Rice restorer lines play an important role in three-line hybrid rice production. Previous research based on molecular tagging has suggested that the restorer lines used widely today have narrow genetic backgrounds. However, patterns of genetic variation at a genome-wide scale in these restorer lines remain largely unknown. The present study performed re-sequencing and genome-wide variation analysis of three important representative restorer lines, namely, IR24, MH63, and SH527, using the Solexa sequencing technology. With the genomic sequence of the Indica cultivar 9311 as the reference, the following genetic features were identified: 267,383 single-nucleotide polymorphisms (SNPs), 52,847 insertion/deletion polymorphisms (InDels), and 3,286 structural variations (SVs) in the genome of IR24; 288,764 SNPs, 59,658 InDels, and 3,226 SVs in MH63; and 259,862 SNPs, 55,500 InDels, and 3,127 SVs in SH527. Variations between samples were also determined by comparative analysis of authentic collections of SNPs, InDels, and SVs, and were functionally annotated. Furthermore, variations in several important genes were also surveyed by alignment analysis in these lines. Our results suggest that genetic variations among these lines, although far lower than those reported in the landrace population, are greater than expected, indicating a complicated genetic basis for the phenotypic diversity of the restorer lines. Identification of genome-wide variation and pattern analysis among the restorer lines will facilitate future genetic studies and the molecular improvement of hybrid rice. Oryza sativa L.) is one of the most important food crops. In 1973, the field production of Indica hybrid rice succeeded when Chinese rice breeders completed the three-line breeding system 2 was soon developed for hybrid rice cultivation, greatly increasing rice yield in China. In the three-line breeding system, the cytoplasmic male sterility (CMS) line is crossed with the restorer line to produce the F1 hybrid rice, and with the maintainer line for self-reproduction. The restorer line is widely considered as being key to further improve the resistance, yield, quality, and heterosis of hybrid rice As the main staple food for more than half of the world's population, rice . In total, an effective depth of 30× coverage was achieved, with an average of 10× for each restorer line . The alignment of reads was used to build consensus genome sequences for each rice accession. Furthermore, approximately 10.78 G high-quality raw databases were aligned with the reference sequence of cultivar 9311 using SOAPaligner rer line . The reshttp://rice.sicau.edu.cn/re-sequencing/variation/9311.rar). These outcomes resulted in a non-redundant collection of 568,787 SNPs after excluding the shared SNPs of each sample by synteny analysis  may reflect the genetic improvement of the recent restorer lines (such as SH527 and MH63) from older lines (such as IR24), an analysis of the variations and their distributions among the samples was performed. Synteny analysis of variations revealed 81,956 shared SNPs, 2,799 different SNPs, 24,053 shared InDels, and 860 different InDels between IR24 and MH63; 89,589 shared SNPs, 3,998 different SNPs, 26,936 shared InDels, and 634 different InDels between IR24 and SH527; and 129,364 shared SNPs, 2,927 different SNPs, 35,066 shared InDels, and 613 different InDels between MH63 and SH527. The distributions of these variations on each chromosome are showed in The SNPs in coding regions were analyzed to gain further insights into the potential functional effects of the detected SNPs . BetweenThree hundred thirty-one large-effect SNPs that were expected to affect the integrity of encoded proteins were also identified. These included changes introduced by premature termination codons , elimination of translation initiation sites , and replacement of nonsense with sense codons . Of these large-effect SNPs, only 10 SNPs  were observed from the different SNPs; the rest were from the shared SNPs .GO and PFAM analyses were further carried out for the shared and different SNPs (InDels) in genes between samples to explore gene functions. In both the shared and different SNPs (InDels), the top GOs were protein kinase activity, nucleic acid binding, protein binding, DNA binding, and catalytic activity  and 6. GPibXa1Pi9Xa21Xa26Bph14ALKqSW5GS3Gn1aHTD2GW2EUI1Rf1aRf4Several important rice genes related to yield, quality, resistance, and development processes were subjected to molecular cloning and functional analysis. Natural variations among the genes, which might explain the phenotypic differences of the sequenced sample, were then evaluated. A large number of SNPs  were detIn the present study, we conducted re-sequencing and genome-wide variation analysis of three famous representative restorer lines, namely IR24, MH63, and SH527, with the aim of uncover genetic variation at a genome-wide scale by using the Solexa sequencing technology. Identification of genome-wide SNPs, InDels, and SVs, as well as pattern analysis of restorer lines can provide valuable resources for future genetic studies and the molecular improvement of hybrid rice.NipponbareNipponbare is 382,150,945, of which the effective size is 372,089,805. When the Nipponbare genome was used as the reference, the number of SNPs detected was noticeably higher (data not shown). However, quality of original sequence data such as mapped bases, sequencing depth, and coverage decreased, rendering the SNP data less reliable. Given that genetic variations between restorer lines, not the japonica and indica rice varieties, underlie the mechanism of their phenotypic differences, the 9311 genome sequence was then used as the only reference for detecting SNPs, InDels, and SVs, and for assembling the consensus sequence to exclude the large amount of background variations that account for differences between the japonica and indica rice varieties.We firstly used the 9311 −4, which is lower than that for other species, such as pandas Interestingly, approximately 76,000, 71,000, and 76,000 heterozygous SNPs in IR24, MH63, and SH527, respectively, were identified throughout the