In the recent decade, high-throughput genotyping and next-generation sequencing platforms have enabled genome-wide association studies (GWAS) of many complex human diseases. These studies have discovered many disease susceptible loci, and unveiled unexpected disease mechanisms. Despite these successes, these identified variants only explain a small proportion of the genetic contributions to these diseases and many more remain to be found. This is largely due to the small effect sizes of most disease-associated variants and limited sample size. As a result, it is critical to leverage other information to more effectively prioritize GWAS signals to increase replication rates and better understand disease mechanisms. In this review, we introduce the biological/genomic features that have been found to be informative for post-GWAS prioritization, and discuss available tools to utilize these features for prioritization With the developments of affordable and reliable high-throughput genotyping and next-generation sequencing platforms, many genome-wide association studies (GWAS) have been successfully conducted to identify DNA variants associated with many complex human diseases and traits, such as cancer, autoimmune diseases, height, blood pressure, body mass index, among others. As of 11/16/13, there were 11,907 single nucleotide polymorphisms (SNPs), 940 traits with 15,052 associations documented in the GWAS catalog, maintained by the National Human Genome Research Institute . These sIn spite of these accomplishments, many challenges remain in GWAS design and analysis. The first limitation is the limited statistical power to identify all disease-associated loci. Although many susceptible loci have been identified, they only explain a small fraction of the overall heritability, with the majority of heritability remaining unexplained. One possible reason is that the missing heritability is due to the lack of coverage of genetic variations on the genotyping platforms, such as those rare or even private variations. Another explanation is that most disease-associated variants have small effect sizes that are not likely detected due to low statistical power, even with thousands of subjects. To better identify these variants, more powerful and cost-effective designs and statistical methods are desired. Several approaches have proved cost-effective to enrich signals and increase statistical power. For example, a number of customized genotyping platforms have been used to target certain genomic regions with high density to fine map disease-associated variants. The successful examples include the use of the ImmunoChip  to fine The second limitation is the difficulty to interpret the biological relevance of susceptible loci and link them with the disease etiology. Because the ultimate goal of association studies is to understand disease etiology and develop effective strategies to prevent and treat diseases, it would be desirable that GWAS results can implicate functional variants and disease pathways that can be experimentally studied in follow-up functional studies. However, a large proportion of disease-associated variants fall into non-coding regions of the genome, with 88% of the associated SNPs in GWAS catalog non-coding , making cis-eQTL SNPs were enriched in known CD-associated SNPs. Based on this observation, the authors proposed to select a subset of SNPs to follow-up a public CD GWAS dataset, to intersect the top 500 GWAS hits with cis-eQTLs in an eQTL database. The SNPs thus selected, cis-eQTLs of the genes UBE2L3 and BCL3, were replicable in two independent replication cohorts. This represents a successful application of the annotation-based prioritization methods.The first step, and sometimes the only step of many SNP prioritization approaches is to annotate the candidate SNPs by intersecting GWAS signals with desired genomic features, such as eQTLs, transcription factor binding sites, DNase hypersensitive sites, histone modifications, and others. For CD, The genomic features may implicate functional roles of the prioritized SNPs to disease etiology, and these hypotheses can be formally tested through molecular studies. These include variants both in coding and non-coding regions. Through these filtering/intersecting methods, researchers can focus on a much smaller number of SNPs in follow-up studies. Although the proximity of a SNP with a documented genomic feature may suggest a functional role of the SNP, it may not necessarily increase the probability that it will affect the phenotype of interest, nor the probability that the locus is truly susceptible. In general, the genomic features discussed above are considered biologically plausible and extensively used to prioritize SNPs, but whether each feature is informative on a SNP’s functional relevance is disease and context dependent. In a recent study, p-value less than 0.01, but not for the missense SNPs, indicating potential loss of information if non-coding SNPs are ignored.By contrasting the SNPs documented in the GWAS catalog  with thoPolymorphisms in the coding region may have different effects on protein function. Synonymous SNPs do not change the corresponding protein sequence; non-synonymous SNPs change amino acid composition, or truncate the protein sequence by causing an early codon; indels can change protein sequence with varying consequence depending on whether the indel is in-frame or frame-shifting; SNPs and indels can also interrupt splicing sites, thus change the mRNA isoform. In other words, mutations in the coding region may be benign or deleterious to protein function, with deleterious mutations more likely to have a phenotypic effect. Many computational tools have been developed to predict “deleteriousness” of SNPs and indels . These mThe most extreme case of protein function interruption is the loss of function mutations. However, genome sequencing studies found that all human carry loss of function mutations without obvious phenotypic effect, and common loss of function variants were depleted in polymorphisms associated with complex disease like CD and rheumatoid arthritis . The resTable 1).Gene expression microarrays and RNA-seq are commonly used to study gene expression profiles in disease cases and matched controls, and differentially expressed genes thus identified may suggest disease mechanisms and potential biomarkers that can be further explored in follow-up studies. DNase I hypersensitive sites (DHSs) are markers of accessible chromatin, which indicate regulatory roles in the transcription process. DHS have been mapped in 349 cell and tissue samples genome-wide by next-generation sequencing . EnrichmThere are many more genomic features collected and annotated in large community projects, such as the Encyclopedia of DNA Elements ENCODE; , which aThe biological features discussed so far are measured/inferred from laboratory cell lines and the sequence and annotation of the human genome, which do not provide trait-specific information. However, trait-relevant features are intrinsically helpful for prioritization. For example, a DNase-seq experiment in intestine tissues and immune cells of CD patients would be more informative for prioritizing variants associated with CD than those measured in brain tissues. Table 1). In their model, each SNP is assigned a prioritization score, which is a linear sum of scores derived from pathway information, comparative genomics, linkage scan, and results of other independent GWAS studies. The weights are decided by the strength of the link between the SNP and the annotations. For example, for a SNP that is in LD with a non-synonymous SNP of a susceptible gene, the assigned weight will be less than that of SNPs physically in the gene. The methodology prioritized SNPs with increased biological relevance in a GWAS study of nicotine dependence. Here we review methods and tools that prioritize GWAS signals at single SNP level. There are mainly two steps in these methods. The first step applies annotations or filters based on whether or not the candidate SNP has the desirable features and the second step scores the candidate SNPs by integrating evidence from multiple sources. Table 1. Most of these tools are only applicable to SNPs, and tools that can prioritize indels are still lacking.There are also web servers that perform SNP prioritization in an annotation fashion. They annotate the candidate SNPs by single or multiple features, but do not combine the results. They differ by the features and strategies they use in prioritization. A list of SNP prioritization resources are provided in FASTSNP uses a decision tree framework to assign different risk level to SNPs by considering the genomic location and functional effect of the SNPs , which could provide useful information for prioritization. Basically, these tools take a list of query variants as input, and annotate them with their in-house databases. Among these, HaploReg (Besides the SNP prioritization tools, there are also many web servers and software for variant annotation (HaploReg  and ReguHaploReg  provide HaploReg  and VariHaploReg  has convHaploReg  and SnpeHaploReg  are stanIn this review, we have focused on biological and genomic features that are informative for SNP prioritization. In the second phase of association studies, researchers can use these databases or tools to choose SNPs in follow-up studies. The observation that eQTLs and open chromatin regions are enriched of trait-associated SNPs highlights the potential rich information in the non-coding regions of the genome . Nonethep-values less than 0.01. The SNPs are treated equally regardless of the association p-values. However, the p-value, the effect size, and other statistics that summarize the association level of individual SNP could be informative for SNP selection. A computational framework that incorporates the significance level with the biological/genomic features discussed above might improve the performance of the prioritization scheme. A discussion of different signal measures of association was given by The prioritization methods discussed here take as input a list of candidate SNPs, which is usually derived by taking all the SNPs achieving a specified significance level in GWAS, e.g., all SNP with Although there are many web servers and databases for SNP prioritization, most of them provide annotations of different types of features, but do not rank these SNPs through integrating GWAS and annotation information. Also, these methods do not employ disease-specific information. There is still a great need for statistical methods that select and integrate multiple annotations in a disease-specific manner, and re-rank SNPs under a coherent statistical framework.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.Lin Hou and Hongyu Zhao conceived and wrote the paper.


