Data from the 1000 genomes project (1KGP) and Complete Genomics (CG) have dramatically increased the numbers of known genetic variants and challenge several assumptions about the reference genome and its uses in both clinical and research settings. Specifically, 34% of published array-based GWAS studies for a variety of diseases utilize probes that overlap unanticipated single nucleotide polymorphisms (SNPs), indels, or structural variants. Linkage disequilibrium (LD) block length depends on the numbers of markers used, and the mean LD block size decreases from 16 kb to 7 kb,when HapMap-based calculations are compared to blocks computed from1KGP data. Additionally, when 1KGP and CG variants are compared, 19% of the single nucleotide variants (SNVs) reported from common genomes are unique to one dataset; likely a result of differences in data collection methodology, alignment of reads to the reference genome, and variant-calling algorithms. Together these observations indicate that current research resources and informatics methods do not adequately account for the high level of variation that already exists in the human population and significant efforts are needed to create resources that can accurately assess personal genomics for health, disease, and predict treatment outcomes. A primary goal of the human genome project was to produce a high quality DNA sequence that could serve as a common reference for understanding the genetic basis of health and disease. The reference sequence has been a guiding principle for the development of a vast array of reagents, arrays, genotyping assays, computational tools, and clinical resources. Moreover, the reference sequence is the foundation for databases and bioinformatics algorithms that are used to define target regions for resequencing, perform genome wide association studies, or measure inter-species conservation. Thus, the reference sequence has become essential for clinical applications, and is used to determine alleles for risk, protection, or treatment-specific response in human disease Because so much work is currently based on the concept of a standardized reference sequence, we have evaluated the extent to which our growing knowledge of human genome variation should alter this paradigm. New data emerging from the 1000 Genomes Project (1KGP) Microarrays have been one of the most utilized tools in genetic research and are the basic platform for Genome-Wide Association Studies (GWAS). These arrays contain millions of DNA probes that are used to determine the genotypes of polymorphic loci. The optimal genotyping capacity is predicated on two basic assumptions concerning genomic sequences. First, the genotyped samples are completely identical at all locations within the probe, except for the targeted SNP. Second, the SNP must be one of the two variants for which the array was designed as variants are assumed to be biallelic. If either of these two conditions are violated, then the microarray probe will not function as well, or at all, and lead to false negative or false positive results for some individuals Using the 1KGP dataset (25 million single nucleotide polymorphisms (SNPs) from 629 individuals), we evaluated the extent to which these assumptions are violated for each of four commonly used microarray platforms, the Illumina Human Omni1M-quad, Illumina Omni 2.5 M, Affymetrix 6.0, and the Affymetrix Axiom CEU array, which takes into account new knowledge concerning variation within the genome Next, we examined polyallelic SNPs, where there are more than 2 possible alleles at a locus. The 1KGP data contained 496 loci over 629 genomes, whereas the Complete Genomics (CG) data contained 61,153 loci in just 69 genomes, representing a 123-fold increase in detection. Polyallelic SNPs are more difficult to investigate because many variant calling pipelines are biased toward biallelic SNPs We then compared the “heterozygous, dual non-reference” SNP locations to lists of the probe bases on the standard microarrays and found thousands of instances where the microarray probe assumed a biallelic context but is instead polyallelic . This prThe impact of the above issues is that they can confound GWAS studies due to false homozygous or negative calls. Hence, we next sought to examine which of these problematic probes appear in published GWAS studies. 34% of the studies  have significant hits with a microarray probe affected by neighboring un-probed SNPs, SVs, indels or polyallelic SNPs . Though Widely used bioinformatics tools for examining the effect of a specific mutation on a protein’s structure, such as PolyPhen We utilized the high-depth CG genomes to evaluate the extent to which there are multiple SNPs within an exon or the full coding regions of a gene. For each individual, there is an average of 6,077 genes (sd = 570) having multiple SNPs and an average of 3,320 (sd = 341) individual exons having multiple SNPs. The vast majority of exons with multiple SNPs only have two SNPs, but there are some exons with a larger number of SNPs. BRCA1, one of the most well studied breast cancer genes, contains numerous genetic variants. In the CG data 36 of 69 individuals have multiple (2–5) non-synonymous variants within their BRCA1 gene.Haplotype blocks can, in theory, reduce genotyping measurements by using one SNP to “tag” other SNPs To further determine the extent to which both the number of SNPs and the number of individuals analyzed affected LD calculations, we separately selected random samples of different percentages of individuals and SNPs from the 1KGP data for chromosome 20. As the number of SNPs increased , the average size of an LD block decreased from 16 kb (sd = 26 kb) to 5.4 kb (sd = 13 kb) . Based uWhile it is understood that increasing variation will decrease LD block size, the impact of increased variation has not been documented. This decreased LD has strong implications for the diagnostic genotyping of these genes and association studies An additional measure used for studying population variation is the presence of runs of homozygosity (ROH). Individuals from a homogenous populations share similar chromosomal segments, and therefore, many contiguous loci across the individuals’ genomes would be homozygous. Conversely, individuals from admixed populations show less homozygosity in their genome. This homozygosity is investigated using SNP data and a run of homozygous (ROH) SNPs The variant calls produced by 1KGP and CG represent two fundamentally different approaches to interrogating the variation across human genomes. The 1KGP project aims to sequence 2,500 low-coverage genomes, and the release we examined contained genotypes for 629 genomes. In contrast, the CG set contains 69 high-depth fully sequenced genomes. Since these approaches are so distinct, it is worth comparing the overlap of SNP calls between the two sets . While aThrough efforts like the 1KGP and CG public data releases, we are getting a new view that human variation much more extensive than previously thought. These data also expose several shortcomings of current microarray tools and alter the view of some basic tenets of the allelic variance of the human genome.While genotyping arrays have been a main tool in genetic studies for almost two decades, only now can we observe that current arrays will do an imperfect job measuring variation in randomized populations due to the greater than anticipated extent of probe-affecting variation. The regions near known SNPs have been assumed to be largely free of indels, SNPs, and SVs, and also biallelic, but all of these assumptions are incorrect for a large fraction of probes used in most genotyping platforms. The largest percentage of the probes is affected by structural variants, which cover a substantial percentage of the genome, ranging from insertions and deletions to large-scale tandem repeats and copy number variants. When a probe matches one of these regions, the actual location that is interrogated in the genome is ambiguous. In individuals lacking the SV, the reference location is interrogated, while in individuals with the SV, the variant location is interrogated. This inconsistency will produce variable results between individuals. We acknowledge that neither 1KGP data nor arrays are perfect and therefore the exact list of probes that are found to be potentially problematic will always be a moving target. Nevertheless, the overall counts and distribution of problematic probes would be highly similar if the 1KGP data were error-free.As the number of questionable probes increases with the number of SNPs identified, it is doubtful that a “perfect” microarray or set of “population-specific” microarrays could be constructed based on the variation found in 1KGP, because only a small fraction of the variability of the human genome will be found. Even when the 1KGP is done, it will only have sampled 2,500 people, representing a very small percentage of people on the planet. Hence, sequencing will likely always be a more robust method to assess known and unknown variation in genomes.Similar to shortcomings in large-scale microarray-based genotyping, functional predictions based on simple categorization of variants as synonymous or non-synonymous is limited. When multiple SNPs within a gene or an exon exist, the variants can act together Other assumptions about human genetics, developed through previous scans of genomic variation, are also being re-evaluated as we collect more data. The average LD length, often used to tag SNPs and impute variation, decreases in size with increasing numbers of variants. As LD length distributions are Poisson, the number of blocks that are shorter than the average increases significantly with increasing numbers of variants. Similarly, bioinformatics programs and analyses that use assumptions based on HapMap Clearly, data collection and analysis are integrally connected. This work demonstrates that the best approaches for assessing global variation in the human population at both the data collection and analysis phases are at an early stage. Methods are still being developed and the best way to make global measurements is under debate. As evidenced by the 1KGP and CG data sets, each set contains similar numbers of SNPs , yet the two sets are clearly different in the numbers of genomes represented and average base coverage per genome. Hence, it is worthwhile considering the economic costs and benefits of the deep individual or shallow population approaches. The 1KGP approach, using low coverage sequencing on many genomes, identified more SNPs and is on course to identify many more SNPs when the expected 2,500 genomes from a broad spectrum of populations are completed. The high coverage CG approach on a few genomes has produced fewer SNPs but more detailed information for each genome. For example, thousands of polyallelic SNPs were identified within the CG data set, but were almost completely missed by 1KGP. One reason for this is that the low coverage per-genome of the 1KGP dataset requires pooled genotyping, which tends to bias against rare or singleton variations. Additional factors for this disparity include annotation techniques. The 1KGP variant calling pipeline discards tri- and tetraallelic variants as errors, because alleles are either assumed to be biallelic or simply do not occur with high enough frequency in pooled data.The current human population is over seven billion and growing. New mutations have been accumulating for over 5000 generations at the rate of between one and 100 mutations per generation de novo.Moreover, in applications like tumor profiling, gene expression, or other functional genomics assays, a single reference sequence can be problematic. For a cancer genome, the best reference genome to which tumor data should be aligned is a matched normal genome of patient. This is the only way to be confident that driver mutations or rearrangements are novel in the tumor and not present in normal cells. In the case of RNA assays, cDNA reads should be mapped to the samples’ genomes from which the RNA was isolated. Some regions with known high variability, like the MHC, already have alternative assemblies because a single reference sequence causes too many mapping biases. Other quantitative assays  likely suffer from similar issues within individual samples but have not been systematically studied due to the high cost of creating individualized genome sequences Nonetheless, a reference genome sequence is clearly needed for research. Without a point of reference and common coordinate, or naming system, research and clinical assay results cannot be reported in ways that allow for inter-lab comparisons and independent validation of research results. There are many important questions yet to be addressed as to how to best approach developing a universal reference sequence and establish best practices for using it. Addressing population and individual variability in a universal reference requires that we think about the genome, not as a single sequence, but rather as a union of differences. A basic coordinate system needs to be developed that can accommodate any indel and rearrangements, and analytical tools need to assume higher levels of differences than they do now. To begin addressing these issues, we need to have a much greater number of de novo assembled genomes from both evolutionarily distant and closely related individuals and improved methods for variant calling. Fortunately, much work is ongoing on both frontsftp://ftp-trace.ncbi.nih.gov/1000genomes/ftp/release/20100804/ALL.2of4intersection.20100804.genotypes.vcf.gzThe combined pilot data from the 1000 Genomes Project: Release 23 of HapMap containing variant calls for 90 CEU individuals based on human reference assembly hg18.ftp://ftp2.completegenomics.com.The Complete Genomics data consisted of the 69 publicly available genomes that were released in April 2011 and were downloaded from: ftp://hgdownload.cse.ucsc.edu/goldenPath/hg19/database/gwasCatalog.txt.gz.The list of GWAS studies from the UCSC browser ftp://ftp.1000genomes.ebi.ac.uk/vol1/ftp/release/20100804/README.20100804_genotypes_and_imputation. While this data from the 1KGP project is not the most current data, it is the only published data that is allowed for publishable analysis according to the Fort Lauderdale data release policies The 1KGP release includes data from 629 individuals and includes the variants identified by two of the four pipelines utilized by the 1KGP. Because of the variability between different software packages and a concern for false positives, the results of the four pipelines were merged to create a file including any call made by at least two of the pipelines. Further explanation of this process can be found at: For the GC data, only called SNP genotypes were used, and no-call loci were ignored. All sequence data were aligned to hg19. The RefSeq genes release 37.1 was used for the determination of coding regions and the Complete Genomics annotations were used to identify of non-synonymous changes.For the comparison of SNPs between the CG and 1KGP datasets, we took all of the hg19 coordinates for each genome, and then included a base-pair +1 and −1 for that location. This addition allowed for the potential single-base slippage The Linkage Disequilibrium (LD) analysis was performed with a combination of PLINK http://genome.sph.umich.edu/wiki/LiftOver). Next, the haplotype blocks were determined using Golden Helix SVS package version 7.4  and the default settings for each of the two datasets for each gene. The “Haplotype Blocks” tables that were generated were then summarized into the reported results. The plots of LD were generated using the Plot Linkage Disequilibrium command with default settings. The Runs of Homozygosity (ROH) were determined using Golden Helix and the default parameters.For the gene-specific analysis of BRCA1 and JAK2, the HapMap data were converted from hg18 to hg19 using the LiftMap tool Click here for additional data file.Table S2A list of all microarray probes from the Illumina and Affymetrix platforms where we have identified a problem.(TXT)Click here for additional data file.