whole rice genome, leading to an estimated heterozygosity rate of approximately 1.98–2.0×10Here we report variations over the whole genome among elite rice restorer lines. Our results indicate that genetic variations among these lines, although far lower than those reported for a more diverse landrace population 1 hybrid rice. The three elite restorer lines, together with the F1 hybrid rice, were then planted in the following year for phenotypic evaluation and field test. All the restorer lines and the F1 hybrid rice were planted across 20 lines, with three replicates totaling 12 plants in each line. Eight middle plants of the 10 middle lines were surveyed, and data were recorded for statistical analysis. To compare the field performances of these elite restorer lines, we used a One-way ANOVA and LSD's test of DPS Software (http://www.chinadps.net/index.htm). To compare the contribution of restorer lines to their hybrids' field performances, we used a Two-way ANOVA and LSD's test of DPS Software (http://www.chinadps.net/index.htm) Seedlings of IR24, MH63, and SH527 and six other widely used CMS lines, namely, G3A, Zhongjiu A, II-32A, G46A, 92A, and Chuangu A, were planted in the experimental field of the Rice Research Institute, Sichuan Agricultural University, Wenjiang. When they reached the flowering stage, these three restorer lines were crossed with the six CMS lines to obtain the FTotal genomic DNA was extracted from the leaf tissues of one individual for each line using a DNeasy Plant Mini Kit (Qiagen). The DNA of each line was then randomly fragmented. After electrophoresis, DNA fragments of the desired length were gel-purified. Adapter ligation and DNA cluster preparation were performed and subjected to Solexa sequencing.The raw pair-end (PE) sequencing reads were aligned to the 9311 reference genome sequence using SOAPaligner Based on the alignment results, and taking into consideration the analysis of data characters, sequencing quality, and other factors influencing the experiments, a Bayesian model was applied to calculate the probability of genotypes with the actual data. The genotype with the highest probability was selected as the genotype of the sequencing individual at a specific locus, and a quality value was designated accordingly to reveal the accuracy of the genotype. Polymorphic loci against the reference sequence were selected from the consensus sequence and then filtered under certain requirements  using SOAPsnp According to the principle of PE sequencing, under normal situations, one read of PE should be aligned to the forward sequence and another should be aligned to the reverse. The distance between the two aligned positions at the reference should be in accordance with the insert size. Thus, the alignment of the two paired reads to the genome is regarded to be of normal direction and appropriate span. If the direction or span of the alignments of the two paired reads is different from that expected, then the region might have SVs. Abnormal PE alignments observed in our analysis were further analyzed by clustering and compared with previously defined SVs. In this manner, the SVs were detected using SOAPsv The localization of SNPs in coding regions, noncoding regions, start codons, stop codons, and splice sites were based on the annotation of gene models provided by the Rice Genome Sequencing Project of 9311 The frequencies of SNPs, InDels, and SVs for each sample were plotted over a 100 kb sliding window with a step size of 50 kb along each chromosome to explore the genomic distribution of DNA polymorphism in these lines The SNPs/InDels/SVs detected for each individual line were further compared between samples to identify the shared and unique SNP/InDel loci. Only those loci for which at least one effective sequence read was mapped for every individual were selected for comparison. A phylogenetic tree was constructed using the MEGA4 software


The release of the porcine genome sequence offers great perspectives for Pig genetics and genomics, and more generally will contribute to the understanding of mammalian genome biology and evolution. The process of producing a complete genome sequence of high quality, while facilitated by high-throughput sequencing technologies, remains a difficult task. The porcine genome was sequenced using a combination of a hierarchical shotgun strategy and data generated with whole genome shotgun. In addition to the BAC contig map used for the clone-by-clone approach, genomic mapping resources for the pig include two radiation hybrid (RH) panels at two different resolutions. These two panels have been used extensively for the physical mapping of pig genes and markers prior to the availability of the pig genome sequence.In order to contribute to the assembly of the pig genome, we genotyped the two radiation hybrid (RH) panels with a SNP array (the Illumina porcineSNP60 array) and produced high density physical RH maps for each pig autosome. We first present the methods developed to obtain high density RH maps with 38,379 SNPs from the SNP array genotyping. We then show how they were useful to identify problems in a draft of the pig genome assembly, and how the RH maps enabled the problems to be corrected in the porcine genome sequence. Finally, we used the RH maps to predict the position of 2,703 SNPs and 1,328 scaffolds currently unplaced on the porcine genome assembly.A complete process, from genotyping of a high density SNP array on RH panels, to the construction of genome-wide high density RH maps, and finally their exploitation for validating and improving a genome assembly is presented here. The study includes the cross-validation of RH based findings with independent information from genetic data and comparative mapping with the Human genome. Several additional resources are also provided, in particular the predicted genomic location of currently unplaced SNPs and associated scaffolds summing up to a total of 72 megabases, that can be useful for the exploitation of the pig genome assembly. Sus scrofa domestica) genome sequencing project used high resolution physical maps based on a combination of restriction fingerprint maps and BAC end sequencing together with state-of-the-art Human-Pig comparative maps [e.g.