Pinus pinaster Ait.), the main conifer used for commercial plantation in southwestern Europe.Single nucleotide polymorphisms (SNPs) are the most abundant source of genetic variation among individuals of a species. New genotyping technologies allow examining hundreds to thousands of SNPs in a single reaction for a wide range of applications such as genetic diversity analysis, linkage mapping, fine QTL mapping, association studies, marker-assisted or genome-wide selection. In this paper, we evaluated the potential of highly-multiplexed SNP genotyping for genetic mapping in maritime pine (in vitro SNPs/Indels) and from Sanger-derived Expressed Sequenced Tags assembled into a unigene set (829 in silico SNPs/Indels). Offspring from three-generation outbred (G2) and inbred (F2) pedigrees were genotyped. The success rate of the assay was 63.6% and 74.8% for in silico and in vitro SNPs, respectively. A genotyping error rate of 0.4% was further estimated from segregating data of SNPs belonging to the same gene. Overall, 394 SNPs were available for mapping. A total of 287 SNPs were integrated with previously mapped markers in the G2 parental maps, while 179 SNPs were localized on the map generated from the analysis of the F2 progeny. Based on 98 markers segregating in both pedigrees, we were able to generate a consensus map comprising 357 SNPs from 292 different loci. Finally, the analysis of sequence homology between mapped markers and their orthologs in a Pinus taeda linkage map, made it possible to align the 12 linkage groups of both species.We designed a custom GoldenGate assay for 1,536 SNPs detected through the resequencing of gene fragments (707 Our results show that the GoldenGate assay can be used successfully for high-throughput SNP genotyping in maritime pine, a conifer species that has a genome seven times the size of the human genome. This SNP-array will be extended thanks to recent sequencing effort using new generation sequencing technologies and will include SNPs from comparative orthologous sequences that were identified in the present study, providing a wider collection of anchor points for comparative genomics among the conifers. Conifers represent over 670 species , some raDuring the past 20 years, many sophisticated genomic tools have been developed to accelerate the domestication process by exploring in depth their genetic diversity. Linkage maps represent a major tool in genomic-based breeding because of their central role in dissecting the genetic architecture of quantitative trait variation . MoreovePinus taeda, P. pinaster, P. sylvestris, P. strobus, P. radiata, P. palustris, P. elliottii, P. edulis, P. densiflora, P. contorta, P. caribaea, and P. brutia) and four spruce species  (reviewed by .Out of the 1,536 SNPs, 707 and 829 markers were classified as atabase  were still successful. A relaxed criterion could thus be attempted especially for those non model species for which few SNPs are available or for some SNPs in candidate genes of interest.Before the construction of the SNP bead array, a designability score  was given to each SNP by the Illumina Assay Design Tool. This score ranges from 0 to 1, with higher values reflecting greater ability to design a successful assay, and 0.6 being the lowest threshold recommended by Illumina to design the GoldenGate OPA (Oligo Pool Assay). In our study, the mean Illumina SNPScore for successfully genotyped SNPs (i.e polymorphic and monomorphic SNPs) and failed SNPs were 0.85  and 0.75 , respectively, which indicates that 94.1% of the successful and 78.3% of the failed SNPs corresponded to Illumina's recommendation (i.e SNPScore ≥ 0.6). A Chi-square test for independence confirmed earlier findings  that thein vitro SNPs (30.3% of the successfully genotyped SNPs) and 139 polymorphic in silico SNPs (26.4%). In the F2 mapping population, 104 of the polymorphic SNPs came from the in vitro set (19.7%) and 89 from the in silico set (16.9%). There was no correlation between the SNP origin and the proportion of polymorphic SNPs .After visual inspection of successfully genotyped SNPs that allowed assignment of individual trees to their respective genotypic classes, we declared 299 and 193 polymorphic SNPs for the G2 and F2 mapping populations, respectively  or contig (for in silico SNPs) provided a unique opportunity to validate the genotyping assay by checking the inconsistency between offspring genotype assignments of physically linked SNPs. For the G2 population, 40 contigs presented more than one SNP. From a total of 3,322 data points we only detected 13 recombination events between SNPs belonging to the same contig, which translates to a genotyping error of 0.4%, if we consider that the probability of crossover within a contig of a few hundred base pairs long is null. For the F2 population, there were 21 contigs containing more than one SNP totalizing 1,543 data points, from which 7 recombination events were observed (i.e. 0.4% error rate). These results confirm the GoldenGate assay reproducibility in maritime pine as previously reported , where n is the minimum number of randomly distributed markers needed to cover a proportion p, of a genome size k, at a maximum distance of 2c between two adjacent markers. We estimated that 373 and 935 markers were necessary to cover 95% of the genome with a maximum distance of 20 or 4 cM between markers, respectively.In the present study, a consensus map of maritime pine was generated from segregation data obtained in full-sib (G2) and selfed (F2) populations. The integrated map provided the relative position of 280 framework and unique SNPs, as well as additional 74 markers . The consensus map comprised 354 markers in total, with two adjacent markers spaced by 4 cM on average. With these parameters, we estimated, the coverage of maritime pine genome using the formula: -e-2dn/L , with c  Boehnke , i.e. n Pinus is 12. However, the 354 markers mapped are not far away from the number of markers expected to cover 95% of the genome for a maximum distance of 20 cM between markers. This suggests that the markers are probably not distributed randomly along the genome and that more markers should be needed to saturate the map. As suggested by Sewell et al. [The consensus map was comprised of 24 linkage groups, while the haploid number of chromosomes for l et al. , the gapl et al.  indicateP. taeda and other Pinus species using putatively orthologous markers [a posteriori search for additional orthologous loci between markers mapped on loblolly and maritime pines. We identified 50 putative COS markers of which 46 showed multiple parallel linkages between 7 linkage groups; the remaining 4 COS markers mapped in non orthologous linkage groups, thus suggesting that they belonged to different members of the same gene families. Altogether, 77 COS markers (31 EST-Ps and 46 SNPs) allowed alignment of all 12 homologous linkage groups between both pine species. Macro-synteny and macro-colinearity were generally well conserved, but some inconsistencies in the marker order within LGs were observed. Such inconsistencies can be attributed either to different members of gene families with different map positions within the same LG, or to the low resolution of the marker order in the consensus map, because of a low number of anchor loci. To ensure that future comparative mapping studies will rely on an a priori defined set of orthologous markers instead of defining orthology between mapped markers a posteriori, we have provided a set of COS markers between the two most economically important conifer genera Pinus and Picea.Comparative mapping consists of studying the conservation of gene content (synteny) and order (colinearity) in the genome of related species and allows inferring chromosome evolution and transferring genetic information between species. Comparisons are generally made by detecting orthologous loci through sequences homology or conserved map regions ,58. Lobl markers ,58-60. MThe present study demonstrates the usefulness of highly multiplexed SNP genotyping arrays to rapidly generate informative molecular markers showing Mendelian segregation in different mapping pedigrees and to establish the first SNP-based genetic linkage map in non-model species such as maritime pine. The genotyping of five other maritime pine mapping populations is underway and will allow us to create a more precise and denser consensus map for this species. The sequencing of parental lines of these mapping pedigrees has recently been carried out using 454-Roche sequencing to extend the set of informative SNPs (data available on the Short Read Archive of the NCBI). Crossing this extended set of SNPs to the COS markers identified in this study will provide new resources for high resolution comparative genomics at the dawn of the first conifer full-genome sequences.EC and CB sampled the plant material and extracted the DNA. AB checked the quality of the DNA. AJE, JW and DN provided most of the resequenced amplicons (CRSP project) and provided loblolly pine mapping information for comparative mapping. JPJC and SCGM provided plant material for CRSP project and checked the re-sequenced data. DG was involved in additional candidate gene resequencing. GLP, PGG and JMF were involved in generating the unigene set from maritime pine Sanger ESTs, and GLP wrote the associated section of the manuscript. GLP, PC and MTC were involved in cDNA and SSH library construction. CL coordinated and performed the SNP analysis from sequence analysis to array design with the help of PGG. CL wrote the associated sections of the manuscript. YCL, YvdP and PR carried out the COS analysis and wrote associated sections of the manuscript. DZ coordinated the genotyping work. EC and PC validated the polymorphic SNPs. EC constructed the linkage maps. CP and MTC coordinated the project, conceived and designed the experiment. AK coordinated the Evoltree Network of Excellence that provided the funding for the genotyping. EC and CP wrote the paper. All authors read and approved the final manuscript.cDNA libraries and sequencing.Click here for filein vitro SNP detectionList of the 49 candidate gene fragments used for .Click here for fileList of the 1,536 SNPs and associated features.Click here for fileSummary of the different types of markers combined with SNP markers for the construction of the G2 linkage maps.Click here for fileGenetic linkage maps for maritime pine: G2 Female and G2 Male, F2 and consensus.Click here for fileNumber of SNP markers and contigs mapped on the G2 and F2 linkage maps.Click here for fileNumber of orthologous markers mapped on the loblolly and maritime pine linkage groups.Click here for fileCOS markers between TGI pine, TGI spruce and maritime pine unigene.Click here for file