The use of molecular markers has revolutionized the pace and precision of plant genetic analysis which in turn facilitated the implementation of molecular breeding of crops. The last three decades have seen tremendous advances in the evolution of marker systems and the respective detection platforms. Markers based on single nucleotide polymorphisms (SNPs) have rapidly gained the center stage of molecular genetics during the recent years due to their abundance in the genomes and their amenability for high-throughput detection formats and platforms. Computational approaches dominate SNP discovery methods due to the ever-increasing sequence information in public databases; however, complex genomes pose special challenges in the identification of informative SNPs warranting alternative strategies in those crops. Many genotyping platforms and chemistries have become available making the use of SNPs even more attractive and efficient. This paper provides a review of historical and current efforts in the development, validation, and application of SNP markers in QTL/gene discovery and plant breeding by discussing key experimental strategies and cases exemplifying their impact. Allelic variations within a genome of the same species can be classified into three major groups that include differences in the number of tandem repeats at a particular locus  , segmentin silico SNP discovery through the mining of SNPs within EST databases followed by PCR-based validation [in silico and experimental mining of EST databases resulted in the discovery of a large number of nonallelic SNPs that represented paralogous sequences and were suboptimal for application in molecular breeding [While SNP discovery in crops with simple genomes is a relatively straightforward process, complex genomes pose serious obstacles for the researchers interested in developing SNPs. One of the major problems is the highly repetitive nature of the plant genomes . Prior tlidation . Althouglidation . As manylidation , in silibreeding . Recent breeding . Transcrbreeding . This mebreeding , canola breeding , eucalypbreeding , sugarcabreeding , tree spbreeding , wheat [breeding , avocadobreeding , and blabreeding . Originabreeding  brought breeding . This teDespite the attractiveness of SNP discovery via transcriptome or exome resequencing, this process is targeted, focusing solely on coding regions. It is obvious that the availability of SNPs within coding sequences is a very powerful tool for molecular geneticists to detect a causative mutation . HoweverAlthough several complexity reduction approaches are being developed to generate data from NGS platforms, it is often challenging to identify candidate SNPs in polyploid crops species such as potato, tobacco, cotton, canola, and wheat. In general, minor allele frequency could be used as a measure to identify candidate SNPs in diploid species . HoweverApeKI, is applied to digest DNA prior to sequencing to generate reduced representation libraries (genome complexity reduction component), which are further subject to sequencing [in silico mapping of the sequenced fragments to the genome. Although GBS has the potential to discover several million SNPs, one of the major drawbacks of this technique is large numbers of missing data. To solve this problem, computational biologists developed data imputation models such as BEAGLE v3.0.2 [Although CRoPS and RAD technologies are powerful tools to detect SNPs in genome-wide fashion, they can hardly be called HTP, because on an average only ~1,000 SNPs pass stringent quality control . While tquencing . In polyE v3.0.2  and IMPUE v3.0.2 , to brinE v3.0.2 , 51.The availability of reference sequence and sophisticated software does not always guarantee that the discovered SNP can be converted into a valid marker. In order to insure that the discovered SNP is a Mendelian locus, it has to be validated. The validation of a marker is the process of designing an assay based on the discovered polymorphism and then genotyping a panel of diverse germplasm and segregating population. Compared to the collection of unrelated lines, a segregating population is more informative as a validation panel because it allows the inspection of the discriminatory ability and segregation patterns of a marker which helps the researcher to understand whether it is a Mendelian locus or a duplicated/repetitive sequence that escaped the software filter .http://www.kbioscience.co.uk). These modern genotyping assays and platforms differ from each other in their chemistry, cost, and throughput of samples to genotype and number of SNPs to validate. The choice of chemistry and genotyping platform depends on many factors that include the length of SNP context sequence, overall number of SNPs to genotype, and finally the funds available to the researcher, because most of these chemistries still remain cost intensive. Comparative analyses of these four genotyping assays and platforms were described in Kumpatla et al. [The most popular HTP assays/chemistries and genotyping platforms that are currently being used for SNP validation are Illumina's BeadArray technology-based Golden Gate (GG)  and Infia et al. .Gossypium hirsutum (AD1) and G. barbadense (AD2), consist of two subgenomes A and D, where A genome was derived from diploid progenitors, such as G. herbaceum (A1) and G. arboreum (A2), and D genome resulted from another diploid progenitor G. raimondii (D5). Simple, or true SNPs are markers that detect allelic variation between homologous loci of the same subgenome of two tetraploid samples. For example, in G. hirsutum (AD1) and G. barbadense (AD2) and separates samples into homozygous A (blue) and B (red) clusters. This marker does not discriminate polymorphism in D subgenome, because the D genome allele is absent there (pink dot in G. raimondii). In contrast to simple SNPs, hemi-SNPs detect allelic variation in the homozygous state in one sample and the heterozygous state in the other sample. In G. hirsutum (heterozygous green cluster) and one allele A in G. barbadense (a homozygous blue cluster) and could be vice versa. Homoeo-SNPs detect homoeologous and possibly paralogous loci both in A and D subgenomes and result in monomorphic loci in tetraploid species (right image). In G. herbaceum and G. arboreum) had allele A (blue) and D genome progenitor (G. raimondii) had allele B (red), but both tetraploid species (G. hirsutum and G. barbadense) were grouped into heterozygous AB (green) cluster. As homoeo-SNPs can detect paralogous loci, the diploid progenitors both have different alleles.Though all genotyping chemistries and platforms are applicable to generate genotypic data in polyploid crops, analysis of SNP calls is somewhat challenging in polyploids due to multiallele combinations in the genotypes. SNPs in polyploid species can be broadly classified as simple SNPs, hemi-SNPs, and homoeo-SNPs. Here, we describe simple, hemi-, and homoeo-SNPs using an example of allele calls in tetraploid and diploid cotton species . Genomes2, RIL, and DH populations. Homoeo-SNPs are of lesser value for mapping purposes as most of the genotypes result in heterologous loci due to polymorphism between the homoeologous genomes or duplicated loci within each of the polyploid genotypes [Simple SNPs as well as hemi-SNPs are useful markers for genetic mapping and diversity screening studies. Simple SNPs segregate like the markers in diploids in most of the mapping populations and would account for approximately 10–30% of total polymorphic SNPs in various polyploid crop species. Hemi-SNPs form a major category (30–60%) of polymorphic SNPs in a polyploid crop species and could be used for genetic mapping purposes in Fenotypes .Genetic mapping studies involve genetic linkage analysis, which is based on the concept of genetic recombination during meiosis . This enkgw3a (GS3) and kgw5 (GW5/qSW5). Using the SNP bin map, GW5/qSW5 QTL for grain width was accurately narrowed down to a 123 kb region as compared to the 12.4 Mb region based on the RFLP/SSR genetic map. Likewise, GS3 QTL for grain length was mapped to a 197 kb interval in comparison to 6 Mb region with the RFLP/SSR genetic map. Beside the power and the resolution, maps based on high-density SNP markers are also highly suitable for fine mapping and cloning of QTL and at times SNPs on these maps are also functionally associated with the natural variation in the trait. In another QTL mapping project, SNP and InDel markers were used to fine map qSH1 gene, a major QTL of seed shattering trait in rice [4F2 and BC3F2 populations in fine mapping approach with SNP and InDel markers, the authors mapped the functional natural variation to a 612 bp interval between the QTL flanking markers and discovered only one SNP. They further showed that this SNP in the 5′ regulatory region of the qSH1 gene caused loss of seed shattering. Fine mapping approach was also taken to positionally clone the rice bacterial blight resistance gene xa5, by isolating the recombination breakpoints to a pair of SNPs followed by sequencing of the corresponding 5 kb region [Piz and Piz-t rice blast resistance genes and developing PCR-based SNP markers [Piz and Piz-t (averaging one SNP every 248 bp), while InDels were much lower. This dense distribution of SNPs helped in developing SNP markers in the vicinity of these genes. Advancements in rice genomics have led to mapping and cloning of several genes and QTL controlling agronomically important traits, enabled routine use of SNP markers for MAS, gene pyramiding, and marker-assisted breeding (MAB) [A recent study on QTL analysis in rice for yield and three-yield-component traits, number of tillers per plant, number of grains per panicle, and grain weight compared a SNP-based map to that of a previous RFLP/SSR-based QTL map generated using the same mapping population . Using t in rice . The QTLb region –64. SNP b region . A study markers . The autng (MAB) –68.qHO6) affecting maize seed oil and oleic acid contents. This QTL encodes an acyl-CoA:diacylglycerol acyltransferase (DGAT1-2), which catalyzes the final step of oil synthesis [SNP markers have facilitated the dissection of complex traits such as flowering time in maize. Using a set of 5000 RILs, which represent the nested association mapping (NAM) population and genotyping with 1,200 SNP markers, the authors discovered that the genetic architecture of flowering time is controlled by small additive QTL rather than a single large-effect QTL . The samynthesis .Lr34/Yr18/Pm38 locus that confers resistance to leaf rust, stripe rust, and powdery mildew diseases [Fhb1 [Fhb1 [Fhb1 gene. MAS in wheat has been extensively applied for simple traits that are difficult to score [Recent advances in wheat genomics have led to the implementation of high-density SNP genotyping in wheat –75. Genediseases . These mes [Fhb1 . Recentlb1 [Fhb1 . These nto score . Rag1, fine mapping was done to accurately position the gene, which was previously mapped to a 12 cM interval [Rag2, originally mapped to a 10 cM interval, was fine mapped to a 54 kb interval using SNP markers that were developed by resequencing of target intervals and sequence-tagged sites [In order to improve the effectiveness of MAS and clone soybean aphid resistance gene, interval . The auted sites . In anoted sites . In all fad2 and fad3 gene, single nucleotide mutations were identified by sequencing the genomic clones of these genes and subsequently SNP markers were developed [fad2 and fad3 alleles in marker-assisted trait introgression and breeding. In barley, SNP markers were identified that were linked to a covered smut resistance gene, Ruh.7H, by using high-resolution melting (HRM) technique [Beet necrotic yellow vein virus resistance genes, Rz4 [Rz5 [VTC2 gene in Arabidopsis [In a study conducted in canola to map the eveloped . Allele-echnique . In suganes, Rz4  and Rz5 Rz4 [Rz5 . A conseRz4 [Rz5 . In one