[With the important reduction in cost of sequencing, whole genome sequencing data are becoming much easier to produce. However sequencing projects still need medium to long range information to anchor scaffolds on chromosomes and produce high quality reference maps . The Pigive maps ,3. Althoive maps -11. In tive maps  and 12,0ive maps , with esive maps . These pive maps -16 as weaps [e.g.-23) radiRecently, a high density SNP array was produced for the pig , allowinWe describe here genome-wide high resolution RH maps of the pig autosomes constructed by genotyping the two pig RH panels with the Illumina porcineSNP60 array. The construction of RH maps in this context presented two challenges: the accurate genotype calling from the raw intensity data and the construction of chromosome maps with thousands of markers. Because answering these questions is error prone, we validated the map ordering using genotyping data in families. Once confident in the validity of our RH maps, they were compared to a draft version of the pig genome assembly (build9) which led to propose improvements that were included in the build10 assembly. Finally we show the added value of RH maps for the mapping of unassigned sequences: we propose likely positions for 1328 unplaced scaffolds totaling 72 megabases of genome sequence.The two pig radiation hybrid panels ,13, makiThe main objective of this study was to compare the SNP order as defined by the RH maps to the one defined by a preliminary version of the pig genome assembly in an attempt to identify discrepancies that could pinpoints some assembly problems. We provide details on the methods used to build RH maps in the Methods section.Briefly, for each autosome, the analysis was done in three separate steps:1.We confirmed the linkage between markers, using RH data alone, and remove unlinked markers. This step led to the removal of 570 SNPs.2.We built comparative RH maps  with allrobust maps[3.We extracted a subset of markers from the initial RH maps for which the order was strongly supported by RH data. This procedure removed about 3,000 SNPs that could not be confidently ordered and led to RH bust maps comprisiThe construction of robust maps follows the same rationale as the construction of framework maps (see Methods). Figure i.e. RH maps were generally more parsimonious than the assembly map in the number of recombination events needed to explain the genetic data. This suggested that the build9 assembly contained errors that could possibly be corrected using our RH maps.z To validate the orders of the RH maps, we used genetic data from 263 small half-sibs families, forming a total of 728 meioses Table . Each faOur analysis of recombination on genetic data allows us to estimate the probability that a crossing over occurs in a particular interval on a map. We used this information to examine in more details regions involved in the differences in genetic length between the assembly and the RH maps: we focused on intervals exhibiting both a discrepancy (a breakpoint) between the assembly and the RH map and a high recombination rate. We distinguished several types of differences. First, we observed cases where large differences in placement were found for a single SNP or a small number of SNPs. This was frequently seen on chromosome 1, the largest chromosome by far, and accounts for 9 cM of the 22 cM difference between genetic lengths of the assembly and the RH order for this chromosome. Because this problem affects typically a single SNP at a time, it can be due to an error in the mapping of the SNP sequence to the chromosome rather than an error in the assembly. Second, we observed high recombination rates for some regions located at the end of the chromosome while they were typically placed within the chromosome on the RH map. We know that in the assembly process sequences assigned to a chromosome but with no strong information on the localization were placed at the end by default. Finally, we observed some differences for large chromosome segments probably indicating problems in the BAC-based physical map. This is the case for example on chromosome 15 where the large discrepancies come together with large increases in recombination rates in the corresponding region of build9 Figure .a.e.g. they did not involve breaking contigs or scaffolds). As an illustration of such an improvement, Figure Based on these comparisons we listed a set of proposed improvements to the pig genome assembly. Most of these improvements were the basis of changes in the pig genome assembly, when they were compatible with sequencing data  of the pig genome sequence, this rate met our expectations. We initially conducted this analysis on the build9 genome assembly. The released pig genome has improved a lot and 3706 of the uSNPs with RH vectors are now located on the pig genome. This provided us with the possibility to evaluate the performance of our prediction. We found that, among these 3706 uSNPs, 2.6% were assigned to the wrong chromosome by our procedure and 2.4% were assigned a position more than 1Mb from their true location. Thus, our procedure comes with an error rate of about 5%, which seems reasonable. Note that this does not imply that positioning of SNPs on robust maps come with a similar error rate. First, for these SNPs we have a concordance between the position given by the genome sequence (information missing for uSNPs) and the RH data. Second, the procedure to establish robust maps is much less prone to errors than the simple approach used for uSNPs. Looking in more details at the precision of our localization , this lack of linkage can be due either to wrong assignments of SNP sequences to chromosomes or to genotype calling errors. Applying our prediction procedure provided mixed results. First, only 44% (252) of the 570 SNPs could be assigned a chromosome, much lower than the 90% success rate obtained on the uSNPs. Among the 252 SNPs, 44% (117) were predicted a different chromosome than on build9. This figure goes down to 19% with build10, which indicates some success in our