Single nucleotide polymorphism (SNP) based association studies aim at identifying SNPs associated with phenotypes, for example, complex diseases. The associated SNPs may influence the disease risk individually (main effects) or behave jointly (epistatic interactions). For the analysis of high throughput data, the main difficulty is that the number of SNPs far exceeds the number of samples. This difficulty is amplified when identifying interactions.In this paper, we propose an Adaptive Group Lasso (AGL) model for large-scale association studies. Our model enables us to analyze SNPs and their interactions simultaneously. We achieve this by introducing a sparsity constraint in our model based on the fact that only a small fraction of SNPs is disease-associated. In order to reduce the number of false positive findings, we develop an adaptive reweighting scheme to enhance sparsity. In addition, our method treats SNPs and their interactions as factors, and identifies them in a grouped manner. Thus, it is flexible to analyze various disease models, especially for interaction detection. However, due to the intensive computation when millions of interaction terms needs to be searched in the model fitting, our method needs to combined with some filtering methods when applied to genome-wide data for detecting interactions.By using a wide range of simulated datasets and a real dataset from WTCCC, we demonstrate the advantages of our method. Rapid Improvements of high-throughput genotyping technologies enable us to detect genetic variations with much finer resolution than before. In genome-wide association (GWA) studies of complex diseases, a few thousands samples are collected and hundreds of thousands of single nucleotide polymorphisms (SNPs) have been genotyped for each sample .Researchers have been investigating disease-associated gene mapping for decades and various approaches have been proposed. However, most of them have used a single-SNP based strategy, in which each SNP is analyzed individually . Each SNP has only three genotypes: two homozygous genotypes (AA and aa) and one heterozygous genotype (Aa). Therefore, each SNP can be naturally treated as a three-level factor and be coded with three dummy variables. Similarly, the interaction between two SNPs can be treated as a nine-level factor. In order to encourage sparsity on factors (groups of variables) rather than a single dummy variable, we impose a group constraint on the set of dummy variables that represent a disease model . Hence, we propose an Adaptive Group Lasso (AGL) method to identify main effects and epistatic interactions from large-scale SNP data. Since Lasso . In this case, the nominal type-I error rates should be 10, 20, 30 per one million SNPs for significance thresholds at 0.1, 0.2, 0.3.• Case 2: We use genomeSIMLA  to simulWe summarize the type-I error of our model (2) in Table 8 MCMC and set the significant threshold as 0.3 after Bonferroni correction. BEAM does not report any interactions. The identified main effects of the two methods agree with each other. Some of them are given in Table Current version of BEAM software can not handle WTCCC data on the genome-wide scale. To compare with BEAM on the real data, we apply our main effect model (2) and BEAM to analyze WTCCC Rheumatoid Arthritis (RA) data in the chromosome-wise manner. For BEAM, we run 10• WTCCC reports that SNP rs6457617 located at 6p21 shows a very strong association. Our experiment verifies this result.• We do not identify SNP rs6679677 located at 1p13 reported by WTCCC. Instead, we identify two SNPs rs7551793 and rs948620 near SNP rs6679677. The signals of these two SNPs are much stronger than rs6679677.• We also identify SNPs in the moderate association regions reported in . We summDetecting interactions in genome-wide scale is very challenging and multi-stage strategies are often explored. For example, MDR  usually For identification of epistatic interactions, we focus on two candidate regions: 6p21 and 7p21. These two regions are reported in our previous work named SNPHarvester  which is-3 given by univariate analysis). However, they do show a strong interaction. We also run BEAM on the selected segment (5 × 106 MCMC). BEAM reports  and  as the two most significant interactions based on the B-statistics. We carefully check these two pairs based on the logistic regression model. We find that the interaction of the SNP pair  is very weak using the standard χ2 test based on logistic regression models with df = 4, while the interaction of the SNP pair  is strong. We further explore the reason why our method does not report the interacting pair . We observe that SNP rs9268645, which is highly correlated (strong LD) with SNP rs3129872, enters our model as a main effect term. Consequently, the pair  does not enter the model as a interaction term. This shows that the SNP pair  should not be reported as an interacting pair.• For the region 6p21, we select a segment covering the SNPs reported in . This se• For the region 7p21, we select a segment which covers the SNPs reported in . The seg• Neither BEAM nor our method finds significant interactions in the region 6q23.The definition of interactions is not consistent in literatures. For example, the interaction effect of two SNPs is define via logistic regression models of genotypes and their combinations , while iIn this paper, we proposed an Adaptive Group Lasso method for large-scale SNP data analysis. The novelty of our method is that it analyzes SNPs and their interactions simultaneously. It imposes a sparsity constraint at the group level and enables us to identify associated SNPs  from large-scale SNP data robustly. We show that our method outperforms its recent competitors in both simulation studies and real application.The limitation of our method is that the interaction model can not be directly applied to genome-wide scale SNP data analysis. The main difficulty comes from the computation burden of searching for all SNP pairs. There are two possible solutions to solve this issue. One solution is to make use of some filtering method to reduce the number of SNPs to a manageable size, for example, . AnotherA and B) and lowercase letters  to denote major and minor alleles, respectively. We also use G1 to denote the collection of all three genotypes of one SNP and use G2 to denote the collection of all nine combinations of two SNPs:SNPs are high-density bi-allelic markers. We use capital letters  matrix. We shall use Xj, a submatrix of size N × 3, to denote the columns of X corresponding to the j-th SNP. Similarly, Xij, a submatrix of size 1 × 3, corresponds to the i-th sample and the j-th SNP. We propose an Adaptive Group Lasso logistic regression model (AGL) for main effect identification:Suppose γ is a real value parameter controlling the trade-off between the likelihood and the constraint, pj = 3 is the number of dummy variables used to coding the j-th SNP for j = 1, ⋯, L, βj = , β) is the log-likelihood of logistic regression:where active set j|βj ≠ 0}. We use an iterative algorithm to adaptively assign weights wj in (2) as given in Algorithm 1.For convenience, we define an SNPj is less likely to be associated with disease, then the penalty weight should increase to prevent SNPj from entering the model in the next iteration, and vice versa. The theoretical justification is given in the supplementary document.From the iterative algorithm, the penalty weight is adjusted according to its previous estimation. If the current estimation Algorithm 1 Adaptive Reweighing Algorithm of Group Lassom = 0. Initially set j = 1, ⋯, L.1. Set the iteration count β(2. Solve problem (2) to obtain 3. (1) Update the weight:SNPj, when j ∉ (2) Remove m and go to step 2.4. If the active set The proposed model has following characteristics:• Flexibility: Due to the dummy variable representation, our model is flexible to analyze different main effects  in a unwj is assigned adaptively in Algorithm 1 to enhances the sparsity at the factor level. If without reweighting, too many noise SNPs would enter the model in GWA studies.• Sparsity: The sparsity constraint SNP1 and SNP2 are often defined via logistic regression models as in , U is a diagonal matrix with diagonal elements pi(1 - pi). Here where Γ is a , fit the logistic regression model with the main effect term replacing the interaction term and obtain the log-likelihood ℓl\.2. For each interaction term in l using χ2 tests based on the value 2(ℓfull-ℓl\) with df = dffull - dfl\.3. Obtain p-value of interaction term The authors declare that they have no competing interests.CY performed the implementations and drafted the manuscript. XW participated in the experimental design. QY, HX and WY finalized the manuscript. All authors read and approved the final manuscript.The supplementary document provide some backgrounds of our method. It also provides details of our optimization algorithm, and comprehensive experimental results.Click here for file


Manihot esculenta Crantz) is an important crop that provides food security and income generation in many tropical countries, and is known for its adaptability to various environmental conditions. Its draft genome sequence and many expressed sequence tags are now publicly available, allowing the development of cassava polymorphism information. Here, we describe the genome-wide discovery of cassava DNA polymorphisms. Using the alignment of predicted transcribed sequences from the cassava draft genome sequence and ESTs from GenBank, we discovered 10,546 single-nucleotide polymorphisms and 647 insertions and deletions. To facilitate molecular marker development for cassava, we designed 9,316 PCR primer pairs to amplify the genomic region around each DNA polymorphism. Of the discovered SNPs, 62.7% occurred in protein-coding regions. Disease-resistance genes were found to have a significantly higher ratio of nonsynonymous-to-synonymous substitutions. We identified 24 read-through (changes of a stop codon to a coding codon) and 38 premature stop (changes of a coding codon to a stop codon) single-nucleotide polymorphisms, and found that the 5 gene ontology terms in biological process were significantly different in genes with read-through single-nucleotide polymorphisms compared with all cassava genes. All data on the discovered DNA polymorphisms were organized into the Cassava Online Archive database, which is available at http://cassava.psc.riken.jp/.Cassava ( Manihot esculenta Crantz (2n = 36), is a tropical crop that is important for food security in tropical regions worldwide Cassava, Accumulation of nucleotide sequence information from various organisms, including cassava, has been promoted as an effective method for gene discovery in recent decades Molecular markers are important for plant research and breeding, and are being applied to accelerate effective plant selection through marker-assisted selection, based on genome-level selection of chromosomal segments. In plant