bidopsis . The autGWAS is increasingly becoming a popular tool for dissecting complex traits in plants –92. The fad2, at ~2 kb from one of the associated markers, and this was considered a likely causative gene. With the discovery of millions of SNPs in maize and the availability of tools such as NAM populations, GWAS was effectively applied to dissect the genetic architecture of leaf traits and it was also shown that variations at the liguleless genes contributed to more upright leaf phenotype [allene oxide synthase 2 (StAOS2) gene locus were associated with the largest effect on the trait of interest. A GWAS approach was also successfully applied to understand the genetic architecture of complex diseases such as northern and southern corn leaf blights [Brassica napus and associated them with glucosinolate content variation in seeds. Due to the precision of this method, scientists were able to correlate specific deletions in canola genome with two QTL controlling the trait. Annotation of deleted regions revealed the orthologs of the transcription factor HAG1, which controlled aliphatic glucosinolate biosynthesis in A. thaliana. This research work gives an optimism on successful application of GWAS in polyploid crops.The successful use and first time demonstration of the power of GWAS was through the identification of a putative gene associated with a QTL in maize . In thathenotype . Utilityhenotype . Using thenotype . Two SNP blights , 98. Alt blights . Harper  blights  leverageDue to the availability of HTP SNP detection and validation technologies, the development of SNP markers becomes a routine process, especially in crops with reference genome. How has that influenced the application of SNP markers in plant breeding? In a review article, Xu and Crouch  indicateCurrent MAS strategies fit the breeding programs for the traits that are highly heritable and governed by a single gene or one major QTL that explains a large portion of the phenotypic variability. In reality, most of the agronomic traits such as yield, drought and heat tolerance, nitrogen and water use efficiency, and fiber quality in cotton have complex inheritance that is controlled by multiple QTL with minor effect. Use of one of those minor QTL in MAS will be inefficient because of its negligible effect on phenotype.The MAS scheme using paternity testing has recently been proposed to address challenges associated with selection gains that can be achieved in outbred forage crops . PaterniMeuwissen et al.  describeGS of superior lines can be carried out within any breeding population. In order to enable successful GS, the experimental population must be identified. The population should not be necessarily derived from bi-parental cross but must be representative of selection candidates in the breeding program to which GS will be applied . The expSNP markers have become extremely popular in plant molecular genetics due to their genome-wide abundance and amenability for high- to ultra-high-throughput detection platforms. Unlike earlier marker systems, SNPs made it possible to create saturated, if not, supersaturated genetic maps, thereby enabling genome-wide tracking, fine mapping of target regions, rapid association of markers with a trait, and accelerated cloning of gene/QTL of interest. On the flip side, there are some challenges that need to be addressed or overcome while using SNPs. For example, the biallelic nature of SNPs needs to be compensated by discovering and using a larger number of SNPs to arrive at the same or higher power as that of earlier-generation molecular markers. This could be cost prohibitive depending on the crop and the sequence resources available for that genome. Working with polyploid crops is another challenge where useful SNPs are only a small percentage of the total available polymorphisms. Creative strategies need to be employed to generate a reasonable number of SNPs in those species. The use of SNP markers in MAB programs has been growing at a faster pace and so is the development of technologies and platforms for the discovery and HTP screening of SNPs in many crops. SNP chips are currently available for several crops; however, one disadvantage is that these readily available chips are made based on SNPs discovered from certain genotypes and, therefore, may not be ideal for projects utilizing unrelated genotypes. This necessitates creation of multiple chips or the usage of technologies that permit design flexibility but are economical. Although GBS creates great opportunities to discover a large number of SNPs at lower per sample cost within the genotypes of interest, the lack of adequate computational capabilities such as reliable data imputation algorithms and powerful computers allowing quick processing and the storage of a large amount of sequencing data becomes a major bottleneck. Despite certain disadvantages or challenges, it is clear that SNP markers, in combination with genomics and other next-generation technologies, have been accelerating the pace and gains of plant breeding.


Genetic analysis of these data clustered chickpea accessions based on geographical origin. Genotyping data generated for 671 CKAMs on the reference mapping population (Cicer arietinum ICC 4958 × Cicer reticulatum PI 489777) were compiled with 317 unpublished TOG-SNPs and 396 published markers for developing the genetic map. As a result, a second-generation genetic map comprising 1328 marker loci including novel 625 CKAMs, 314 TOG-SNPs and 389 published marker loci with an average inter-marker distance of 0.59 cM was constructed. Detailed analyses of 1064 mapped loci of this second-generation chickpea genetic map showed a higher degree of synteny with genome of Medicago truncatula, followed by Glycine max, Lotus japonicus and least with Vigna unguiculata. Development of these cost-effective CKAMs for SNP genotyping will be useful not only for genetics research and breeding applications in chickpea, but also for utilizing genome information from other sequenced or model legumes.A set of 2486 single nucleotide polymorphisms (SNPs) were compiled in chickpea using four approaches, namely (i) Solexa/Illumina sequencing (1409), (ii) amplicon sequencing of tentative orthologous genes (TOGs) (604), (iii) mining of expressed sequence tags (ESTs) (286) and (iv) sequencing of candidate genes (187). Conversion of these SNPs to the cost-effective and flexible throughput Competitive Allele Specific PCR (KASPar) assays generated successful assays for 2005 SNPs. These marker assays have been designated as Chickpea KASPar Assay Markers (CKAMs). Screening of 70 genotypes including 58 diverse chickpea accessions and 12 BC Cicer arietinum) is the third most important legume crop, a source of dietary protein and a beneficial agricultural crop in the semi-arid regions of the world. The development of sustainable high yielding varieties against persisting abiotic stresses and biotic stresses is a prerequisite to meet the world hunger. Molecular breeding strategies have been adopted to improvise crop improvement programmes in several crops including legumes such as soybean and common bean  and SNPs are the markers of choice for genetics and plant breeding applications . Althoughttp://www.kbioscience.co.uk) seems to be an attractive marker genotyping assay .Depending on the sample size and number of markers to be analysed, medium- to high-throughput assay platforms such as BeadXpress and GoldenGate assays from Illumina Inc.  with varying set of multiplexes  are available. Such platforms have been developed and used in several crop species such as barley  to develop a second-generation genetic map based on SNPs, and (v) to determine the extent of genetic synteny of chickpea with some closely related legume species.This study has been undertaken in chickpea with the following objectives: (i) to compile a large set of informative SNPs, (ii) to develop KASPar assays for cost-effective SNP genotyping, (iii) to analyse genetic diversity in the selected With an objective of developing the cost-effective KASPar assays for chickpea genetics and breeding applications, 2486 informative SNPs were compiled following four approaches .C. arietinum), and one genotype (PI 489777) of wild species (Cicer reticulatum)  . After aculatum) . By compOn the basis of cluster analysis of 27 259 Sanger expressed sequence tags (ESTs), 9569 unigenes including 2431 contigs and 7138 singletons were identified in an earlier study .Cicer species provided 1893 SNPs in our earlier study  . On the basis of SNP analysis on this data set, a GoldenGate assay was developed for 768 SNPs including 733 SNPs from TOGs and 155 SNPs from other sources. Genotyping of the reference mapping population with this GoldenGate assay integrated a total of 450 SNPs including 429 TOG-SNPs onto the genetic map. On the basis of designable criteria for KASPar assays, a total of 604 TOG-SNPs including 410 mapped and 194 unmapped SNPs were selected .In brief, a set of 2486 SNPs including 1409 SNPs from Solexa/Illumina sequencing, 286 SNPs from mining Sanger ESTs, 187 SNPs from allele-specific sequencing of candidate genes and 604 TOG-SNPs was assembled . It is iC. arietinum) from 11 countries, three accessions from the wild species (C. reticulatum) and 12 BC3F2 lines generated after introgressing a genomic region containing QTLs for several drought tolerance traits from ICC 4958 into JG 11 by using marker-assisted backcrossing approach (unpublished results).The selected set of 2486 SNPs was used for developing KASPar assays . The devin silico (assembled) and alleles called in the KASPar assays for the 2005 validated CKAMs showed 100% consistency. The PIC values for the polymorphic CKAMs varied between 0.02 and 0.50 with an average of 0.12  CKAMs were validated of the 2486; of these, 1341 (66.8%) CKAMs were polymorphic among 58 genotypes, 664 (33.1%) were monomorphic in the genotypes tested, and 481 (19.4%) failed to generate a useful amplification signal . No atte of 0.12 .C. arietinum × C. reticulatum) crosses than in intraspecific (C. arietinum × C. arietinum) crosses. Among interspecific crosses, maximum number of polymorphisms (930 CKAMs) was observed in the reference mapping population (ICC 4958 × PI 489777) followed by crosses segregating for Helicoverpa resistance, that is, ICC 3137 × IG 72953 (620 CKAMs) and ICC 3137 × IG 72933 (276 CKAMs). In the case of the intraspecific crosses, maximum polymorphism was identified between Arerti and ICC 4958 (159 CKAMs), which represent parents of MABC population for improvement of chickpea for drought tolerance. The polymorphism status of CKAMs between different parental combinations is given in Analysis of CKAMs on the parental genotypes of the mapping populations showed higher polymorphisms in interspecific (win V5.0.128 software (C. reticulatum) genotypes (IG 72953 and PI 489777), and the Cluster-II comprised 56 genotypes of C.arietinum species, with an exception of one genotype IG 72933, belonging to C. reticulatum species, that branches off sequentially at the base of the dendrogram closer to the Cluster-I. In the Cluster-II, few landraces and cultivars from India , Ethiopia (Arerti), Mexico (ICC 12037) and Israel (ICC 7571) formed a clear outlying group, with the remaining 48 genotypes clustering into two main groups—the Cluster-IIa and the Cluster-IIb. The Cluster-IIa has 13 genotypes that mainly belong to Afghanistan (2), Chile (1), Ethiopia (1), Iran (4), Portugal (1), Turkey (1), Mexico (1) and former USSR (2). The Cluster-IIb is comprised of 35 genotypes, of which 33 belong exclusively to India, one to Iran and one to Cyprus. Within the Cluster-IIb, ICC 1882 is separated from the rest of the genotypes. Overall, the clustering pattern showed a distinctive grouping of genotypes into separate clusters based on their geographical origin and also based on species background  with ICC 4958 genotype were