate genes and the agreement of the results of the two analysis approaches present strong support for the outcomes of this study. Our findings herein lay a preliminary foundation for guiding follow-up replication studies, and eventually revealing the causal mutations underlying milk production traits in dairy cattle.


The recent settlement of cattle in West Africa after several waves of migration from remote centres of domestication has imposed dramatic changes in their environmental conditions, in particular through exposure to new pathogens. West African cattle populations thus represent an appealing model to unravel the genome response to adaptation to tropical conditions. The purpose of this study was to identify footprints of adaptive selection at the whole genome level in a newly collected data set comprising 36,320 SNPs genotyped in 9 West African cattle populations.After a detailed analysis of population structure, we performed a scan for SNP differentiation via a previously proposed Bayesian procedure including extensions to improve the detection of loci under selection. Based on these results we identified 53 genomic regions and 42 strong candidate genes. Their physiological functions were mainly related to immune response , nervous system  and skin and hair properties .Trypanosoma(sp). Overall, these results might open the way towards the identification of important variants involved in adaptation to tropical conditions and in particular to resistance to tropical infectious diseases.The main possible underlying selective pressures may be related to climatic conditions but also to the host response to pathogens such as  Bos primigenius was not native to sub-Saharan Africa, West African cattle populations are representative of both shorthorn (Bos taurus brachyceros) and longhorn (Bos taurus longifrons) humpless taurines, humped zebus (Bos indicus) and zebu/taurine hybrid cattle. This early suggested that West African cattle has originated from several successive and recent colonization events , thuFSTij were taken as the median of the posterior distribution.The posterior distributions of the different parameters of interest were estimated via MCMC procedures as previously described  from 10,BFi defined as the ratio of the posterior to the prior odds that locus i is selected:In order to identify outlier loci, we decided to base the decision rule on a Bayes Factor δi is an indicator variable, the prior and posterior probabilities that δi = 1 are easy to compute since they reduce to expectations as shown below:As π(P) is the density of the distribution of P defined above. To make interpretation of the BFi easier, we expressed them in deciban units (dB) ie dBi = 10log10(BFi) so that 10 dB corresponds to an odds ratio of 10, 20 dB to an odds ratio of 100, 30 dB to an odds ratio of 1,000 and so forth. We then applied the Jeffreys' rule [BF using the following scale: "strong evidence" when 10<dBi<15, "very strong evidence" when 15<dBi<20 and "decisive evidence" when dBi>20. Notice that the BF captures the change in the odds in favor of δi = 1 as we move from the prior to the posterior. In that way, it makes this criterion independent of the prior distribution of δi and thus guarantees some robustness.where ys' rule  which quP = 0.1), 500 (P = 0.01) and 50 (P = 0.001) under selection (αi ≠ 0) were simulated under the inference model. To mimic characteristics of our samples, we considered 9 populations with parameters βj equal to the ones estimated on the real data (mean of the corresponding posterior distribution). For each pair of simulated locus/population, we subsequently simulated the ηij parameter by adding to the corresponding βj, a value of γij sampled from a Gaussian distribution ) and a value for αi equal to 0 if the locus was neutral or sampled from a Gaussian distribution ) if the locus was under selection. The frequency xiin the ancestral population (or migrant gene pool) was sampled from a Beta distribution with parameters equal to 0.7. This is equivalent to assume the ancestral population was at equilibrium with 4Nμ = 0.7 where N is the effective population size and μ the mutation rate [xiand ηij we subsequently derived the pij parameter of the binomial distribution from which the simulated observed aij was sampled . Only SNPs displaying MAF>0.01 in at least one simulated West-African taurine and one West-African zebu (identified according to the simulated βj) were conserved which led to the exclusion of few SNPs . To a first approximation, this also allowed the mimicking of the ascertainment bias introduced by the SNP selection criterion. The posterior distributions of the different parameters of interest were then estimated as described above from 1,000 post burn-in samples  and a thinning interval of k = 25. BFi for each simulated loci were then computed (see above) and used to compute the (true) FDR and FNR for a given threshold. To investigate relationships between the coefficient of selection and the magnitude of the locus effect αiwe performed simulations under a simplified version (without mutation) of the Wright-Fisher island model with selection proposed by Beaumont and Balding [FST of 0.15. In total, 5,000 neutral SNPs and 500 SNPs for each investigated coefficient and mode of selection were simulated. Initial allele frequencies were drawn from a Beta distribution with both parameters equal to 0.7. At the end of the process, SNPs displaying a MAF <0.01 in at least 2 populations were discarded. Locus specific FST were then computed following Weir and Cockerham [FST was compared to the one obtained under