genetic research, molecular markers are also being used for the analysis of population structure, the study of evolutionary relationships, and, in sequenced model systems such as Arabidopsis, for studies on the genetic structure of individuals at the whole-genome level Studies on genetic mapping and molecular marker development in cassava have been published Many public databases of major plant genomics resources have been constructed to integrate knowledge and to facilitate further research http://cassava.psc.riken.jp/.Here, we describe the discovery of over 10,000 SNPs and InDels in cassava, and the relationship between polymorphism and gene function. We have organized the results of our research, including gene models and functional annotations, into a database named “Cassava Online Archive,” which can be accessed at http://compbio.dfci.harvard.edu/tgi/software/) with the default runtime options. Vector sequences included in these ESTs were then trimmed using the cross_match utility of the Phred/Phrap package with -minmatch 10 -minscore 20 runtime options http://www.ncbi.nlm.nih.gov/VecScreen/UniVec.html). Contamination was detected via BLASTN sequence similarity searches with the default runtime settings against both the Escherichia coli K12 genome (GenBank accession number U00096) and the bacteriophage phi_X174 (GenBank accession number J02482) genome sequences. Sequences with threshold E-values of less than 1e-100 were removed. The cleaned sequences were then classified by cultivar description in each GenBank format file. A portion of the sequences was confirmed by contacting the contact person or submitter of the sequence record (http://www.phytozome.net/) We retrieved cassava sequences from the GenBank EST section in December 2012 e record . To discThe sequences obtained by the above process and the predicted transcript sequences from the cassava draft genome sequence were assembled using the CAP3 program with the default runtime options The physical position of each SNP or InDel was deduced from the cassava draft genome sequence. Primer pairs were then designed to amplify the genomic region around each discovered SNP or InDel site using Primer3 (Release 2.2.3) To validate the SNPs discovered in this study, we compared them with the validated SNP information published in a previous report Transition-to-transversion ratios, nonsynonymous and synonymous substitutions, and premature stop and read-through SNPs were examined using custom Perl scripts that analyzed the sequence assembly and the annotated transcript sequences. SNPs located within cassava gene models (JGI annotation v4.1) were classified based on the positions of the 5′-untranslated region (UTR), 3′-UTR, and protein-coding sequence (CDS) within the gene model. SNPs detected in cassava predicted transcript sequences were examined for the relevant codon as well as for nonsynonymous and synonymous substitutions. SNPs were further annotated according to whether they produced a premature stop codon or disabled a stop codon. For the assignment of a cassava gene model to a Pfam protein domain For gene ontology (GO) ij) and adjusted standardized residuals (dij) are given by the following equations:ij is the observed frequency in the cell in row i  and column j ; ni., total frequency for row i ; n.j, the total frequency for column j ; N is the overall total frequency; and Eij is the expected frequency in the cell in row i and column j. The larger the absolute value of the adjusted standardized residual, the larger the difference between the observed and expected frequencies. An absolute value of the adjusted standardized residual of >1.96 indicates that a GO term is significantly different between the grouped variables with a significance level of 0.05. Similarly, an absolute value of >2.575 indicates that a GO term is significantly different between the grouped variables with a significance level of 0.01.To confirm the independence of GO assignments between the two groups, we used the Pearson chi-square test with the adjusted standardized residual method We retrieved 80,631 cassava sequences from GenBank The sequences were assembled using the CAP3 For 8,794 of the 10,546 SNPs discovered, we succeeded in designing a PCR primer pair for amplification of the genomic region. SNPs were discovered in 3,252 genes, with an average of 3.2 SNPs per gene and a SNP frequency of 1 SNP per 1,072.5 bp in transcripts, including introns . Similarhttp://cassava.psc.riken.jp/.SNPs have been previously identified in cassava We validated the physical locations on the cassava draft genome sequence and alleles of the SNPs discovered in this study using the information from a previous paper, which reported SNP detection and validation Six possible types of single-nucleotide changes were distinguished: C/T, G/A, A/C, A/T, C/G, and T/G, accounting for 2,893, 2,952, 1,108, 1,285, 996, and 1,312 SNPs, respectively. The total numbers of transitions (C/T or G/A) and transversions  were 5,845 and 4,701, respectively . The traIn 8 cassava varieties , from which more than 500 sequences were collected, we conducted further analyses of SNPs for each possible pairwise combination of varieties . A polymAs described above, in total, 10,546 SNPs and 674 InDels were discovered . We founWe next calculated the codon position for each SNP in a CDS. The numbers of SNPs located at the first, second, and third bases of a codon were 1,638, 1,240, and 3,735, respectively . More thTo clarify how many synonymous changes could potentially affect protein functions in the deduced cassava proteome, we focused on comparative analysis of CDSs regions. SNPs were first classified according to whether they caused no amino acid change (synonymous) or one amino acid change (nonsynonymous). The percentage of nonsynonymous SNPs was 46.8% , similarGenes encoding members of the ubiquitin family and ATP synthases had lower nonsynonymous-to-synonymous substitution ratios. In contrast, sequences encoding NB-ARC domains and leucine-rich repeats had significantly higher ratios of nonsynonymous-to-synonymous SNPs . DomainsWe identified 24 read-through (change a stop codon to a coding codon) SNPs and 38 premature stop (change a coding amino acid to a stop codon) SNPs . By assihttp://cassava.psc.riken.jp/). To access SNP/InDel records, the Cassava Online Archive provides for web-based searching by keyword, polymorphism identifier, gene model identifier , and cassava variety name; target information can also be browsed using the Generic Genome Browser The DNA polymorphism information collected in this study was organized into the Cassava Online Archive database Click here for additional data file.Figure S2Distribution of the contigs and sequences in the assembly for detecting DNA polymorphisms. The numbers of sequences per assembled contig were between 2 and 707 with an average of 5.9 sequences per contig.(TIFF)Click here for additional data file.Figure S3Overview of polymorphism detection and average number of polymorphisms per contig in which polymorphisms were detected. The average number of polymorphisms per contig was 3.8, and the average per fraction ranged from 2.6 to 6.2 polymorphisms/contig.(TIFF)Click here for additional data file.Figure S4Distribution of the InDel lengths. The numbers of 1-, 2-, and 3-nucleotide InDels (insertions and deletions) are 612, 42, and 20, respectively.(TIFF)Click here for additional data file.Table S1Summary of genes with allelic SNPs detected by pairwise analysis of cassava varieties(DOC)Click here for additional data file.Table S2Details of SNPs located in predicted CDSs from the cassava draft genome sequence(XLS)Click here for additional data file.Table S3Details of SNPs located in predicted CDSs from the cassava draft genome sequence Of the 1,190 validated SNPs by Ferguson et al. in 2013, 103 were able to be assigned to the locations of the discovered SNP in this study. All the alleles were also identical to the result of this study.(XLS)Click here for additional data file.Text S1Example of an input file during a Primer3 program run.(TXT)Click here for additional data file.


The recent completion of the swine genome sequencing project and development of a high density porcine SNP array has made genome-wide association (GWA) studies feasible in pigs.Using Illumina's PorcineSNP60 BeadChip, we performed a pilot GWA study in 820 commercial female pigs phenotyped for backfat, loin muscle area, body conformation in addition to feet and leg (FL) structural soundness traits. A total of 51,385 SNPs were jointly fitted using Bayesian techniques as random effects in a mixture model that assumed a known large proportion (99.5%) of SNPs had zero effect. SNP annotations were implemented through the Sus scrofa Build 9 available from pig Ensembl. We discovered a number of candidate chromosomal regions, and some of them corresponded to QTL regions previously reported. We not only have identified some well-known candidate genes for the traits of interest, such as MC4R (for backfat) and IGF2 (for loin muscle area), but also obtained novel promising genes, including CHCHD3 (for backfat), BMP2 , and some HOXA family genes . The candidate regions responsible for body conformation and FL structure soundness did not overlap greatly which implied that these traits were controlled by different genes. Functional clustering analyses classified the genes into categories related to bone and cartilage development, muscle growth and development or the insulin pathway suggesting the traits are regulated by common pathways or gene networks that exert roles at different spatial and temporal stages.This study is one of the earliest GWA reports on important quantitative traits in pigs, and the findings will contribute to the further biological function analysis of the identified candidate genes and potential utilization of them in marker assisted selection. The domestic pig has a long history of extensive natural and artificial selection partly to meet human dietary needs http://www.animalgenome.org/cgi-bin/QTLdb/SS/index). However, those approaches have limitations. Candidate gene selection according to their putative physiological roles could be limited, and may miss novel gene identification and / or pathways influencing the traits. The regions of identified QTL are generally large and fine mapping is required to find more closely linked markers or causative variants suitable for marker-assisted selection. The consistency of QTL mapping may be limited when it is based among resource families developed from diverse founders Both candidate gene and QTL mapping strategies have been used in domestic animals for the discovery of genetic markers suitable for MAS i.e., to predict an animal's breeding values using information provided by numerous SNPs across the entire genome. In this seminal work, two Bayesian approaches (Bayes