prediction. However, if we consider this proportion as an error rate, like we did for uSNPs, then it is much higher than for uSNPs (2.6%). This suggests that genotype calling errors contribute significantly to the linkage problem initially identified and that the prediction for these SNPs is not reliable enough for assignment.Among the 2,703 SNPs for which we could assign a position on the pig genome, we were able to align 1,947 to 1,428 scaffolds present in the pig genome sequence but currently unassigned. In total, this represents 79 Megabases of pig genome sequence. As a first validation, we noticed that essentially all SNPs assigned to a given scaffold had consistent predicted localization on the pig genome (same chromosome and same position). Only one SNP on a single scaffold (chrU_scaffold3109) had a different chromosome than the 3 other SNPs of this scaffold. The SNP was therefore removed from the analysis. We note however that there is a chance that this scaffold is chimeric as its sequence aligns to two distant regions of Human chromosome 1 (at 6 and 206 Mb). Being aware of the potential errors in localization mentioned above, we used comparative genomics to validate our predictions. We aligned the candidate scaffolds to the Human genome and identified the surrounding homologous region on the pig genome from conserved syntenies predicted by the Narcisse comparative genome browser . We clasdiscordant). These adds up to 7% of the total number of scaffolds and 8.7% of the total sequence length. Given that there might also be errors in the comparative genomics prediction, these values can be considered quite close to the estimated 5% error rate above.scaffolds for which the position predicted by comparative genomics did not match the same chromosome as our prediction (noinfo) or for which the corresponding human genome sequence has no identified homologous region on the pig genome (nosynt). There is insufficient evidence to validate or invalidate our predicted position for these scaffolds. These are typically small scaffolds as, while they represent 27% of the number of unassigned scaffolds considered, they only cover 7.8% of the sum of their sequence lengths  and more .scaffolds with the same predicted chromosome by RH maps and comparative genomics. Within these scaffolds, we distinguish between those that have predicted positions separated by less than 1 megabase (tentative scaffolds), we can map more than 72 megabases of unassigned scaffolds on the pig genome. If we consider only scaffolds with the most robust prediction , they still add up to more than 53 megabases. Predicted positions and category of the tentative scaffolds are given as in Additional file Overall, if we consider scaffolds that are not discordant . The predicted localization for this scaffold, based on SNPs predicted positions, is on chromosome 6, between 91.1 and 91.9 Mb. Within this region, the pig genome has a large gap, identified as a 50Kb stretch of ’N’ nucleotides. We verified that scaffold GL894031 aligned on the putative syntenic region of the Human genome and constructed a local RH map of the region including the scaffold SNPs . We discuss here what we believe are important aspects of the current study: genotyping an RH panel with a high density SNP array, construction of high (ultimate) resolution genome-wide RH maps and finally the analysis of discrepancies between maps and assemblies.In this study, we present what we believe is the first example of constructing genome-wide high resolution RH maps from SNP array data. Our main motivation for building these maps was to provide independent data to analyze and validate the pig genome sequence. Before proposing improvements to the pig genome, we carefully validated our results using information on segregation data in pig families. Eventually, this made our inference more robust and allowed us to contribute significant improvements to the pig genome: we proposed modifications for the largest discrepancies and the assembly was modified when this did not contradict sequence data . This can also be seen as using all other data points when calling a particular SNP genotype in a single clone. This genotype calling procedure from intensity data obtained by SNP array genotyping can certainly be improved in future studies. In particular, it would be interesting to try to separate the different effects of clones, SNPs and arrays on the observed intensities in order to provide better prediction of the genotypes, possibly reducing missing data and genotyping error rates. This would require the development of new statistical methods and the genotyping of at least some clones on multiple arrays.resolution expressed as a Kilobases to centiRay ratio. Based on our RH maps, we estimate the resolution of the IMpRH panel at 8.6 Kb/cR and of the IMNpRH2 panel at 5.3 Kb/cR, whereas previous estimates were respectively 35.4 Kb/cR and 12.5 Kb/cR [i.e. not the map of smallest length (in cR). Again, this has the consequence of decreasing the Kb/cR ratio.In RH mapping, the precision of a map or a mapping tool is generally characterized by the .5 Kb/cR . This diThe resolution can however be understood in a broader sense than this simple Kb to cR ratio  between markers that are to be ordered. For example in the case of ordering the scaffolds of a genome assembly, the N50 or N90 scaffold size could be the relevant target resolution. The resolution of an RH panel depends on the panel size and the resolution parameter expressed in Kilobases per centiRay. This parameter is related to the radiation dose (expressed in Rads) but through a process too complex to be modeled so its value can only be guessed from previous studies. However, estimates obtained from the literature must be taken with caution. First they were most likely obtained in other species. Furthermore, as we have shown, these estimates depend on the number of markers and the mapping methods used. Overall, adjusting the resolution through the radiation dose is going to be imprecise. In previous RH panels construction experiments, the panel size was purposely limited to 90 clones because of PCR genotyping where a