tested with all 2005 CKAMs to assess the genome recovery of JG 11 parent in the MABC lines. As a result, 108–117 markers showed similarity between the given BC3F2 line and JG 11  to 98%  . These markers seem to be the potential mappable markers in the genomic region transferred from ICC 4958 to JG 11.A set of 12 BCnd JG 11 . In brie3F2_248) . FurtherThe reference mapping population (ICC 4958 × PI 489777) was targeted for integrating CKAMs in the genetic map of chickpea. In this context, a total of 930 CKAMs showed polymorphism between the parental genotypes. The polymorphic CKAMs include 503 Solexa/Illumina SNPs, 377 TOG-SNPs and 50 candidate gene sequencing–based SNPs. As genotyping data were already available on the reference mapping population for all 371 TOG-SNPs via GoldenGate assay, only 118 markers representing all the linkage groups were selected for genotyping via KASPar assays mainly for quality control. Therefore, genotyping data were generated on the reference mapping population for a total of 671 CKAMs . High-quality genotyping data, however were generated for 651 CKAMs . Analysis of genotyping data showed Mendelian segregation ratio for a total of 525 markers, and the remaining 126 (19.3%) markers exhibited segregation distortion  owing tooinMap v 4.0 program (http://cmap.icrisat.ac.in/cmap/sm/cp/hiremath/)  and high-quality genotyping data were generated for 112 TOG-SNPs from this set via KASPar assay in the study, the genotyping data for the remaining 317 TOG-SNPs generated via GoldenGate assay were added to the data set of 651 CKAMs. In addition, genotyping data were also assembled for (i) 61 genic molecular markers (GMMs) including 31 CGMMs, 15 CISRs and 15 ICCeMs (remath/) . DetailsUneven distribution and clustering of markers was observed along the length of all the chickpea linkage groups in this map. Occurrence of both minor (3–5 cM) and major (>5 cM) gaps between adjacent loci was observed . A detaihttp://cmap.icrisat.ac.in/cmap/sm/cp/hiremath/. These comparisons reflect a greater congruency in terms of grouping of markers into specific linkage groups. A few exceptions were also observed. For instance, TA4L-TA199R-3_300 and TA4L-TA191R_291-284 loci were mapped on LG04 by The developed genetic map with 1328 marker loci was compared with the 1291 loci genetic map (Medicago truncatula (Mt 3.5), Lotus japonicus (Lj 2.5 pseudomolecules), soybean (Glycine max) (Glyma1) and the genetic map of cowpea  . Each chLotus . In the Lotus .in silico SNP validation, gene-specific SNP assays, marker saturation in the regions of interest and marker application projects that utilizes defined set/panel of smaller number of SNP markers on varying number of genotypes. In such cases, SNP genotyping technologies such as arrayed primer extension reaction (APEX) (http://www.kbioscience.co.uk/reagents/KASP.html) have been developed. While choosing a particular SNP genotyping platform, several features such as the reproducibility, accuracy, capability of multiplexing, the level of throughput, time consumption and cost (considering both the equipments required and the cost per genotype) need to be considered. As molecular breeding applications, generally, require screening of large populations with a few markers, this study developed cost-effective KASPar marker assays for SNP genotyping in chickpea.Until recently, SSR markers were the commonly used markers for chickpea genetics research and breeding applications . High-thn (APEX) , primer A total of 2486 SNPs were assembled from different sources for developing KASPar assays. KASPar assays developed for chickpea have been referred as CKAMs. Genotyping of these 2486 CKAMs on a panel of 70 genotypes provided a validated set of 2005 CKAMs. This includes KASPar assays for 539 TOG-SNPs that were initially assayed on GoldenGate assays. Conversion of these TOG-SNPs into KASPar assay will facilitate use of TOG-SNPs in chickpea genetics and breeding application.To compare the success rate of converting putative SNPs into successful and informative KASPar assays, amplification and polymorphism statistics were checked across the four sets of SNPs. The set of markers that gave higher rate of failures were those SNPs identified from alignments of Sanger ESTs . The possible reasons could be attributed primarily to (i) SNPs were mined from the ESTs with sequencing artefacts, (ii) frequency of one of two alleles for a given SNP is very low in the EST data set, and (iii) all the genotypes for which EST-based mining approach provided SNPs were not included in the genotype panel used in the current study  analysed. Two accessions of C. reticulatum are resolved as a separate group; however, IG 72933, a C. reticulatum, was found closer to C. arietinum. Similar results were observed in an earlier genetic diversity study using 513 SSR markers in which the IG 72933 genotype showed 40% similarity with the C. arietinum genotypes . Overall, these results are in general congruence with earlier studies and indicate that the cluster topology is reliable.This study demonstrates the suitability of KASPar assays for SNP genotyping for understanding the relationships in the germplasm collection as well as for molecular breeding applications. Despite using a wide diverse collection of genotypes with all 2005 CKAMs, an overall success rate of 81% was achieved. The genetic dissimilarity analysis of the germplasm accessions determines relationships of accessions with each other. The dendrogram developed based on genetic dissimilarity coefficient depicted clear clustering of chickpea accessions into two main clusters as per their geographical origin and species type of all 58 accessions  with > 95% genome recovery of JG 11 in MABC experiments. These lines may be used for multi-location field trials for evaluating agronomic performance as well as for developing the near isogenic lines (NILs) for fine mapping the QTLs.The study also demonstrates the utility of CKAMs for assessing the genome recovery of BCAs expected, the number of polymorphic markers observed between interspecific mapping populations is higher than intraspecific mapping populations. For instance, maximum number of polymorphic markers is 930 (ICC 4958 × PI 489777) in interspecific crosses as compared with 159 (Arerti × ICC 4958) in intraspecific crosses. As ICC 4958 × PI 489777 population is a reference mapping population, genotyping data were generated for the polymorphic CKAMs. Although genotyping data were earlier generated for TOG-SNPs on the mapping population via GoldenGate assays , a set of 118 TOG-SNPs distributed on all eight LGs was also targeted for generating genotyping data via KASPar assays for quality control purpose. Comparison of high-quality data for 112 markers generated via KASPar assay with that of GoldenGate assay showed no discrepancy. After assembling genotyping data for 539 remaining CKAMs, 317 TOGs and 396 marker loci from other sources . Through the comparative analysis, high conservation of synteny was observed between chickpea and Medicago, whereas lowest level of synteny conservation was observed between chickpea and cowpea. Apparently, during the time of analysis genome sequence information was not available for cowpea; hence, the analysis was carried out by comparing with high-density linkage map developed by Medicago and Lotus  matched to the MtChr02 and the remaining 116 were similar to those on other chromosomes. Only 53 loci are in linear order with Mtchr02 chromosomal regions, and the remaining are in nonlinear positions. These findings support the earlier reports by A number of chickpea unique loci matching to different chromosomal regions on Mt 3.5, Glyma1, Lj 2.5 and cowpea genetic map were identified. Of the 69 chickpea unique loci that mapped on 227 regions distributed over eight chromosomes of The study reports compilation of a large number of SNPs and their conversion into cost-effective KASPar assays. A set of 2005 KASPar assays have been developed for accelerating chickpea genetics research and breeding applications. Together with these markers and recently developed SSR markers from genomic libraries (C.arietinum) and PI 489777 (C.reticulatum) was used for genetic mapping.A set of 70 different chickpea genotypes was used for validation of SNPs using KASPar assays. Details of these genotypes are given in Total genomic DNA of all the accessions was extracted from leaves of two-week-old seedlings using high-throughput mini DNA extraction protocol as mentioned in Helicoverpa armigera for a period of 5 days under green house conditions (temperature of 28 ± 5 °C and relative humidity of >65%). After a brief infestation period, leaf samples from both genotypes were harvested for total RNA extraction. Total RNA was also extracted from 22-day-old root tissues of PI 489777, a wild species genotype. Subsequently, the total RNA samples of all the genotypes were sent for Solexa/Illumina sequencing at National Center for Genome Research (NCGR), USA.Five different chickpea genotypes, viz. ICC 4958, ICC 1882, PI 489777, ICC 506 and ICCC 37, which are parents of different mapping populations, were selected for RNA sequencing. Roots of 22-day-old seedlings of ICC 4958 and ICC 1882 were subjected to drought stresses, and subsequently total RNA was extracted from both genotypes  were selected . SubsequThe PIC refers to the value of a marker for detecting polymorphism within a given germplasm, depending on the number of detectable alleles and the distribution of their frequency. In this study, the PIC value of markers was calculated using the following formula  analysis (P < 0.05). All markers were primarily divided into linkage groups using the ‘group’ command of mapmaker/exp 3.0 program (mapmaker were mapped using JoinMap 4 program (http://www.kyazma.nl/index.php/mc.JoinMap/). ‘Kosambi’ mapping function was used to calculate centimorgan (cM) distances. LOD values ranging from 3 to 7 were considered for grouping and mapping. MapChart (2.1v) was used for drawing maps (http://www.biometris.wur.nl/uk/Software/MapChart/).Genotyping data obtained using KASPar assays (CKAMs) were compiled with the marker data for TOGs-SNPs  and selected markers from all 8 linkage groups mapped in earlier studies (M. truncatula (Mt 3.5), L. japonicus (Lj 2.5 pseudomolecules), soybean (Glyma1 genome assembly) and cowpea genetic map (http://comparative-legumes.org/. Hits matching a minimum of 70% sequence identity were retained for comparative study. Identification of homologous blocks was performed using i-ADHoRe v2.1 (Medicago chromosomes are in Mb.Sequences data for mapped chickpea marker loci were queried using BLAST against genomes of