A and Bayes B) were developed to predict genomic estimated breeding values (GEBV). In the years following this paper, alternative statistical approaches for genomic selection have been developed, derived from Bayesian models and some other parametric methods including GBLUP and mixed regression models i.e., frequently included in the model for GEBV prediction) can be identified as those likely linked to QTL In contrast to studies in humans where association analyses have the primary purpose of identifying markers for disease, the applications of dense SNP arrays in livestock focus on genomic selection , in order to improve selection accuracy to accelerate genetic improvement for economically important performance traits in breeding animals Most of the published GWA studies carried out in animals involved Mendelian or disease related traits Loin muscle depth or loin area and fat depth are good predictors of carcass lean content and are two traits targeted in pig breeding programs. Body conformation as well as feet and leg (FL) structural soundness are relevant to growth, feed intake capability, reproductive efficiency and sow longevity and are receiving more attention in modern swine production where pigs are being raised in confined production systems. It has been reported that approximately 40% of breeding sows were culled because of FL problems Detailed descriptions of exploratory analyses of all phenotypes are in Sus scrofa (SSC) Build 9, a total of 55,446 of these SNPs have been mapped to a genomic location. The average physical distance between any two neighboring SNPs on the same chromosome was approximately 41.6 Kb, ranging from 35.2 Kb (SSC14) to 81.4 Kb (SSCX) (http://www.thearkdb.org), the average genetic distance between SNPs on the chip is 0.046 cM, ranging from 0.02 cM (SSC1) to 0.08 cM (SSCX)  . Based oM (SSCX) .PARK2 gene located between 6.5 and 7.5 Mb on SSC1.The distribution characteristics of SNPs were analyzed and among mapped loci, 15,338 are intragenic and are from 7,099 genes. The maximum number of SNPs from one gene is 33 and it is the Only five genotyped animals had average genotype call rates less than 80%, and were removed from further analysis. Additionally, SNPs with no call  or call rates less than 90% (400) were discarded. A total of 51,385 SNPs passed these quality control steps and were retained in the dataset.A population stratification analysis using IBS distance clustering showed that gilts from the two lines present in the data could be classified into one population, suggesting no significant genetic difference existed between these two lines. Since pedigree information indicated the lines had been separated, lines were fitted as a fixed effect in the model for association analyses.th rib backfat with around 250 SNPs likely accounting for 54% of total genetic variance (µ = 0.995), loin muscle area with the same number of SNPs accounting for 55%. While other traits, such as weak top line and high top line, had relatively lower heritability estimates.Based on the heritability of each trait , appropret al.The putative candidate chromosomal regions were obtained by finding those genomic locations comprising windows of 5 consecutive SNPs with the highest genetic variance. Significance level was subsequently determined by bootstrap analyses under the null hypothesis that these windows did not harbor QTL. The model frequency statistic was used to find the most promising SNPs within these regions. With the assumption that the experiment had 50% power and a 99% null hypothesis of no QTL in the SNP window, then the PFP  from Fernando th rib backfat revealed that some of the genes underlying them were different, even though their genetic correlation has been estimated to be nearly 0.90 MC4R on SSC1, ATP6V1H and OPRK1 genes on SSC4, LDHD gene on SSC6, CHCHD3 gene on SSC18 and ATP2B3 genes on SSCX. In total, 21 regions were associated with last rib backfat , and are listed in As for 1018 and X . Surpris18 and X . A large reports . For exaMC4R Asp298Asn along with any other SNP involved in previous patents or patent applications were not included in the chip so that no infringement would exist. However, the SNP MC4R Asp298Asn was evaluated in this population in an earlier study MC4R Asp298Asn were added to the dataset, and fitted as a random effect in a Bayes C model, achieving a model frequency of 0.08  dropped, suggesting there may be interactions associated with backfat between MC4R and other SNPs in the region.The SNP  of 0.08  with theMC4R Asp298Asn SNP and four other significant SNPs. The other SNPs were in different haplotype blocks, whereas ALGA0006623 and Asp298Asn were from the same block  and body shape traits  and are shown in dy width . The regHOXA1, HOXA2 and HOXA3) was identified, and on SSC9, the region containing the TWIST1 and SP4 genes was detected.For overall leg action, there were 14 candidate regions  and S5, SOX9, LRCH1, BMP2, COL4A3 and COL4A4, some might be related to muscle development such as MYOD1, MUSK, MYH1, MYH2, MYL9, MAP2K6 and MAP3K4, while others could be relevant to the insulin pathway such as PDX1, PTPN1, CTGFL and WISP2. These groups are all known to be associated with growth and development.However, for FL structural traits, which have relatively lower heritabilities, multiple genes seem to be involved. The putative candidate genes associated with traits were quite different, even for similar traits such as front leg pastern and rear leg pastern positions, and different traits at the same body position such as front leg pastern and front leg turned in . A clustThe GWA and genomic selection studies conducted to date are beginning a promising era in animal breeding. To our knowledge, this might be the earliest GWA report on economically important growth and production traits in pigs. The primary results will contribute to the dissection of molecular mechanisms regulating important quantitative traits, identification of novel candidate genes and networks, and functional analysis of promising genes.It is likely that GWA studies will be increasingly important to animal improvement. For disease related traits, a case-control strategy that is well-known in human studies has been generally utilized Population stratification is a confounding component that affects association analyses. The animals in this study were from two different genetic lines, which originated from the same breed resources. A clustering analysis based upon IBS distance classified them into one population. Nevertheless, the lines were fitted as a fixed effect in the model for association analyses based on pedigree information.The Bayesian method used for association analyses in this study has been demonstrated to have higher accuracy even with different relationship scenarios between training and validation populations th rib backfat, and half of them corresponded to previously reported QTL regions. The most promising SNP ALGA0118164 was 13 kb upstream of the CHCHD3 (coiled-coil-helix-coiled-coil-helix domain containing 3) gene, which is primarily expressed in mitochondria. The Leptin gene is around 4 Mb downstream of this gene, but the nearest SNP M1GA0023128 to Leptin was not in strong LD with ALGA0118164 and it did not achieve high model frequency (0.004). There are no reports about the association between CHCHD3 and phenotypes related to obesity/diabetes in humans and rodents. An investigation involving additional SNPs within CHCHD3 and functional analysis using adipocyte lines is warranted to verify the association of this gene with fat development.A total of 18 candidate chromosomal regions were identified for 10Sus scrofa Build 9, including MC4R, one of the important functional genes associated with backfat. In pigs, the SNP Asp298Asn was found to be significantly associated with backfat and growth rate BCL2 and CCBE1 are two of interest. In addition, the candidate region harboring the SNP M1GA0005986 on SSC4 corresponded to a fat QTL location reported in the first porcine QTL mapping study et al.RXRG and SDHC genes. These two genes are located at 89 Mb on SSC4 (Build 9), about 8 Mb from the region we found. The actual FAT1 QTL effects caused by the identified genes or others such as ATP6V1H and OPRK1 in this region should be explored to identify the causative mutation.One important region was identified between 165.47–168.63 Mb on SSC1, with around 20 coding genes annotated in BMP2 gene on SSC17. It is well known that BMP2 is involved in regulating early myogenesis and could inhibit proliferation or induce presumptive muscle cells to undergo apoptosis, thus inhibiting muscle development. These results, combined with those discussed later in this manuscript involving the association between BMP2 and body dimensions help explain the role this gene has in controlling body dimension and muscle development. A region on SSC2 including the significant SNPs M1GA0002180 and M1GA0002244 is close to the locus where the IGF2 gene is located. IGF2 has been considered to be one of the major causative genes associated with muscle development in vitro interaction with a nuclear factor and was associated with a threefold increase in IGF2 messenger RNA expression in postnatal muscle There were 14 candidate regions associated with LMA, and eight of them corresponded to QTL regions previously reported. The most interesting SNP INRA0052808 was from the HOXA1, HOXA2 and HOXA3 were identified. HOX genes encode a class of transcription factors that contain an antennapedia (HOM gene) related homeobox and are conserved throughout metazoan evolution in vertebrates, and they exert essential roles on the morphogenesis of skeletal structure along the antero-posterior axis FHL3 gene was associated with leg action. FHL3 negatively regulates myotube formation of C2C12 cells, and it could form a complex with MyoD inhibiting its transcription activity and control the expression of muscle specific genes such as muscle creatine kinase and myogenin TWIST1 and SP4 genes, which are of interest for their association with leg action.A total of 14 candidate regions were found to be associated with overall leg action. On SSC18, one candidate region including a cluster of HOX family genes including BMP2 gene was associated with all three body size traits. The allele T of SNP INRA0052808 located in BMP2 was associated with long body length, shallow body depth and narrow body width, and additionally it was associated with upright front/rear leg pastern postures and smaller LMA , a type of hereditary disease characterized by muscles that fatigue easily resulting in muscle weakness caused by neuromuscular transmission dysfunction in humans MAP2K6 and MAP3K4 are two of the MAPK kinases that are involved in the p38 mitogen-activated protein kinase (MAPK) pathway. The p38 MAPKs regulate the transcriptional activities of MRFs and function in