single marker is genotyped for all clones disposed on a 96-well plate (with wells reserved for control samples). SNP array genotyping however proceeds by genotyping all SNPs on a single clone and therefore does not impose such a design so panel sizes can be made larger to increase the resolution. Given a panel size and resolution parameter, Additional file Designing an RH mapping experiment requires to define what is the desired resolution, The resolution of discrepancies directly addresses the question of the reliability of the order defined on one side by the genome map and on the other by the assembly. The construction of robust maps was precisely designed to address the reliability of RH maps . On the The X chromosome was not investigated in this study because it requires a specific analysis. First, both RH panels were constructed using male DNA hence with a single X chromosome and therefore a reduced retention in comparison to other chromosomes, with the exception of the pseudo-autosomal region which is believed to cover a small fraction of the X chromosome (∼5% ). SecondOverall there is good agreement between the current genome assembly and the robust RH maps presented here. Although the pig genome sequence is now released, we believe our RH maps can still be useful to the community. There are likely to still be ordering problems on the pig genome, as has been seen for other mammalian genomes before, and the RH information can provide the pig genetics community with valuable information to help improve further the pig genome sequence. Also, the RH maps allow the positioning of currently unassigned SNPs and sequence to the pig genome, which effectively increases the coverage of the assembly, allowing for example the mapping of significant genome-wide association signals currently positioned on an unassigned scaffold. Our RH maps have already proved to be useful to study the recombination rate patterns in the pig , by provde novo thanks to NGS techniques, producing high-resolution genome maps based on non sequence data  will be essential for the production of good quality genome assemblies. This will most likely require bypassing the limitations mentioned above, for example by substituting SNP arrays by Genotyping By Sequencing techniques and radiation hybrids by other chromosome breaking mechanisms such as genetic recombination in large samples. The methods used here can readily be applied to such datasets.The approach used for this study relies on the availability of both RH panels and a high density SNP array. For species where both tools are available, this study demonstrates that high density RH panels are very useful for providing physical evidence for the ordering genome sequence and for positioning unplaced scaffolds. However, we do not expect that it can be applied as presented here to a large number of species. Indeed, the production of an RH panel, let alone two, is a labor intensive, highly technical and therefore expensive task. High density SNP array also come with a large designing cost and will likely be available for a small number of species. While we can expect an increasing number of genomes to be sequenced, even We genotyped two RH panels with the Illumina porcineSNP60 chip. In the same experiment, one sample of pig genomic DNA and hamster genomic DNA were added as controls. Radiation hybrid samples contain only a fraction of the total genomic DNA found in a cell, as such, the genotype calling methods used on genomic DNA samples are not adapted for radiation hybrid data. We used a specific methodology for calling the RH genotypes: the principle is to identify markers for which raw signal intensities show a clear-cut difference between the negative hamster control and the positive control.We started with a data set of 59,950 SNPs for which at least 5 genotypes were initially called in the 180 radiation hybrids. We then compared raw intensities on the X and Y axes  in the hamster and averaged over RH clones Figure , left. AFor the remaining SNPs, we determined thresholds for genotype calling based on the observed distribution of raw intensities Figure , right: a priori information (here coming from the build9 assembly) and RH data as implemented in carthagene by merging the two RH datasets and the assembly dataset. Using this method guarantees that discrepancies observed between the RH map and the assembly are strongly supported by RH data, as regions were RH data are not informative keep the prior (i.e. the assembly) ordering. RH maps were built with the lkh command. On these initial RH maps 570 SNPs did not show clear evidence for linkage and were therefore removed to obtain RH maps totaling 41,729 SNPs.We first partitioned SNPs according to their assigned chromosome on assembly build9. For each autosome, we determined linkage groups among SNPs, based on RH data alone. Specifically, we used the multigroup option of the carthagene software , requirirobust maps. Briefly, the principle of the method is to obtain a set of possible maps with associated probabilities of being correct and then extract from this distribution a subset of markers that show the same ordering across maps of high probability. Specifically, we first estimated the posterior distribution of possible orders using an MCMC approach [We next applied a novel approach  to buildapproach  implemenapproach , with deapproach . In contapproach .de novo would require a very long time. We decided to use a more rapid approach, which would be good enough to provide reasonable predictions.6076 SNPs with RH vectors were not assigned to a chromosome on build9. Here we describe how we used the RH data to assign them to the pig genome and to predict their position on the pig genome assembly. The robust RH maps that we developed are composed of a subset of markers for which a position on build9 was known . Given the high number of markers, producing maps The principle of the mapping of an unassigned SNP (uSNP) on a pig chromosome is based on computing a similarity score between this SNP and all the already mapped SNPs (mSNPs). Given two SNP RH vectors (one uSNP and one mSNP), we count the number of clones which