PstI was the best enzyme producing 1.4 million unique reads per animal and initially identifying a total of 63,697 SNPs. After removal of SNPs with call rates of less than 70%, 51,414 SNPs were detected throughout all autosomes with an average distance of 48.1 kb, and 1,143 SNPs on the X chromosome at an average distance of 130.3 kb, as well as 191 on unmapped contigs. If we consider only the SNPs with call rates of 90% and over, we identified 39,751 on autosomes, 850 on the X chromosome and 124 on unmapped contigs. Of these SNPs, 28,843 were not tightly linked to other SNPs. Average marker density per autosome was highly correlated with chromosome size  with higher density in smaller chromosomes. Average SNP call rate was 86.5% for all loci, with 53.0% of the loci having call rates >90% and the average minor allele frequency being 0.212. Average observed heterozygosity ranged from 0.046–0.294 among individuals, and from 0.064–0.197 among breeds, with Brangus showing the highest diversity as expected. GBS technique is novel, flexible, sufficiently high-throughput, and capable of providing acceptable marker density for genomic selection or genome-wide association studies at roughly one third of the cost of currently available genotyping technologies.High-throughput genotyping methods have increased the analytical power to study complex traits but high cost has remained a barrier for large scale use in animal improvement. We have adapted genotyping-by-sequencing (GBS) used in plants for genotyping 47 animals representing 7 taurine and indicine breeds of cattle from the US and Africa. Genomic DNA was digested with different enzymes, ligated to adapters containing one of 48 unique bar codes and sequenced by the Illumina HiSeq 2000.  Beef and dairy cattle industries are major contributors to global agriculture, providing high protein foods for human consumption and many raw materials to industry. Even though cattle production has become more efficient and industrialized, profit margins remain relatively low due to high production costs, diseases and fluctuating market prices. Maximizing resources by investing in genetically superior breeding stock is a viable strategy for increasing production and cattle industry profits The search for major genes controlling production-related quantitative traits in livestock has had limited success, primarily because single gene effects tend to be small and the number of available genetic markers are insufficient for estimating effects accurately Array-based and other SNP genotyping platforms commonly include SNPs that were previously discovered by DNA sequencing. Because these SNPs may not be geographically representative and tend to be at higher frequency than random SNPs, population genetic parameters such as diversity, population subdivision and recombination estimates may be biased In the dairy industry, the use of SNPs for evaluating bulls as commercial semen donors has resulted in better selection at significantly reduced cost ® sequencing platform. The method is suitable for population studies, germplasm characterization, genetic improvement and trait mapping in a variety of diverse organisms. The major advantages over other protocols are both technical simplicity GBS, originally developed for crop plants TM, Promega, Madison, WI) and a plate-format fluorometer . For optimization of the GBS protocol, 200 ng of DNA for ApeKI and 500 ng of DNA for EcoT22I, PstI and EcoT22I/PstI, were digested separately for 2 hours using a ten-fold excess of enzyme and reaction conditions as specified by the enzyme manufacturer . After ligation of appropriate adapters : 6 Holstein, 6 Angus, 3 Hereford and 27 Brangus from the US and 2 African indicine (White Fulani) and 3 African taurine (Muturu) cattle from Nigeria. Samples came from animals slaughtered in commercial meat plants in the US and Nigeria respectively (Table S1). US samples were collected with permission from Leona Meat Plant, Inc. Troy, PA. Nigerian samples were collected from a public abattoir. DNA was extracted using the Genomic DNA-Tissue MiniPrep  and quantified using an intercalating dye (QuantiFluorPstI and adapters were ligated. The adapters comprised a set of 48 different barcode-containing adapters and a “common” adapter. The oligonucleotide sequences of the barcode adapters were:A 48-plex GBS library comprising 47 cattle DNA samples and a negative (no DNA) control were prepared according to Elshire et al. AACGCACATT, GAGCGACAT, CCTTGCCATT, GGTATA, TCTTGG, GGTGT, GGATA, CTAAGCA, ATTAT, GCGCTCA, ACTGCGAT, TTCGTT, ATATAA, TGGCAACAGA, CTCGTCG, GCCTACCT, CACCA, AATTAG, GGAACGA, ACAACT, ACTGCT, CGTGGACAGT, TGGCACAGA, TGCTT, GCAAGCCAT, CGCACCAATT, CTCGCGG, AACTGG, ATGAGCAA, CTTGA, GCGTCCT, ACCAGGA, CCACTCA, TCACGGAAG, TATCA, TAGCCAA, ATATCGCCA, CTCTA, GGTGCACATT; and their respective complements; and the common adapters were:5′ ACACTCTTTCCCTACACGACGCTCTTCCGATCTxxxxTGCA and, 5′-yyyyAGATCGGAAGAGCGTCGTGTAGGGAAAGAGTGT, where “xxxx” and “yyyy” denote the barcodes TGACGCCA, CAGATA, GAAGTG, TAGCGGAT, TATTCGCAT, ATAGAT, CCGAACA, GGAAGACAT, GGCTTA, 5′-CTCGGCATTCCTGCTGAACCGCTCTTCCGATCTTGCA.5′-AGATCGGAAGAGCGGTTCAGCAGGAATGCCGAG and Individual ligations were pooled, and purified using QIAquick PCR Purification Kit . Genomic fragments were then amplified in 50 µL volumes with 10 µL pooled DNA fragments, 1X Taq Master Mix (New England Biolabs), and 12.5 pmol, each, of the following primers:5′-AATGATACGGCGACCACCGAGATCTACACTCTTTCCCTACACGACGCTCTTCCGATCT and5′-CAAGCAGAAGACGGCATACGAGATCGGTCTCGGCATTCCTGCTGAACCGCTCTTCCGATCT.PstI GBS library was purified again as above, and an aliquot was run on the Agilent BioAnalyzer 2100 for evaluation of fragment sizes and the presence of adapter dimers. After quantification on the Nanodrop 2000  the library was sequenced on the Illumina HiSeq 2000 at the Cornell University Life Sciences Core Facility.Temperature cycling consisted of 72°C for 5 min, 98°C for 30 s followed by 18 cycles of 98°C for 30 s, 65°C for 10 s, and 72°C for 30 s, with a final extension step at 72°C for 5 min. The http://www.maizegenetics.net/tassel/docs/TasselPipelineGBS.pdf). To determine copy number and genomic coordinates, sequence tags were aligned to the Bos taurus reference genome  The raw Illumina DNA sequence data (100 nt qseq files) were processed through the GBS analysis pipeline as implemented in TASSEL v3.0  and MEGA 5.05 software PstI library, with the ApeKI in particular, and EcoT22I libraries contained a higher proportion of repetitive DNAs. We found that a 196 bp repetitive DNA fragment that comprised a substantial proportion of the total population of fragments was present in the EcoT22I/PstI double digest. As might be expected from the higher number of predicted genomic restriction sites, fragments produced by ApeKI  tended to be slightly smaller than fragments from six-base cutters PstI (CTGCAG) and EcoT22I (ATGCAT) (figure S1). Based on these results, we chose to sequence GBS libraries derived from PstI genomic digests containing little repetitive DNA.The fragment size distributions of GBS libraries from bovine genomic DNA digested with different restriction enzymes showed that discrete peaks  were present at least to some extent in all libraries (figure S1). The size distribution curve was smoothest for the PstI library showed that all 47 barcoded DNAs were represented, and that on average 1.4 million reads with a barcode and cut-site remnant were produced per animal. From these, 496,417 unique sequence tags containing 63,697 SNPs were identified. The distribution of read numbers and SNP call rates  in individual samples from the PstI library is shown in Sequencing results for the 48-plex PstI-derived SNPs were identified throughout all autosomes, separated by an average distance of 48.1 kb. The X chromosome contained 1,143 SNPs, at an average distance of 130.3 kb, and 191 SNPs were located on unmapped contigs. The average number of reads per individual for the SNPs was 4.59. Only 10,953 SNPs were eliminated because of low coverage . We found 15,339 (29.1%) very tightly linked SNPs, representing multiple SNPs within the same set of 64 nucleotide reads. The distribution of distances between the SNPs not tightly linked showed that 44.0% of them were <50 kb apart and with 13.8% separated by >150 kb , with higher densities of SNPs on the smaller chromosomes . We found that 34.9% of the tags showed counts >10 in at least 1 individual. The distribution of read counts among all individuals showed specific clustering along the chromosomes . These clusters could reflect copy number variations (CNVs), sequencing biases, or a combination of both. To investigate whether GBS data can show the presence of CNVs, we analyzed the counts per animal for all reads (polymorphic or not) in a 0.4 Mb region of chromosome 18 that contains a polymorphic duplication SNPs appear to be largely evenly distributed along the chromosomes , but with higher densities towards the telomeres in most of the chromosomes with the highest density bias found in telomeres on chromosomes 3, 5, 6 and X. However, chromosomes 7, 14 and 25 showed regions other than the telomeres with SNP density biases. In comparison, the markers on the Illumina BovineSNP50 BeadChip as expected were distributed evenly along the chromosomes, except for some density biases on a few autosomes (e.g. chromosomes 7 and 11), as well on the X chromosome.The overall average minor allele frequency (MAF) for the GBS SNPs was 0.212. Brangus showed the highest MAF average of 0.215  and its A neighbor-joining analysis of the relationships among samples showed that animals usually grouped by breed . There wde-novo sequencing methodologies are developed as they are sequenced, they can be used in any population without ascertainment bias An average MAF of 0.272 was reported in three DNA pools representing 15 Holstein lineages, 35 Angus bulls and a mixed population of minor beef breeds , using reduced representation libraries (RRLs) SIGLEC12 gene on chromosome 18 (rs109478645) associated with sire and daughter calving ease, strength, stature, body depth, and rump width Copy number variations (CNVs) have been identified in human and other mammalian genomes and are now considered a source of heritable variation in complex traits PstI is methylation-sensitive possibly resulting in a bias for incorporation of under-methylated regions of the genome, which are also associated with high gene density.We can estimate the total number of polymorphic sites found from the total number of SNPs divided by the total number of tags, which in this case is 2.88 sites per kilo base pairs. Although this estimate should be lower than the actual value since the number of individuals and breeds investigated is small, it is higher than the estimate for the human genome of 2.19 (7 million SNPs in a genome of 3.2 billion bases) Sorghum bicolor ApeKI libraries  has shown that SNP numbers increase with each sequencing run and this effect is especially pronounced during the first few runs  with high quality at a cost of less than $30 per individual with multiplexing of 96 samples per lane. SNP genotyping by using the BovineSNP50 BeadChips surveys a similar number of SNPs  but at >3X higher cost per sample. An additional benefit of GBS is that depending on the budget, the number of SNPs and sequence coverage per SNP locus can be increased by running the same GBS libraries in additional sequencing lanes. For the same cost as chip genotyping, four additional single end sequencing runs can be performed, potentially increasing the number of SNPs with high call rate by an estimated 2.5 fold . This method also has great potential for application in other domestic genomes such as sheep, goat, and buffalo whose reference sequences are either being developed or for which additional breeds are yet to be fully sequenced. Even though the availability of reference genomes is very useful to eliminate repetitive sequences, GBS can be used without a reference genome, by either using consensus sequences of reads as the reference or using the tags simply as dominant markers Supplemental Figure S1Fragment size distribution of the GBS libraries. Fragment size distribution of GBS libraries made with a single DNA sample using three restriction enzymes, separately, and one double digest. Libraries were run on an Agilent BioAnalyzer 2100. The x-axis represents elution time and the y-axis shows fluorescence units. Numbers above hatch marks on the x-axis indicate fragment size in bp. Tall peaks at 15 and 1500 bp are size standards.(PDF)Click here for additional data file.Supplemental Figure S2Distribution of cattle GBS SNPs by chromosome. Location along each of the 30 bovine chromosomes of the SNPs from the GBS (thin line) and the Illumina BovineSNP50 . The thin diagonal, straight line represents a distribution with homogeneous distribution of SNPs along the chromosome. The Y axis is the chromosome location in Mbp and the X axis is the order of the markers.(PDF)Click here for additional data file.Supplemental Table S1Animals used in this study. Breeds and origin of the animals used in this study.(DOCX)Click here for additional data file.