neutrality (αi = 0) to estimate the average SNP effect.Following Foll and Gaggiotti , four daion rate . Based oockerham . Followiockerham , for eachttp://genome.ucsc.edu/. For closer evolutionary distances, the alignments are created using syntenically filtered BLASTZ alignment chains, resulting in a prediction of the orthologous genes in cow. In total, 46,598 different RefSeq identifiers were anchored to the Btau_4.0 bovine genome assembly http://genome.ucsc.edu/. Considering that most consecutive SNPs on the map were separated by more than 20 kb and the relatively high level of LD at shorter distance (see Results and [http://www.ingenuity.com/). Among the 17,190 different TransMap RefSeq identifiers, 17,151 identifiers (99.7%) were represented in the Ingenuity Pathway Knowledge Base (IPKB) and corresponded to 7,177 different genes further considered as the reference set. Finally, 15,336 SNPs were located within a gene, leading to an average of 2.21 SNPs per annotated gene. Notice that 421 of these SNPs were representative of more than one gene.Because the annotation of the bovine genome is still sparse, the gene content information was derived from the TransMap cross-species alignments available in the UCSC Genome Browser ults and ) a SNP wS is computed based on a right-tailed Fisher exact test for the overrepresentation of candidate genes ). In our study, networks were considered as significant when S>3 and their associated top functions and diseases were further reported.Functional and Networks analyses were carried out using an approach similar to the one described in . Based oBF values were first smoothed over each chromosome with a local variable bandwidth kernel estimator [In order to identify regions under selection (with an unexpectedly high proportions of SNPs subjected to selection), we followed the locally adaptive procedure which allows to account for variations in distance between the different tested positions ,82. Indistimator . We furtThroughout the text, gene symbols are given according to HUGO standard nomenclature.Breed Codes: AUB: Aubrac; BAO: Baoulé; BOR: Borgou; KUR: Kuri; LAG: Lagune; ND1: N'Dama (first population); ND2: N'Dama (second population); OUL: Oulmès Zaer; SOM: Somba; ZCH: Choah zebu; ZFU: White Fulani Zebu; ZMA; Zebu from Madagascar.Other abbreviations: ASD: Allele Sharing Distance; BF: Bayes Factor; BTA: Bos TAurus chromosome; dB: deciban unit; HWE: Hardy-Weinberg Equilibrium; IPA; Ingenuity Pathway Analysis; IPKB: Ingenuity Pathway Knowledge database; FDR: False Discovery Rate; FNR: False Negative Rate; LD: Linkage Disequilibrium; MAF: Minor Allele Frequency; MHC: Major Histocompatibility Complex; PCA: Principal Component Analysis; SNP: Single Nucleotide Polymorphism.All authors read and approved the final manuscript. MG conceived the study, analyzed the data and drafted the paper. LF performed functional and network analyses and drafted the paper. AR, FJ, DL and JLF contributed to analyses. KMG provided samples and IG oversaw genotyping and quality control of genotyping results.Supplementary Figures. This additional file is a pdf document with seven pages each corresponding to the following supplementary figures: Figure S1 (page 1): SNP Polymorphism within each population. A) Plot of heterozygosities averaged across all the 36,320 selected SNPs against the corresponding relative increase after removing non informative SNPs from the full data set B) Distribution of the number of SNPs per population for different MAF range. Figure S2 (page 2): Neighbor-Joining tree relating the 437 individuals. The tree was constructed using allele sharing distances averaged over 36,320 SNPs. Edges are colored according to the individual breed of origin. Figure S3 (page 3): Distribution of allele counts for different FIS, allele frequency and number of genotyped individuals. Figure S4 (page 4): Comparison of the distribution of estimated and simulated allele frequencies in the gene pool in four simulated data sets. P represents the proportion of SNP under selection simulated and Alpha the locus effect. Figure S5 (page 5): Posterior distribution of the proportion P of loci under selection. Distributions for the real data set and the four different simulated data sets (with known P) are represented. Figure S6 (page 6): Distribution of distances separating consecutive SNPs. Figure S7 (page 7): Decay of average pairwise r2 with inter-marker distance for the different populations. A vertical dotted line indicates the average marker spacing of 70 kb in the study.Click here for fileSupplementary Tables. This additional file is an excel file containing 7 sheets each corresponding to the following supplementary tables: Table S1: Differentiation between populations. Estimates of FST between pairs of population computed with SMARTPCA (GENEPOP) software are below (above) the diagonal. Diagonal elements are estimates of population FIS. Standard deviations of the estimates are given in parenthesis. Table S2: Power to detect loci under selection as a function of the simulated locus effect. Table S3: BF and FST estimates for each SNP considered in the study. Table S4: Correlation of BF and FST between pair of SNPs for different inter-marker distance bins. Table S5: Description of the 7,177 genes represented by analyzed SNPs. Table S6: Description of significant gene networks. Table S7: Functional annotation of N and N3 networks. Table S8: Genome Coverage of the SNPs.Click here for fileGenotyping Data. This file is a bzipped archive containing the genotyping data  in the Haploview format (see Methods).Click here for file