show the presence of both SNPs. We can derive a chi-square statistic for seeing N match  between the two markers, under the null hypothesis that the two markers are independent. The score used is -log10 of the pvalue corresponding to this statistic. We performed an empirical study to obtain a threshold for the score of unlinked SNPs. We sampled a mSNP on a chromosome and calculated its maximum score on another chromosome. Repeating this process a 1000 times provides a distribution of the scores of a SNP when tested against a wrong chromosome.The first step of our analysis was to assign uSNPs to pig chromosomes. To this end, we calculated the similarity score of each uSNP with all mSNPs, using RH vectors of the lower resolution panel . A uSNP All software and computer programs used in this study are freely available from the corresponding author.The authors declare that they have no competing interests.BS carried out the analysis of the data and wrote the manuscript. TF contributed to the data analysis and writing the manuscript. DZ and NI carried out the sample preparation and genotyping. DM conceived the study, coordinated the genotyping and contributed to the data analysis and drafting the manuscript. All authors read and approved the final manuscript.Radiation hybrid maps of the Illumina pig 60K SNP chip. This text file contains the position (in centiRays) of 38379 SNPs from the Illumina porcineSNP60 SNP chip on radiation hybrid maps. For each SNP, two positions are given, one on the IMpRH panel (pos.rh1)  and one on the IMNpRH2 panel (pos.rh2) .Click here for fileCharacteristics of the RH maps. This table contains detailed characteristics of the RH maps obtained for each chromosome: number of SNPs mapped, number of distinct position for each panel, resolution, average marker distance and retention fraction.Click here for filebuild9 assembly and with the build10 assembly. This file contains comprehensive pictures comparing (i) the build9 draft assembly and the RH maps and (ii) the pig genome sequence build10 and the RH maps.Detailed comparison of RH maps with the Click here for filePredicted positions on the pig genome for 2703 SNPs. This file contains a predicted chromosome and location in base pairs for 2703 SNPs currently not assigned a position on the pig genome. We estimate that 95% of these predicted positions lie at less than 1Mb of the true position, with a median at 120Kb. See details in the text.Click here for fileTentatives scaffolds (see details in the main text).Predicted positions on the pig genome for 1328 scaffolds. This file contains a predicted chromosome and location in base pairs for 1328 scaffolds currently unplaced on the pig genome. This files contains only the Click here for fileValidation of the predicted position for scaffold GL894031. This file contains figures and text presenting the validation of the predicted position for the unplaced scaffold GL894031.Click here for fileResolution of RH mapping for high density arrays. This file contains theoretical calculations on the resolution of RH panels and the design of RH experiments.Click here for fileNumber of uSNPs with multiple chromosome assignments against similarity score threshold. This file is a figure showing the evolution of the number uSNPs with multiple chromosome assignments as a function of the similarity score threshold.Click here for file


Arabidopsis seedlings germinated and grown under optimal conditions to those in seedlings germinated and grown in the presence of NaCl. Chloroplasts of the NaCl-grown seedlings were impaired, with less developed thylakoid and granum membranes than control seedlings. In addition, chloroplasts of salt-grown Arabidopsis seedlings accumulated more starch grains than those in the respective control plants. Steady-state transcript levels of chloroplast-encoded genes and of nuclear genes encoding chloroplast proteins were reduced in salt-grown seedlings. This reduction did not result from a global decrease in gene expression, since the expression of other nuclear genes was induced or not affected. Average cellular chloroplast genome copy number was reduced in salt-grown seedlings, suggesting that the reduction in steady-state transcript levels of chloroplast-encoded genes might result from a decrease in template DNA.The chloroplast is the most prominent and metabolically active plastid in photosynthetic plants. Chloroplasts differentiate from proplastids in the plant meristem. Plant plastids contain multiple copies of a small circular genome. The numbers of chloroplasts per mesophyll cell and of plastid genome copies are affected by developmental stage and environmental signals. We compared chloroplast structure, gene expression and genome copy number in  Salt stress is a major abiotic stress that limits plant growth and productivity worldwide . ExposurArabidopsis cell contains approximately 560 copies of the plastid genome [Chloroplasts are highly structured plastids with a characteristic extensive thylakoid-membrane network. Shoot apical meristems are believed to contain proplastids, lacking thylakoids and chlorophyll-binding proteins, which differentiate into chloroplasts very early in the development of leaf primordia –8. It had genome . Genome d genome ,13. Chlod genome ,15. Unlid genome . Arabidopsis seedlings that were germinated and grown in the presence of 100 mM NaCl. Seedlings developed in NaCl-containing medium developed thicker leaves with smaller surface area. Chloroplasts developed in the presence of salt were swollen, with less developed granum structures and more starch accumulation than chloroplasts in Arabidopsis seedlings grown without salt. Steady-state transcript levels of plastid-encoded genes, as well as of nuclear genes encoding chloroplast proteins, were lower in salt-grown seedlings than in seedlings grown under non-stressed conditions. Quantification of the chloroplast genome showed that the number of plastid DNA copies per haploid nuclear genome is reduced in salt-grown seedlings, suggesting that the reduction in transcripts of chloroplast-encoded genes may result from a reduction in template quantity. In this study, we characterized