Detection of genomic DNA copy number variations (CNVs) can provide a complete and more comprehensive view of human disease. It is interesting to identify and represent relevant CNVs from a genome-wide data due to high data volume and the complexity of interactions.P-values. Our selected SNP networks are statistically significant compared with random SNP networks and play a role in the biological process. For the unique genes that those SNPs are located in, a gene-gene similarity value is computed using GOSemSim and gene pairs that have similarity values being greater than a threshold are selected to construct gene networks. A gene enrichment analysis show that our gene networks are functionally important.In this paper, we incorporate the DNA copy number variation data derived from SNP arrays into a computational shrunken model and formalize the detection of copy number variations as a case-control classification problem. More than 80% accuracy can be obtained using our classification model and by shrinkage, the number of relevant CNVs to disease can be determined. In order to understand relevant CNVs, we study their corresponding SNPs in the genome and a statistical software PLINK is employed to compute the pair-wise SNP-SNP interactions, and identify SNP networks based on their Experimental results demonstrate that our selected SNP and gene networks based on the selected CNVs contain some functional relationships directly or indirectly to disease study.Two datasets are given to demonstrate the effectiveness of the introduced method. Some statistical and biological analysis show that this shrunken classification model is effective in identifying CNVs from genome-wide data and our proposed framework has a potential to become a useful analysis tool for SNP data sets. Copy number variation (CNV) is defined as a genomic segment range from one kilobase to several megabases in size, in which copy number differences have been observed by comparison of two or more reference genomes ,2. Humanp-value for statistical testing. In addition, Oldridge et al. [The fact that DNA copy number variation is a widespread and common phenomenon among human beings was first discovered ,4 followe et al.  quantitaBesides the above mentioned CNV detection methods, high-density single nucleotide polymorphism (SNP) arrays are becoming more and more popular in CNV detection. The main reason is that the inheritance pattern and linkage disequilibrium of CNVs are similar to those of the SNPs ,7. Zhao Recently, disease classification using copy number variation data has been demonstrated by several groups -22. The P-values. Some statistical analysis are done to illustrate the significance of our selected SNP networks. For the unique genes that those SNPs are located in, a gene-gene similarity value is computed using GOSemSim and gene pairs that has a similarity value being greater than a threshold are selected to construct gene networks. A gene enrichment analysis are done to show that some molecular functions and biological process are significantly associated with our gene networks. The whole framework indicates that our classification model is efficient in high dimensional CNV detection and SNP and gene networks constructed afterwards have relationships directly or indirectly to disease study.When many CNVs are used to detect the association between a disease and multiple marker genotypes, we expect in a typical data set that contains the CNV data of several thousands of CNVs in different individuals, it is common to find only several numbers of copy number positions having genetic patterns that are highly specific to each group of individuals. The CNVs are called the relevant CNVs, as opposed to the irrelevant CNVs that do not help much in identifying the group . Due to the large number of CNVs being irrelevant to each group, two individuals in the same group could have low similarity when measured by a simple similarity function that consider the characteristics of all CNVs. The groups may thus be undetectable by classification algorithms. Here, we are interested in the development of high-dimensional numerical classification algorithm that can identify group of individuals and their relevant CNVs, i.e., detect the association between a disease and multiple marker variations. In this paper, we address this problem by applying a shrunken dissimilarity measure to copy number variation values derived from genome-wide SNP genotyping data. Performance was measured via cross-validation classification accuracy. By shrinkage, the number of relevant CNVs can be determined. In order to understand relevant CNVs, we study their corresponding SNPs in the genome and a statistical software PLINK is employed to compute the pair-wise SNP-SNP interactions, and identify SNP network based on their The outline of this paper is given as follows. In Section 2, we propose the LRR calculation for signal intensities, the shrunken dissimilarity measure to analyze CNV data classification and the logistic model to calculate SNP interactions. In Section 3, we present experimental results on two real CNV data sets derived from genome-wide SNP arrays, and illustrate their corresponding SNP and gene networks for statistical and biological analysis. Finally, we give concluding remarks in Section 4.There are many available tools that can be used to do genotyping call and Log R Ratio (LRR) calculation, such as Birdsuite , CNAG 2, dChip ,26 and GX and Y values for each SNP, representing the experiment-wide normalized signal intensity on alleles A and B, respectively. One additional measurement will then be calculated for each SNP, where R = X + Y refers to the total signal intensity. As for a normalized measure of total signal intensity, the LRR value for each SNP is then calculated asThe normalization procedure produces LRR = log2(observedR/expectedR) (1)expectedR is computed from linear interpolation of canonical genotype clusters [observedR of the two alleles A and B relative to the expected expectedR. For normal diploid genotypes, LRR should fluctuate randomly around zero.where clusters . LRR refIt is well-known that DNA microarray data, which can simultaneously measure the expression level of thousands of different genes, have been successfully used to identify genetic heterogeneity of disease. However, mircoarray data typically has a large number of genes (features) and relatively few samples (observations), meaning that conventional machine learning methods may fail when applied to such data. The Prediction Analysis for Microarrays (PAM), has recently been reported as a potential powerful tool for microarrays analysis, which is based on the technique of nearest shrunken centroid ,30. Nearn samples, p CNVs, and K classes. For ith CNV, the class centroid Suppose there are wherekC denote the indices of the kn samples in class k, and s0 is a positive constant included to prevent the possibility that a CNV with a low LRR level could produce a large ijd. Equation (2) can be rewritten as:Then the shrunken centroid is defined as:t+ = t if t > 0 and zero otherwise. The parameter Δ is usually sought by cross-validation with minimal classification error rate. Note that if k for a given i, then all of the shrunken centroids are zero, and the ith CNV does not contribute to the classification process. For prediction, the class label of test sample t is determined by the nearest shrunken centroid:where wherekπ is the prior probability of class k.As our experimental data sets are both disease-trait samples, it is feasible to test epistasis using PLINK to  detect SY~b0 + b1.A + b2.B + b3.AB + e (5)b3, therefore only considers allelic by allelic epistasis. We focus on symmetrical cases in our study, that means only unique pairs are analysed, for example, if SNP1*SNP2 is performed, SNP2*SNP1 will not be calculated again. The χ2 statistics is applied and the odds ratio for interaction is interpreted in the standard manner: a value of 1.0 indicates no effect.The test for interaction is based on the coefficient of The core study of the Wellcome Trust Case Control Consortium (WTCCC) comprised an analysis of genetic signals from each of seven common human diseases  . Genotyp.01, 1042 markers do not have at least two types of genotypes, 2460 markers have abnormal patterns, so there are 27967 SNP markers have been analyzed to construct canonical clustering positions.The quantile normalized signal intensity data, which were generated from the Affymetrix intensity (‘CEL’) files and used as input to the Chiamo genotype calling program, was downloaded from WTCCC webpage and appropriately prepared according to the requirements of input file formats of PennCNV. A biological literature searching indicates that in chromosome 6, there are some well-known T1D related genes. Therefore, we chose chromosome 6 as an example to demonstrate our method. There are 31470 unique SNP loci in this chromosome, 1 markers have complete genotyping failure with confidence threshold of 0.00% when Δ = 10.56.After a 10-fold cross validation setting, 3154 samples were selected as training data and the remaining 350 samples would be the testing data. The highest classification accuracy  in the 10 trials is 80%. The parameter Δ was tuned to obtain the highest accuracy in the test. Figure Our method can select 63 SNPs, 25 of them are located in gene coding areas. In order to investigate how this small set of SNPs can distinguish case and control people so well, we further discovered the distribution of each SNP in case and control group. Table χ2 statistic and asymptotic P-value will be provided in the output file. When adopted different thresholds, different kinds of SNP networks can be constructed. Here “threshold” means only SNP pairs that have a P-value smaller than this threshold will be considered and be included in the SNP network. By constructing these SNP networks, we can figure out some potential SNP-SNP interactions that are still unknown. Table All the SNPs selected by our method can be divided into two categories, those are located in gene coding areas and those are not. We did a statistical analysis between these two categories of SNPs using PLINK . All pai.2765 × 10–7.To further utilize and benefit from these SNP networks selected by our method, we next performed some statistical analysis. For each network under one particular threshold, we randomly picked the same number of SNPs with our selected SNP network to get a random SNP network, calculated all the pairwise SNP-SNP interactions and got the average value of all the P-values within this random network. Such process were