the remodeling of chromatin at specific muscle-regulatory regions, and it is one of the major intracellular signaling pathways affecting myogenesis The genes and transcription factors PDX1 could regulate the expression of pancreatic endocrine cell genes including insulin through the interaction at A-T rich regions contained within the promoter of these genes, and has a role in maintenance of the β-cell phenotype PTPN1 promoter might contribute to the development of T2D and related metabolic traits CTGFL could promote the adhesion of osteoblast cells and inhibit the binding of fibrinogen to integrin receptors, and may play important roles in modulating bone turnover Both insulin and IGF1 are known to induce a wide variety of growth and metabolic responses and play essential roles in anabolic regulation of bone metabolism PPARG, Leptin receptor (LEPR) and IGFBPs (for backfat deposition), Myogenic regulatory factors (MRFs) and Myostatin (MSTN) (for muscle growth), and CALCR, OXTR and PTHR (for leg related traits), were not found to be associated with the analyzed traits in these animals. In addition, most of the traits - associated SNPs (TASs) were from novel genes without obvious biological significance relevant to the analyzed traits, and most of TASs were located in the intergenic regions or introns of coding genes. Similarly, in GWA studies of the humans, the TASs have not always been from the putative candidate genes relevant to disorders or diseases SLC11A1, IL23R and NOD2 were not found to be associated with this disease i) The TASs may be from genes that have not yet been annotated, or linked to the unmapped SNPs, therefore, further annotations on the current Sus scrofa Build 9 are necessary; ii) The sample size and the genetic backgrounds of the studied populations may have effects on association analyses, and additional pure breeds with larger sample size will be of help for GWA implications; iii) Although Bayesian methods are well suited for genomic selection and the statistics such as window variance and model frequency can be used to evaluate associations, the prediction accuracy of the methods were between 40–60%. Therefore, the indicator for association may not be the best one, and additional statistical approaches for GWA study are needed; iv) The large (∼40 Kb) average interval and uneven SNPs distributions of the current porcine 60 K BeadChip may be major limitations for haplotype blocks analyses and fine mapping In this study, a number of candidate chromosomal regions relevant to the analyzed traits were identified, and some were consistent with previously reported QTL regions. These new SNP chip results demonstrated that combining dense SNP arrays and using the Bayes C approach is ideal for GWA studies on important quantitative traits in domestic animals. Meanwhile, some issues are worth discussing when the findings were compared with those of conventional candidate gene studies Animal care guidelines were followed according to the Institutional Animal Care and Use Committee (IACUC) at Iowa State University .A total of 820 gilts were included in the study, which were commercial breeding stock sourced from Newsham Choice Genetics  between October 2005 and July 2006. The gilts belonged to two genetic lines and included 412 Large White line pigs and 408 pigs from a Large White×Landrace cross.Body composition and structural soundness evaluations were carried out on 14 dates, with females having an average body weight of 124±11 kg and age of 190±7 days at appraisal time. Body composition traits comprised ultrasonically measured loin muscle area at the 10th rib, 10th rib backfat and last rib backfat. Ultrasonic images were taken with a Pie Medical 200  by a single technician certified by the National Swine Improvement Federation. A total of 17 body structural soundness traits were recorded including body conformation , front feet and leg structure , rear feet and leg structure  and overall leg action, which is a general appraisal reflecting both structural soundness and freedom of other defects affecting the gait and locomotion. All of the structural soundness traits were independently evaluated by two experienced scorers using nine-point scales, where one and nine indicated the extreme phenotypes of the traits. The intermediate score is the most favorable for four of the scoring traits including top line, turned front legs, turned rear legs and weak/upright rear legs. Scoring criterions and the descriptions of scores are shown in TM ear tag from Agrobiogen . The DNA was isolated from dry ear tissue using the DNeasy 96 Blood & Tissue Kit . DNA quantification was performed using a NanoDrop 1000 . DNA samples were submitted for genotyping with total DNA between 700–1000 ng, 260/280 ratio>1.50 and DNA concentration>20 ng/ul. The genotyping was done by GeneSeek Inc.  using the Porcine 60K BeadChip  and approved standard techniques outlined by the manufacturer. Quality control (QC) was performed after we received the original SNP genotyping data. The SNPs were filtered with call rate≥80%, Gentrain Score≥50%, minor allele frequency (MAF)≥0.05 and P value of A very small amount of ear tissue was collected from gilts using the TypiFixThe statistical analyses for descriptive analyses including population mean and normal distribution testing were performed using the UNIVARIATE procedure of the SAS software package . Top line, turned front leg, turned rear legs and weak/upright rear legs were each divided into two traits prior to analyses because of intermediate optimum of the original trait scoring . In addiThe animals in the study were from two genetic lines but originated from Large White or Large White×Landrace interbreed crossing. Population stratification was examined using an identical-by-state (IBS) distance clustering method in PLINK program y is the vector of phenotypes of the analyzed traits, X is the incidence matrix for fixed effects, K is the number of SNPs in the dataset, jZ is the column vector representing the SNP covariate at locus j, ju is the random substitution effect for locus j, which conditional on σ2u, is assumed normally distributed N when jδ = 1, while ju = 0, when jδ = 0, jδ is a random 0/1 variable indicating the absence (with probability π) or presence (with probability 1−π) of locus j in the model, and e is the vector of random residual effects assumed normally distributed N. Thus, both Bayes B and Bayes C are mixture models that assume a mixture of two distributions for the SNP effects, with assumed mixture fraction π. In these analyses, π was assumed to be 0.995. The variance σ2u (or σ2e) was a priori assumed to be scaled inverse chi-square with uv = 4 (or e = v10) degree of freedom and scale parameters S2u (or S2e) 2u is common to all loci in the model in contrast to the locus specific variance component in Bayes B.A new Bayesian method for genomic selection (Bayes C) has been proposed ju were estimated using the Monte-Carlo means of the posterior distribution of these effects computed by a Gibbs sampling strategy. For a locus j, samples for jδ were obtained from its conditional distribution given b, the effects at all other loci included in the model and the two variance components σ2u and σ2e. This conditional distribution can be written as ju- is the vector of effects in the model other than at locus j. The f was obtained as the sum of Pr(jδ) f computed for jδ  = 0 and for jδ  = 1 SNP effects In each iteration of a Monte Carlo Markov Chain, every SNP was subjected to a likelihood ratio test that determined the probability that SNP would be included in the model given the fixed effects and currently fitted markers, and that SNP was then included in the model in that iteration with the calculated probability. Evidence for an informative SNP was obtained by accumulating the frequency across iterations of the chain that a particular SNP was fitted in the model, we refer to this statistic as model frequency. If consecutive SNPs are in high linkage disequilibrium (LD) with the same QTL, then the effect of the QTL and the SNP model frequency will be distributed across all the SNPs in high LD with the QTL. For this reason model frequency of individual SNP is not a good indicator of the presence of QTL in genomic regions with high LD. Accordingly, two alternative approaches were used to infer presence of QTL. First, by accumulating the window model frequency whereby a genomic window of typically 5 immediately consecutive SNPs was counted as being included in the model when at least one of any of the SNPs in that window were in the model that iteration. Second, a QTL was inferred by computing a statistic representing the relative contribution of variation in window breeding value compared to variation in genomic breeding value. Window breeding value was computed by multiplying the number of Illumina B alleles that represent the SNP covariates for each consecutive SNP in a window by their respective posterior means for substitution effects. After computing these 5 SNP window breeding values for all animals in the population the variance of these breeding values was calculated. Windows that contributed the highest genetic variance were considered to be the strongest signals of association. Genomic breeding values are calculated using the same approach as window breeding values but utilized every SNP in the genome. These window approaches to identify important genomic regions accounted for linkage disequilibrium and better characterize QTL than individual SNP effects.http://bigs.ansci.iastate.edu/bigsgui/login.html), developed by Fernando and Garrick Several factors relevant to the traits, such as genetic line, measurement date and scorer, were considered as fixed class effects or covariates (e.g. body weight). The above procedures were implemented using a web-based program, GenSel  for each putative QTL. A bootstrap sample of the vector y for replicate et alThese bootstrap samples are constructed according to the null hypothesis of no QTL in the identified SNP window. Each bootstrap sample was reanalyzed using the Bayes C model used for the real data, and the genetic variance of the SNP window corresponding to the QTL were accumulated across all the bootstrap samples, for comparison to the test statistic represented by the genetic variance of the SNP window identified in the analysis of the real data. If just 1 bootstrap statistic from the 1,000 simulated exceeded the test statistic from the real data, the comparison-wise p-value was determined to be 0.001<p<0.002. In addition, multiple testing was taken in to account using the probability of false positives (PFP) as in Fernando Haplotype analysis was performed for the chromosomal regions where multiple candidate SNPs were located. The haplotype blocks were identified using Haploview (Ver 4.1) Sus Scrofa Build 9 of pig Ensembl (http://www.ensembl.org/Sus_scrofa/Info/Index). The QTL location for relevant traits and the physical positions of microsatellite markers retrieved from ArkDB (http://www.thearkdb.org) and PigQTL  were integrated, to obtain connections between QTL and candidate regions found from GWA studies.For regions harboring the SNP with the highest model frequency, gene search was performed via http://david.abcc.ncifcrf.gov).Gene ontology analysis was performed to extract the functional annotation clustering using an online site called DAVID Click here for additional data file.Figure S2Proportion of genetic variance explained by each window of 5 consecutive SNP markers across the genome for last rib backfat, which was used to determine the candidate genome regions surrounding the significant SNPs. The X-axis is SNP marker position in genome order, and the Y-axis represents accumulative genetic variance of 5-SNP window Click here for additional data file.Figure S3The SNP model frequency plots for assessing associations between markers and 10th rib backfat. The X-axis is SNP marker position in genome order, and the Y-axis represents model frequency (0.10 was considered as threshold for 10th rib back fat here). Different colors represent SNPs on different chromosomes from SSC1 to X and unmapped markers.(0.08 MB TIF)Click here for additional data file.Figure S4The plots of model frequency of SNPs for assessing the associations between the markers and 10th rib loin muscle area. The X-axis is SNP marker position in genome order, and the Y-axis represents model frequency (0.10 was considered as threshold for 10th rib loin muscle area here). Different colors represent SNPs on different chromosomes from SSC1 to X and unmapped markers.(0.10 MB TIF)Click here for additional data file.Figure S5The plots of model frequency of SNPs for assessing the associations between the markers and overall leg action. The X-axis is SNP marker position in genome order, and the Y-axis represents model frequency . Different colors represent SNPs on different chromosomes from SSC1 to X and unmapped markers.(0.10 MB TIF)Click here for additional data file.Figure S6Proportion of genetic variance explained by each window of 5 SNP consecutive markers across the genome for body conformation traits, which was used to determine the candidate genome regions surrounding the significant SNPs. The body conformation traits are a) body length; b) body depth; c) body width; d) rib shape and e) Hip structure. The X-axis is SNP marker position in genome order, and the Y-axis represents accumulative genetic variance of 5-SNP window Click here for additional data file.Figure S7Proportion of genetic variance explained by each window of 5 consecutive SNP markers across the genome for feet and leg structure soundness traits, which was used to determine the candidate genome regions surrounding the significant SNPs. The feet and leg structure soundness traits are a) front leg pastern; b) rear leg pastern; c) front buck knee; d) front feet size; e) rear feet size; f) front uneven toes and g) rear uneven toes. The X-axis is SNP marker position in genome order, and the Y-axis represents accumulative genetic variance of 5-SNP window Click here for additional data file.Table S1The description of the 17 analyzed traits of body conformation, feet and leg structure and overall leg action.(0.04 MB DOC)Click here for additional data file.Table S2The overall statistics about population means, estimated heritability and the genetic parameters of Bayes C analyses for the traits.(0.05 MB DOC)Click here for additional data file.Table S3The overall statistics on average interval between SNPs and SNP distribution of porcine 60K SNP array.(0.05 MB DOC)Click here for additional data file.Table S4The detail information about candidate regions and the most significant SNPs associated with last rib backfat.(0.05 MB DOC)Click here for additional data file.Table S5The detail information about the putative candidate regions and the most significant SNPs associated with 10th rib backfat.(0.05 MB DOC)Click here for additional data file.Table S6The detail information about the putative candidate regions and the most significant SNPs associated with 10th rib loin muscle area.(0.05 MB DOC)Click here for additional data file.Table S7The detail information about candidate regions and the most significant SNPs associated with body conformation traits.(0.09 MB DOC)Click here for additional data file.Table S8The detail information about the putative candidate regions and the most significant SNPs associated with overall leg action.(0.05 MB DOC)Click here for additional data file.Table S9The detail information about candidate regions and the most significant SNPs associated with feet and leg structural soundness traits.(0.19 MB DOC)Click here for additional data file.


Literature data about glycosides from sponges  are reviewed. Structural diversity, biological activities, taxonomic distribution and biological functions of these natural products are discussed. Triterpene and steroid glycosides are known as widespread secondary metabolites in terrestrial higher plants. Although they are comparatively rare chemical constituents in the animal kingdom, numerous marine “animal” triterpene glycosides were also isolated from sea cucumbers , in which they have been found in all the orders of this class of the phylum Echinodermata and possess a strict taxonomical specificity ,3,4,5,6.Melophlus sarasinorum (family Ancorinidae) is a sponge widely distributed in the Indo-West Pacific region including Great Barrier Reef, Indonesia, Palau Islands, etc. It is also known in literature as Asteropus sarasinorum, Melophlus isis and Stellettinopsis isis as junior synonymic names of the same species -Bottom Hunter labeled C5a to its receptor (IC50 > 10 μM). Eryloside E shows also an immunosuppressive activity (EC50 of 1.3 μg/mL), its immunosuppressive effect was specific and not caused by general cytotoxicity indane core with a trans-syn-trans ring junction were first reported from the Fijian collection of the sponge Jaspis stellifera (the family Ancorinidae) . Co. Coe]ind28 μg/mL . Two addectively .Asteropus sp. (the family Ancorinidae) was used by Schmitz’s group to isolate a series of triterpene galactosides, pouosides A–E (78–82) from the animals collected in Pou Bay of Truk Lagoon in 1979 ..156 [R,3162), was isolated from the sponge Oceanapia phillipensis [162 was also suggested to be a product of enantiodivergent biosynthesis [163) from the sponge Calyx sp. [164), a unique natural compound of hybrid alkaloidal sphingolipid, was also isolated from the marine sponge Oceanapia sp. [Another related bipolar glycosylated lipid, oceanapiside  were named as erylusamines A–E (165–169). Unusual structural features of these compounds include two polar termini of molecules: one end linked with tetrasaccharide moiety and another one, bearing substituted cadaverine moiety. It was shown that erylusamines are potent IL-6 receptor antagonists , erylusine (171), and erylusidine (172) were isolated from the Red Sea sponge Erylus cf. lendenfeldi. Like erylusamines, these unique bipolar lipids contain asymmetrically located carbonyl in a long hydrocarbon chain, two polar termini and partly acetylated trisaccharide, but not tetrasaccharide as in erylusamines carbohydrate chains [Similar unusual secondary metabolites erylusamine TA (e chains . Sugars 1 (1) as prominent constituent in Melophlus sarasinorum, collected in different geographical areas from the North-Western Australia to Guam. Sarasinoside A3 (3) was isolated from M. sarasinorum collected off Palau Islands, Guam Island, Sulawesi Island and the North-Western Coast of Australia, sarasinoside A2 (2)—near Palau Island and from North-Western Coast of Australia, etc. The similar distribution was indicated in relation of erylosides of Erylus formosus, from which formoside (24) and eryloside F (25) were isolated using collections carried out off the Bahamas and Puerto Morelos in the Gulf of Mexico.As a rule, triterpene and steroid glycosides present in sponges as very complicated mixtures and their separation is difficult. This may cause some problems in the use of these substances as chemotaxonomic markers, because investigators may sometimes isolate only a part of components from complicated glycoside fraction while another group of investigators may isolate another part of the components. As result, the comparison of these data may be not correct. On the other hand, distinct structural series of glycosides are evidently distributed in different and concrete taxa of the animals. Distribution of tetracyclic triterpene and steroid glycosides is mainly limited to two orders (Astrophorida and Poecilosclerida) with a few exceptions, by several families and species within them without a strict dependence on ecological and geographical factors. For example, taxonomic distribution of tetracyclic triterpene glycosides, given in Lipastrotethya sp.  collected in Micronesia [As we already have noted, recently, a Korean group reported on the isolation of a series of sarasinosides from the sponge cronesia . However33) from Erylus formosus collected near Puerto Morelos and from E. nobilis collected off Jeju Island, the South Korea), some genera are characterized by the presence of close related glycosides, as it may be exemplified by erylosides found in different species of Erylus, collected off three Oceans . All the glycosides contain 14-carboxy- or 14-nor-methyl-lanostane aglycones, sometimes additionally dealkylated at C-4 and alkylated in side chains. General architectures of carbohydrate chains from a series of glycosides isolated from Erylus spp. are frequently similar to each other, but, as a rule, exact structures are specific.Although findings the same glycosides in different sponge species, belonging to the same genus is very rare  were found in Asteropus sp. (=Melophlus sp.) together with sarasinosides [Therefore, triterpene, steroid and other glycosides may be considered as taxonomic markers specific on species and sometimes on genus or subgenus levels. However, practical usage of this character for improvement of sponge taxonomy seems to be rather difficult not only due to the presence of very complicated mixtures of close related glycosides, but also seasonal and ecological variability in content of some glycoside components in glycoside fractions. For instance, Kubanek et al.  indicateinosides ,48.de novo biosynthesis of triterpene systems may take place in sponges.Such findings of different squalene cyclizations indicate that Glycosides of non-isoprenoid aglycones, including glycosylated macrolides, and nontetracyclic triterpene glycosides were also found in the same orders of Demospongiae as triterpene and steroid glycosides, in majority cases in Astrophorida and Poecilosclerida.Erylus placenta, from which sokodosides [It confirms the presence of specific glycosyltranferases, participating in construction of oligoglycoside chains in a group of close related representatives of these taxa. Moreover, the same species contain sometimes triterpene glycosides and oligoglycosylated bipolar lipids as may be exemplified by odosides  and erylodosides ,95 were et al. [E. formosus collected off the Bahamas and the Southern Florida deters predation by the fish Thalassoma bifasciatum. Formoside (24) and formoside B (26), having four sugars in carbohydrate chain, were less potent than the non-separated fraction of hexaosides, containing the same penasterol as an aglycone. The total glycoside fraction was more active than any subfraction obtained at its separation. Thus, glycosides may play a defensive role against predatory fish.Sponges have always been an important component of coral reef communities . Kubaneket al.  have fouErylus formosus and Ectyoplasia ferox, showed influence of these substances not only on fish feeding, but also on attachment by biofilm-forming bacteria, fouling by invertebrates and algae as well as on overgrowth by neighboring sponges  in field and laboratory assays. The multiple ecological functions of these substances were established [18) [1 (1) [101) [The studies on glycosides from two the Caribbean sponges ablished ,100,101.hed [18) , some erhed [18) ,28,29, shed [18) , caminoshed [18) ,71 and ohed [18) , make alhed [18) ,17,27,47) [1 (1) , pachast1) [101) , and rhi1) [101) .Danio rerio) rejected artificial diet with the sponge chemical defensive compounds. Transcripts, made from a zebra fish cDNA library were expressed in Xenopus laevis oocytes and tested for chemoreceptor activation via electrophysiology. Electrophysiological response to formoside and ectyoplasides A and B was indicated [Ecological activities of the glycosides may be affected by small differences in their molecular structures. There was not found a significant excretion of some triterpene glycosides in sea water, but concentration of the glycosides on surface of sponge species was high. Hence, enemies and competitors of these sponges may be deterred by surface contact with triterpene glycosides rather than by contacts through the sea water . Howeverndicated . In contndicated .An influence of sponge glycosides on membrane properties of host cells is an obvious peculiarity of biological functions of some glycosides, in particular, atypical glycolipids, including unusual bipolar compounds. Structures of the latter compounds suggest their inclusion into lipid bilayer with location of polar heads on the both its inner and outer surfaces. Many of this type of compounds demonstrate antimicrobial properties ,81,87,90About 170 unusual sponge glycosides were isolated, in majority cases from sponges belonging to species of orders Astrophorida and Poecilosclerida. The presence of some or other glycoside series, especially triterpene and steroid, is a characteristic feature of a few taxonomic groups (up to date they were indicated in about two dozens of species) within the class Demospongiae, but not of the class as a whole. It seems to be probable that modern improvement in isolation and spectral techniques will facilitate the isolation of many new glycoside compounds from above-mentioned and other sponge taxa. The distribution and biological activities of these compounds suggest their parallel origin and evolution as defensive agents in several taxa of the class.Chemically, these compounds are very diverse and quite different in the structures of the both aglycone and carbohydrate moieties, when compared with other glycosides of marine or terrestrial origin. Different sponge glycosides have aglycones of a great diversity with alicyclic bi, tri- or tetracyclic aglycones. In many cases, they have previously unknown skeleton systems of aglycones. There are not only mono- and biosides, but also oligoglycosides among compounds, isolated and studied. A large set of monosaccharides (more than two dozens) was found, all are in pyranose forms and the majority is of D-series, although L-sugars were also identified. Such rare sugars as L-quinovose, 6-deoxy-β-L-altropyranose and 6-deoxy-D-talose along with many common monosaccharides were found in carbohydrate moieties of sponge glycosides. Frequently, sponge glycosides contain acylated monosaccharide units. Sponge glycosides demonstrate not only deterrent and ichthyotoxic properties, but also antiprotozoal, antifungal, antitumor and other activities, sometimes very potent and probably connected with their defensive, antifouling, and allelopathic biological functions. Such sponge glycosides as glycosylated acyclic derivatives and unusual bipolar lipids are presumably membrane constituents of sponge cells, modulating their membrane properties.


Cyprinus carpio) is an important aquaculture fish worldwide but only limited single nucleotide polymorphism (SNP) markers are characterized from expressed sequence tags (ESTs) in this species. In this study, 1487 putative SNPs were bioinformatically mined from 14,066 online ESTs mainly from the European common carp, with the occurrence rate of about one SNP every 173 bp. One hundred and twenty-one of these SNPs were selected for validation using PCR fragment sequencing, and 48 out of 81 primers could amplify the expected fragments in the Chinese common carp genome. Only 26 (21.5%) putative SNPs were validated, however, 508 new SNPs and 68 indels were identified. The ratios of transitions to transversions were 1.77 for exon SNPs and 1.05 for intron SNPs. All the 23 SNPs selected for population tests were polymorphic, with the observed heterozygosity (Ho) ranging from 0.053 to 0.526 (mean 0.262), polymorphism information content (PIC) from 0.095 to 0.357 (mean 0.246), and 21 SNPs were in Hardy–Weinberg equilibrium. These results suggest that different common carp populations with geographic isolation have significant genetic variation at the SNP level, and these new EST-SNP markers are readily available for genetics and breeding studies in common carp.The common carp ( Cyprinus carpio) is an important freshwater species for aquaculture and has been domesticated for ca. 4000 years [The common carp . The number of EST sequences per contig ranged from two to 60 (mean 3.4). Based on searches of the 2135 contigs, a total of 49,476 variations were detected, including 40,749 nucleotide substitutions and 8509 indels. After the additional filtering criterion was applied, a total of 1487 putative SNPs were obtained from 303 contigs, with an average frequency of one SNP per 173 bp of contig sequence, which was greater than frequencies reported in Atlantic salmon (one SNP per 614 bp) , brown tca. 27,010 bp was amplified and sequenced, of which 13,213 bp were exons and 13,797 bp were introns. Finally, 121 SNPs were identified from 13,213 bp of exons, and 26 (21.5%) of these putative SNPs were verified in a Chinese common carp population. On the other hand, a large number of new SNPs, which were not detected in the SNP discovery by data mining from the online EST sequences, were identified during the validation. These new SNPs included 202 SNPs and 10 indels in exons, and 306 SNPs and 58 indels in introns  was 1.77 for exon SNPs, 1.05 for intron SNPs and 1.31 for SNPs in complete genomic sequences. These ratios are similar to those reported in turbot (1.885) [s/tv = 0.95) and sockeye salmon (ts/tv = 0.98) [s/tv ratios were significantly lower, and only close to the ratio estimated from the intron regions of common carp ESTs (1.05) in this study. This discrepancy in ts/tv ratios may suggest biased codon usage or substitution rate because fishes from different phylogenetic units may be subject to different selection pressures. Comparison of exon and intron SNPs revealed extreme differences in almost all aspects, including SNP frequency, ts/tv value, and commonest and rarest SNP types  and 82 were transversions , while in intron SNPs there were 154 transitions and 149 transversions . Two SNP (1.885) , chinookNP types , Table 2p > 0.05), and 2 SNPs deviated significantly from HWE after Bonferroni corrections (p < 0.00217). Linkage disequilibrium (LD) was detected between 19 pairs of the SNP loci after Bonferroni corrections (p < 0.00020).Twenty-three SNPs were chosen to test the polymorphisms in 38 unrelated individuals from a Yangtze River common carp population. All these loci were found to be polymorphic, with frequencies of minor allele ranging from 0.053 to 0.368  were downloaded from GenBank, DNA Databank of Japan (DDBJ) and European Molecular Biology Laboratory (EMBL) databases. Sequences of length less than 100 bases were removed, and the remaining 14,066 EST sequences were used for further processing. Overlapping sequences were identified by cluster analysis using the UIcluster v. 2.02 software . After tn = 4, Wuyuan (purse) red carp n = 2, and Xingguo red carp n = 2). A population of common carp with 38 unrelated individuals from the Zhangdu Lake of the Yangtze River, Wuhan, China, was used for polymorphism analysis of selected valid SNPs. Genome DNA was extracted from fin clips following the standard phenol–chloroform protocol [One hundred and ninety-seven SNPs  from 73 protocol .Taq polymerase , 3.5 μL of forward and reverse primer mixture (2.5 mmol/L), 3.5 μL of template DNA and 44 μL of sterile water, using the following program: 94 °C for 5 min, followed by 35 cycles of 94 °C for 40 s, optimum annealing temperature for 40 s, and 72 °C for 60 s, and a final extension of 72 °C for 7 min. PCR amplicons were purified using the Gel Extraction System B , according to the manufacturer’s instructions. Purified PCR products were cloned into the PMD18-T vector and sequenced on an ABI 3730XL machine . For SNP genotyping, PCR-restricted fragment length polymorphism [PCR amplifications were carried out in a thermal cycler  in 60 μL reaction volumes containing 6 μL of 10× reaction buffer, 2 μL of dNTP (10 mmol/l), 2 U of morphism , direct morphism  were carAlignment of the sequenced fragments was performed using ClustalX v. 1.81 , and put-resolution genetic maps, QTL (quantitative trait locus) identification, and maker assisted breeding in the common carp.A total 1487 putative SNPs were identified by mining from online common carp EST sequences. Approximately 320 of the putative SNPs are expected to be true in the Chinese common carp as estimated based on the validation rate of 21.5% in this study. In spite of the low validation rate, large numbers of new SNPs were identified in the Chinese common carp. Of the 23 SNPs tested, all loci were polymorphic in a Yangtze River population with moderate diversity. These results indicate that the occurrence of SNPs varies significantly between European and Chinese populations, and these characterized SNPs are valuable resources for population genetics, high