repeated 500 times. Table Our method can select 63 SNPs, 25 of them are located in gene coding areas, and these 25 SNPs belong to 24 unique genes, which are all shown in Table We computed all the pair-wise functional similarities of these 24 gene products using GOSemSim, which is an open source and open development software project for the analysis and comprehension of genomic data running in the platform of R Bioconductor package . GOSemSiWe can see in Table P-value of 0.1 in Figure .43 x 10–5, but rs2734990 is located in intergenic area and do not have a record in Gene Ontology until now, maybe we can make use of rs6912853’s gene information, BTN3A1, to further analyze the inner functions of rs2734990 and extend GO afterward.We found some interesting relationships from SNP and gene networks. For example, for SNPs rs9321142 and rs9371491, which are interacted in the same SNP network under P-value and detail information. Significantly enriched GO terms are marked yellow. The degree of color saturation of each node is positively correlated with the significance of enrichment of the corresponding GO term. The detail information labeled in the enriched GO nodes are organized as “q/m\t/k”, where q is the count of genes associated with the listed GOID (directly or indirectly) in our dataset, m is the count of genes associated with the listed GOID (directly or indirectly) on the chosen platform, k is the total number of genes in our dataset, t is the total number of genes on the chosen platform, p-value is of the significance for the enrichment in the dataset of the listed GOID under hypergeometric distribution. We also extracted all the GO terms and the corresponding involved genes with a significant P < 0.001, see Table In order to investigate the inner functions of the genes involved in our gene network in an ontology level, we performed the GO enrichment analysis by GOEAST , which iHirschsprung , also known as aganglionic megacolon, is a congenital intestinal disease. Patients suffer from different extent of aganglionosis due to the absence of ganglion cells in the gastrointestinal tract. Trisomy 21 (Down syndrome) is the most recurrent structural variations found in HSCR, contributing to 2-10% of the cases.This data set consists of 330 controls and 121 cases of Chinese ethnicity. In this analysis, we excluded the centromeric and telomeric regions using 500kb threshold because these regions tend to have bias in CNV calling. We transformed the probe intensities into LRR values from this SNP data set (n=3369) using PennCNV with the same parameter setting and procedure as mentioned above..44%, when the threshold of Δ was set either between 0.2 – 0.4 or 0.6 – 3.1. We showed all the 6 SNPs selected when Δ is equal to 3.1 in Table After a 10-fold cross validation, 406 samples were selected as the training set and the remaining 45 samples were used as the testing set. Figure As there are only 6 SNPs selected, the number is too small to construct any networks. So for this dataset, we did not do any analysis in SNP level.All the 6 SNPs selected by our method belong to 3 unique genes. The detailed gene information can be seen in Table P < 0.0001, which can provide an inner functions of our selected genes.For the gene network shown in Figure 1011In this paper, we use the method of nearest shrunken centroid for gene expression data, and apply it to tackle copy number variation data determined from genome-wide SNP arrays. The method can be implemented on a personal computer very efficiently. The relevant SNPs are selected for disease data. Experimental results are reported to show the effectiveness of our method. In particular, we find some SNPs that contain in some genes which are relevant to a particular disease. Based on the SNP and gene networks, we can find out some unknown relationships between their corresponding genes, which can be considered as an extension of existing GO knowledge. The existing Gene Ontology enrichment analysis tool also suggests that our selected genes are associated to some molecular function and biological process. In the future, we will study the following problems. Detailed biological analysis of CNVs determined from other genome-wide SNP data sets will be studied. Statistical and association study of selected SNPs can be carried out.The authors declare that they have no competing interests.MN designed this study and developed the new algorithm. YL designed this study, coded the program, ran the experiments and wrote the manuscript. YF designed this study.


In response to hypoglycemia, rCBF was significantly increased in the thalamus, medial prefrontal cortex, and globus pallidum compared to euglycemia for both PASL-MRI and PET methodologies. Both imaging techniques found similar increases in rCBF in the thalamus, medial prefrontal cortex, and globus pallidum in response to hypoglycemia. These brain regions may be involved in the physiologic and symptom responses to hypoglycemia. Compared to PET, PASL-MRI may provide a less invasive, less expensive method for assessing changes in rCBF during hypoglycemia without radiation exposure.Different brain regions sense and modulate the counterregulatory responses that can occur in response to declining plasma glucose levels. The aim of this study was to determine if changes in regional cerebral blood flow (rCBF) during hypoglycemia relative to euglycemia are similar for two imaging modalities–pulsed arterial spin labeling magnetic resonance imaging (PASL-MRI) and positron emission tomography (PET). Nine healthy non-diabetic participants underwent a hyperinsulinemic euglycemic (92±3 mg/dL) – hypoglycemic (53±1 mg/dL) clamp. Counterregulatory hormone levels were collected at each of these glycemic levels and rCBF measurements within the previously described network of hypoglycemia-responsive regions  were obtained using PASL-MRI and [ Hypoglycemia is a devastating problem for people with diabetes and accounts for 6–10% of all deaths of people with Type 1 diabetes 15O] water positron emission tomography (PET) Changes in regional cerebral blood flow (rCBF) in response to hypoglycemia have been assessed using water PET measurements were performed four times at 15 minute intervals. Hypoglycemia was then induced and PET measurements were obtained four times at 15 minute intervals. Two PASL-MRI measurements were obtained at the end of the hypoglycemic clamp. Each participant was moved between the PET and MRI scanners quickly while maintaining hypoglycemic and euglycemic conditions. All scans were obtained after the subject had reached at least 20 minutes of steady state at the desired glycemic level. Every 30 minutes, arterialized venous samples were drawn for analytes  and blood pressures and heart rates were recorded. Throughout the study cardiac function was monitored using an electrocardiogram.Participants were studied in the morning after a 10-hour overnight fast. They remained supine throughout the study. Two intravenous catheters were inserted into arm veins (one for infusion of insulin and dextrose and the other for injection of radioactive isotope) and one intravenous catheter was inserted into a dorsal hand vein that was kept in a ∼55°C plexiglass box . All nine subjects underwent a hyperinsulinemic  euglycemic (92±3 mg/dL [5.1 mmol/L] ∼2 hours) and then hypoglycemic (53±1 mg/dL [3.0 mmol/L]) x∼2 hours) clamp procedure using variable infusions of 20% dextrose based on plasma glucose determinations  every five minutes . During The severity of the hypoglycemic symptoms was determined every 30 minutes during the euglycemic - hypoglycemic clamp with a validated questionnaire Plasma glucose concentrations were measured with a glucose oxidase method . Plasma insulin concentrations were measured with a two site chemiluminescent assay . Counterregulatory hormones including plasma epinephrine and norepinephrine were measured with a single isotope derivative (radioenzymatic) method 1 = 700 ms, TI2 = 1800 ms, flip angle = 90°, 100 mm tag with a 10 mm gap between the tag and the imaging slice, FOV = 256 mm, 4.0×4.0×8.0 mm voxels. A total of 140 PASL-MRI frames (or 70 PASL-MRI pairs) were analyzed for each subject. One high-resolution Tı-weighted magnetization-prepared rapid gradient echo (MPRAGE)  structural scan was also obtained for anatomical region definition and facilitate image alignment.All MRI imaging was performed on a 1.5T Siemens Avanto System  with a 12 channel radiofrequency head coil. To enable computation of rCBF, a total of four proximal inversion with control for off-resonance effects (PICORE Q2) PASL-MRI perfusion scans (3 minutes in duration) were collected with two at the initiation of euglycemia and two more at the end of the hypoglycemic clamp. Scanning parameters were TR = 2500 ms, TE = 13.0 ms, TI15O]water PET acquisition and analysis methods have been previously reported in detail in prior publications 15O]water (1.85 GBq) were injected at 15 minute intervals General details of the water PET A fall in blood glucose level below ∼80 mg/dL induces a rapid counterregulatory response to restore euglycemia. This process involves the activation of peripheral and central glucose-sensing units involved in the physiological control of counterregulation. The mechanisms by which some patients with diabetes develop impaired responses to hypoglycemia remain unclear. A better understanding of these changes and the brain regions involved in the central integration of these responses, might allow targeted therapies aimed at preventing the loss of these responses in HAAF, and allow diabetic patients safer glycemic control. Our findings demonstrate that acute hypoglycemia activates the thalamus, the medial prefrontal cortex and the globus pallidum, as has previously been shown in studies using [a priori identified regions due to correction for multiple comparisons and a factor of multiple thresholding across the entire brain space.That we only found significant increases in rCBF in the thalamus using a voxel wise approach by both ASL and PET, does not mean that the increases in rCBF in the other regions found in the regional analysis are incorrect. A voxel-wise whole brain analysis is less sensitive in detecting changes in Although PASL-MRI has been already used in hypoglycemia studies The most important finding of our work is the consistent increase in rCBF due to hypoglycemia in the same regions across both imaging methods. Our work suggests that there is presence of a cerebral network involved in the hypoglycemic responses and demonstrates the ability to use this novel MRI method in a reliable manner to study these responses.


IL28B gene are associated with response of patients infected with Hepatitis C virus to treatment with pegylated interferon-α +/− ribavirin or with spontaneous clearance of the virus. The results of such studies are so compelling that future treatment approaches are likely to involve clinical decisions being made on the basis of a patient's genotype. Since IL28B is a paralogue of IL28A with greater than 95% sequence identity, it is possible that without genotyping assay specificity, sequences in IL28A may contribute to genotype identification, and potentially confound treatment decisions. This study aimed to 1) examine DNA sequences in IL28B surrounding each of the reported associated SNPs and the corresponding regions in IL28A; and 2) develop a robust assay for rs12979860, the most ‘cosmopolitan’ SNP most strongly associated with treatment response across all global populations studied to date. Bioinformatic analysis of genomic regions surrounding IL28A and IL28B demonstrated that 3 SNPs were unique to IL28B, whereas the remaining 6 SNP regions shared >93% identity between IL28A and IL28B. Using a panel of DNA samples, PCR amplification followed by Sanger sequencing was used to examine IL28B SNPs and the corresponding regions in IL28A. For the overlapping SNPs, all 6 in IL28B were confirmed to be polymorphic whereas the corresponding positions in IL28A were monomorphic. Based upon IL28A and IL28B sequence data, a specific TaqMan® assay was developed for SNP rs12979860 that was 100% concordant to the sequence-derived genotypes. Analysis using a commercial assay identified one discordant result which led to a change in their genotype-calling algorithm. Where future treatment decisions are made upon the results of genotyping assays, it is very important that results are concordant with data from a sequence-based format. This is especially so in situations where designing specific PCR primers is a challenge.Several SNPs located in or around the  IL28B gene were found to be associated with treatment response in patients chronically infected with HCV Worldwide there are over 170 million individuals who are chronically infected with Hepatitis C virus (HCV) IL28B lies in close proximity on chromosome 19 to its paralogue IL28A, a gene with which it shares >95% sequence identity IL28A may interfere with genotype determination unless a robust, validated genotyping assay was developed. To address this potential concern, this study sought to carry out a detailed bioinformatic analysis of the regions surrounding the IL28A and IL28B gene duplication and to genotype a number of samples by sequencing the DNA surrounding the SNPs in IL28B as well as the corresponding region of IL28A. Finally, a specific and robust genotyping assay was developed for rs12979860, the SNP most strongly associated with treatment response across all global populations studied to date IL28A and IL28B  was extracted and subjected to bioinformatic analysis using Clustal W IL28A and IL28B are found on the forward and reverse strands, respectively, an alignment comparing the extracted sequence to the reverse complement of the same sequence was performed. The locations of each of the SNPs rs8105790, rs11881222, rs8103142, rs28416813, rs4803219, rs12979860, rs12980275, rs8099917 and rs7248668 were identified on the resulting alignment.A 43 kb fragment of chromosome 19 encompassing the sequences of IL28A was performed using the HotStarTaq PCR kit . The PCR reactions (25 uL) included 25 ng of DNA and amplifying primers at a final concentration of 0.4 µM. After enzyme activation at 95°C for 15 minutes, there were 35 PCR cycles comprising 1 minute denaturation at 94°C, 1 minute at the appropriate annealing temperature and 3 minutes extension at 72°C. The last cycle was followed by a final extension at 72°C for 10 minutes. Some amplicons required the use of Q-Solution (Qiagen) as 20% of the reaction volume for amplification. Fragment sizes ranged from 476–2500 bp . These comprised 16 samples each from individuals of Caucasian (CEU), Japanese (JPT) and Yoruba (YRI) origins. PCR amplification of 8 regions spanning the 9 SNP locations and the corresponding regions of –2500 bp .IL28A or IL28B by choosing primer annealing sites with the greatest number of mismatches between the two genes. To further avoid mispriming from homologous regions, the annealing temperature was determined empirically for each set of primers using a gradient cycle from 50°C to 68°C on test samples prior to amplifying from the 48 DNA sample panel. In all cases, the highest annealing temperature that produced a single robust band on a gel and specific IL28A or IL28B sequence data was used for subsequent amplification from the complete panel . All products were sequenced either using their amplification primers or using additional internal sequencing primers . Primers amplifying a 214 bp product that was specific to IL28B , however, in order to choose primers that annealed to specific locations in to IL28B  were manA subset of 24 of the 48 genomic DNA samples that had been sequenced and genotyped were selected for rs12979860 SNP analysis by Monogram Bioscience Inc. . Nine samples were selected to represent the CC genotype, 8 samples to represent CT genotype and 7 samples to represent TT genotype. Aliquot parts of the same DNA samples that had been sequenced and genotyped were submitted for analysis. No details of the testing procedure or analytical validity of the LabCorp Inc. assay have been published IL28A and IL28B genes. In addition, the analysis also showed that a 7.3 kb region extending both upstream and downstream of the two genes shared greater than 95% sequence identity (illustrated in IL28A (IL28B reference sequence (NCBI Genome Build 37) exactly matched the corresponding position in IL28A and the ancestral allele. For example, for rs12979860 the two alleles reported for this SNP were C and T. The reference nucleotide at this location in IL28B was C, whereas it was a T in the reverse complement of IL28A and the ancestral allele was also a T. This finding, together with the high levels of sequence identity across the 7.3 kb region, raised theoretical questions about the potential specificity of genotyping assays for 6 of the 9 reported SNPs. To address this matter, a PCR amplification and Sanger sequencing approach was taken to obtain definitive DNA sequence data to confirm the presence of SNPs in IL28B and to enable assay development for the most cosmopolitan SNP, rs12979860.Analysis of the IL28 gene region confirmed 97.5% sequence identity between the coding exons and introns of the in IL28A . InteresIL28B, as well as the corresponding regions of IL28A, were amplified by PCR and the resulting products were sequenced directly using BigDye® terminator chemistry (Applied Biosystems Inc.). Sequences were assembled to provide a consensus sequence for each PCR fragment and the alleles at each SNP location identified. All 9 SNPs previously identified that were associated with pegylated interferon treatment response were found to be polymorphic, whereas the 6 overlapping regions in IL28A were all monomorphic , and this was used to determine that the underlying 99% and 95% lower confidence limits on concordance were 91.03% and 94.07%, respectively.The genotype results of all 48 Coriell Institute DNA samples are listed in the HapMap data for rs12979860 was available for the JPT and YRI samples . GenotypTo confirm that there had been no sample mix-up with NA18502 in the author's lab, a total of 8 SNPs from 4 chromosomes were determined in addition to a gender marker. All 9 markers were fully concordant with HapMap data suggesting that there was <1% chance of a sample mix up .Upon further analysis at Monogram Biosciences Inc., the T/T signals obtained for sample NA18502 were found to be inconsistent with the normal distribution of T/T results observed in the assay. The interpretation algorithm now used at Monogram Biosciences Inc. has been modified to account for this variant result.The Coriell Institute DNA samples are publically available, therefore the same samples could be used in the future to benchmark newly developed assays.IL28B were associated with response of patients infected with HCV to treatment with pegylated interferon-α (with or without ribavirin) IL28B SNPs has increased tremendously and treatment options based upon a patient's IL28B SNP genotype are being considered Genome-wide association studies have demonstrated that SNPs found in the region surrounding IL28B gene has a close paralogue, IL28A, with which it shares 97.5% sequence identity. This raised the possibility that sequences in IL28A may contribute to the determination of IL28B SNP genotypes. Therefore, understanding genotype assay specificity would be vitally important to consider when using IL28B genotypes to make treatment decisions. Clinicians need to be confident that ‘homebrew’ assays are robust and fully validated to allow prospective genotyping to make treatment decisions on single subjects rather than assays for retrospective cohort-based analyses.The IL28A and IL28B genes likely arose due to an ancestral gene duplication and inversion event, with IL28A being located on the positive DNA strand of chromosome 19 and IL28B 24 kb downstream on the negative strand. Although these genes are 97.5% identical across their 1.4 kb length, a region of >95% sequence identity stretches a total of 7.3 kb in both 5′ and 3′ directions and it includes 6 of the 9 reported treatment response associated SNPs. Our observation that in all cases, one of the two alleles of the IL28B SNPs also corresponded to identical nucleotides in IL28A led us to take a sequence-based approach in order to confirm SNP genotypes. Sequencing specific regions from a panel of DNA samples from 48 individuals representing CEU, JPT and YRI origins demonstrated that all 9 SNPs in the IL28B region were polymorphic, whereas the corresponding sequences in IL28A were all monomorphic. Despite this finding, there was still the potential for IL28A sequences to contribute to genotyping calls if assays were not sufficiently specific.The IL28A sequences. This assay gave 100% concordant results to the sequence-derived genotypes. Whilst not implying that published rs12979860 assays have not been technically validated, examining a selection of different types of rs12979860 genotyping assays found varying numbers of base mismatches in the PCR amplification primers. For example, one TaqMan® assay had 0 and 3 mismatches in the amplification primer pairs IL28B is only driven by a single primer. Two commercially available rs12979860 assays from LabCorp Inc and Quest Diagnostics Inc. have not reported details of their respective assay components. Analysis of a 24 sample subset of the 48 Coriell DNAs revealed one discordant genotype using the LabCorp assay; interestingly in a sample that had its genotype missing in the HapMap data. Re-analysis of this discordant data-point led to a modification of the genotype calling algorithm now used at Monogram Biosciences Inc.As a general rule when developing PCR-based genotyping assays, the greater the number of mismatches between the target primers and other homologous sequences, the greater the assay specificity. SNP rs12979860 currently appears to be the best single choice variant for diagnostic purposes across global populations or for use in clinical trials Given the importance of the studies being used worldwide to examine associations between SNP rs12979860 and response to treatment in various hepatitis populations, it is vitally important that robust and well validated assays are used. This is particularly important as future hepatitis patient treatment decisions will likely be dependent upon results obtained from such genotyping assays.Figure S1Representative sequencing chromatograms covering the 9 IL28B SNPs as well as the corresponding regions in IL28A. Sanger sequencing chromatograms illustrating the different genotypes found in different Coriell samples for each IL28B SNP and the corresponding regions in IL28A.(DOC)Click here for additional data file.Figure S2Sequencing chromatograms covering the IL28B rs12979860 SNP for the 48 Coriell Institute DNA sample panel. Sanger sequence chromatograms for the CEU sample panel (NA17233 - NA17297), the JPT sample panel (NA18954 -NA19085) and the YRI sample panel (NA18502 - NA19223).(DOC)Click here for additional data file.Table S1Primers used to sequence PCR amplicons. Details of sequencing primers used to examine the PCR amplicons encompassing SNP locations in IL28B and the corresponding reverse complement (RC) regions of IL28A.(DOC)Click here for additional data file.Table S2Novel SNPs identified in IL28A and IL28B gene regions in DNA samples from the Coriell Institute. SNP location and corresponding alleles are indicated using the IUPAC ambiguity codes .(DOCX)Click here for additional data file.Table S3Genotypes determined for Coriell Institute DNA samples using DNA sequencing, TaqMan® and LabCorp genotyping assays and data extraction from HapMap. Sample NA18502 (bold) is the sample with the discordant result in the LabCorp Inc. assay. n/n indicates the 2 samples that were genotyped in HapMap project, but no results were reported.(DOCX)Click here for additional data file.Table S4Concordance of additional genotypes determined for Coriell Institute DNA sample NA18502 compared to HapMap data (Release 24). r2 values from linkage disequilibrium analysis in the YRI population were 0.12 for rs2032586 and rs2235015 on chromosome 6; on chromosome 7 they were 0.06 for rs622342 and rs316019; and on chromosome 19 they were 0.48 for rs8105790 and rs11881222; 0.08 for rs8105790 and rs7248668; and 0.05 for rs11881222 and rs7248668. Analysis also determined that NA18502 was from a female.(DOCX)Click here for additional data file.