the effects of salinity on chloroplast morphology and on gene expression of chloroplast-encoded genes in 2-week-old Arabidopsis thaliana (Col) seedlings were surface-sterilized and cold-treated as previously described [escribed . Seeds wescribed . Where iLeaf tissue was extracted overnight with 90% (v/v) acetone, absorbance was measured at different wavelengths (661 and 644 nm) after the tissue had been bleached, and the concentrations of chlorophyll a and b were calculated .Tissue was fixed with 1% (w/v) glutaraldehyde in 50 mM cacodylate buffer (pH 7.2) for 30 min at 4 °C, and washed for 10 min with ice cold 50 mM cacodylate buffer. Tissue was then post-fixed with 1% (w/v) osmium tetroxide in the same buffer for 1 h at 4 °C, followed by 10 min wash in ice-cold water. After dehydration in a graded series of ethanol, the tissue was embedded in Araldite. Semi-thin sections (1 µm thick) of fixed material were stained with 2% (w/v) toluidine blue and examined under a light microscope. Ultrathin sections were stained with uranyl acetate and lead citrate and examined using a FEI Tecnai 12 G2 TWIN transmission electron microscope .Relative steady-state transcript levels were assayed by quantitative (q) RT-PCR as described previously ,19–21. RWHIRLY1. Other conditions were as described previously [Genomic DNA was prepared from seedlings essentially as described previously . Seedlineviously ,19–21.Each assay was performed in three biological replicates. Seedlings germinated and grown in the presence of 0.1 M NaCl were smaller than those grown under non-stress conditions, with reduced size of all vegetative tissues . In addiComparison of chloroplast ultrastructure using electron microscopy showed that the chloroplasts in leaves of salt-treated seedlings were much less developed than those in control-grown seedlings . ChloropArabidopsis [Arabidopsis seedlings exposed to salt stress [ Since thylakoid membranes harbor chlorophyll, we determined chlorophyll content in seedlings grown under control and saline conditions. Chlorophyll content in salt-grown seedlings was approximately 40% of that in seedlings grown in the absence of NaCl , with a bidopsis  and mungbidopsis  seedlingt stress –27.In our experimental protocol, NaCl was present throughout all stages of seed germination and seedling development. The results may therefore reflect both plastid/chloroplast duplication and chloroplast development. The number of chloroplasts per mesophyll cell seemed to be only slightly affected by NaCl , suggestArabidopsis) [Our results agree with previous studies showing that chloroplast structure is affected by abiotic stresses, for example swollen chloroplasts in NaCl-treated plants  and in tidopsis) . Chloropidopsis)  and osmoidopsis) . Impaireidopsis) ,34, or iidopsis) .2 fixation (RBSC1A and RCA), was also markedly reduced in salt-grown seedlings . The observed reduction in steady-state levels of chloroplast-encoded gene transcripts  might reArabidopsis seedlings developing under salt-stress conditions are impaired in chloroplast development, and show reduced chloroplast genome copy number, and reduced levels of transcripts of chloroplast-encoded genes and nuclear genes encoding proteins involved in photosynthesis. We show that File S1Supporting tables. Table S1. Primers used for qRT-PCR. Table S2. Primers used for qPCR.(DOC)Click here for additional data file.


TIMING OF CAB EXPRESSION 1 (TOC1). EARLY FLOWERING 3 (ELF3) has been implicated as a repressor of light signaling to the clock [CCA1 and LHY [cca1-11 lhy-21 elf3-4 plants to separate the repressive function of ELF3 from its downstream targets CCA1 and LHY. We further demonstrate that ELF3 associates physically with the promoter of PSEUDO-RESPONSE REGULATOR 9 (PRR9), a repressor of CCA1 and LHY expression, in a time-dependent fashion. The repressive function of ELF3 is thus consistent with indirect activation of LHY and CCA1, in a double-negative connection via a direct ELF3 target, PRR9. This mechanism reconciles the functions of ELF3 in the clock network during the night and points to further effects of ELF3 during the day.The circadian clock provides robust, ∼24 hr biological rhythms throughout the eukaryotes. The clock gene circuit in plants comprises interlocking transcriptional feedback loops, reviewed in , wherebyhe clock  and, par and LHY . We use  TOC1, PRR9, GI, and PRR7 gene expression ► Repression by ELF3 is genetically separable from repression by LHY and CCA1 ► ELF3 physically associates with the promoter of PRR9 in a time-dependent manner► ELF3 is a regulator of  Seedlings were grown under short day conditions (6:18 hr light:dark cycles) for 6 days, and hypocotyl length was assessed on day 7. The wild-type ecotype, Wassilewskija (Ws), and cca1-11 lhy-21 seedlings showed hypocotyls of similar length, whereas cca1-11 lhy-21 elf3-4 showed a phenotype very similar to elf3-4 seedlings  collapsed in the elf3-4 plants, which became arrhythmic in LL. Transcript analysis under LD was more informative. The low-amplitude rhythm in both CCA1 and LHY transcripts , whereas in cca1-11 lhy-21 mutants, ELF3 RNA showed little rhythmicity under LD and arrhythmia under LL (ELF3 expression [rhythmia . We confunder LL F. CircadTOC1 and GIGANTEA (GI), is proposed to generate the short-period rhythms observed in lhy cca1 double mutants [elf3-4, cca1-11 lhy-21, and cca1-11 lhy-21 elf3-4 plants, we aimed to test the role of ELF3 in the proposed evening loop. Plants were grown under 12:12 LD cycles for 6 days, sampled on day 7, and tested for expression of PRR9, PRR7, GI, and TOC1  showed an early-morning peak of high amplitude  of PRR9, PRR7, and GI expression in the night, as reported in [GI, as well as slightly higher nighttime expression of TOC1. The aberrant gene expression continued into the early morning, when CCA1 and LHY should be active in the wild-type  lost the early peak observed in cca1-11 lhy-21, but then had the higher nighttime expression characteristic of elf3-4. This suggests that ELF3 influences the circadian network at more than one point and thus affects both morning and evening loops.An evening loop, involving at least  mutants . Throughand TOC1 . In cca1mplitude C and 2D. and LHY A. The elild-type C and 1D.ELF3 shows some sequence homology with transcription factors [ELF3::YFP fusion protein from either the native ELF3 promoter or the 35SCaMV promoter. We also used 35S::ELF4::YFP to investigate whether ELF3 and ELF4 act on the same promoters. EARLY FLOWERING 4 (ELF4) is a circadian-controlled gene that shows similar gene expression patterns and clock phenotypes to ELF3 [PRR9 promoter  at ZT = 6 , although this promoter fragment is sufficient for rhythmic transcription [PRR9 promoter is sufficient to explain low levels of CCA1 and LHY expression in the elf3-4 mutant , suggesting that ELF3 is involved in the regulation of their expression indirectly.Because  factors , we inve to ELF3 . ELF3 anpromoter . Howevert ZT = 6 C. ELF3'sP plants . Associafor PRR9 . Testingcription . However4 mutant C and 1D, and LHY . The proPRR9 and GI were specifically investigated because they have both been implicated in light signaling to the clock [elf3-4, cca1-11 lhy-21, and cca1-11 lhy-21 elf3-4 mutants, and represented the morning and evening loops of the circadian network. Wild-type plants showed strong light induction of PRR9  and repress the expression of circadian genes. This function is not observed in the cca1-11 lhy-21 double mutant because the component is still being repressed by ELF3.To investigate the role of 4 plants A. cca1-11 lhy-21 . This hi1 lhy-21 . Thus, tPRR9 promoter provides a mechanism for ELF3's direct (PRR9) and indirect (CCA1/LHY) effects on the clock network. The fact that ELF3 affects the clock network beyond the times when ELF3 is detected at the PRR9 promoter is consistent with the known complexity of the clock circuit  were amplified by PCR from wild-type Ws genomic DNA by PCR primers with added restriction sites to facilitate cloning. The sequence of primers and the corresponding restriction sites are provided in ELF3 promoter fragment contained 2695 nucleotides upstream of the start codon of the ELF3 gene and included the full 5′ untranslated region. The ELF3 and ELF4 CDS fragments included the full coding sequence but not the translational termination codons. The 35S:PHYA-YFP pPCVB812 binary vector has been described [PHYA cDNA in 35S:PHYA-YFP pPCVB812 was replaced with the ELF3 or ELF4 CDS fragments, resulting in 35S:ELF3-YFP pPCVB812 and 35S:ELF4-YFP pPCVB812. Then the 35S promoter in 35S:ELF3-YFP was replaced by the ELF3 promoter fragment, which yielded ELF3:ELF3-YFP pPCVB812. The binary vectors containing the gene constructs described above were transferred to Agrobacterium tumefaciens GV3101 cells. The constructs were transformed into wild-type Ws (ELF4 construct) and elf3-4 mutant plants (ELF3 constructs) by the floral-dip method [The escribed . The PHYp method . Primary−2 s−1) in LD cycles at constant 22°C. All plants were grown on 1% agar Murashige-Skoog (MS) plates. Photoperiod light conditions were either short day (SD) 6:18 or standard 12:12, as shown in figures. ZT = 0 is defined as the last dark:light transition before measurements start.All plant lines are in the Ws ecotype. Surface sterilized seeds were stratified for 4 days in the dark at 4°C before being grown under cool-white fluorescent tubes  were set up using a Tecan Freedom EVO robot controlled by EVOware standard software with Master Mix containing SYBR Green (Roche), gene-specific primers at 3 μM, and RNase-free dH2O. The qPCR was conducted in triplicate in a Roche LightCycler 480 controlled by LightCycler 480 SW1.5 software. Transcript levels were normalized to the control transcript IPP2 [For LD time courses, approximately 70 seedlings per sample were harvested for each genotype into 1 ml of RNAlater solution (Ambion). Samples were taken at 2 hr intervals starting at ZT = 0. Total RNA was extracted  according to manufacturer's instructions. cDNA was synthesized from 1 μg of total RNA, and random hexamer primers were supplied with the Fermentas cDNA synthesis kit. cDNA was diluted 1:5 in RNase-free dHipt IPP2  and wereAll presented measurements are an average of three independent experiments. Gene-specific primer pairs are listed in −2 s−1) photoperiod conditions on MS and 1% agar plates for 6 days, and hypocotyls with centimeter ruler were imaged using a digital camera. Measurement of hypocotyl length was performed by ImageJ (http://rsb.info.nih.gov/ij/), with hypocotyl length being defined as from V in hypocotyls-cotyledon formation to hypocotyls-root junction.Plants were grown under SD (6:18 LD) white-light (70–100 μmol mChIP was carried out as previously described , with th


Trifolium repens L.) is an allotetraploid species possessing two highly collinear ancestral sub-genomes. The apparent existence of highly similar homeolog copies for the majority of genes in white clover is problematic for the development of genome-based resources in the species. This is especially true for the development of genetic markers based on single nucleotide polymorphisms (SNPs), since it is difficult to distinguish between homeolog-specific and allelic variants. Robust methods for categorising single nucleotide variants as allelic or homeolog-specific in large transcript datasets are required. We illustrate one potential approach in this study.White clover (We used 454-pyrosequencing sequencing to generate ~760,000 transcript sequences from an 8th generation white clover inbred line. These were assembled and partially annotated to yield a reference transcript set comprising 71,545 sequences. We subsequently performed Illumina sequencing on three further white clover samples, generating 14 million transcript reads from a mixed sample comprising 24 divergent white clover genotypes, and 50 million reads on two further eighth generation white clover inbred lines. Mapping these reads to the reference transcript set allowed us to develop a significant SNP resource for white clover, and to partition the SNPs from the inbred lines into categories reflecting allelic or homeolog-specific variation. The potential for using haplotype reconstruction and progenitor genome comparison to assign haplotypes to specific ancestral sub-genomes of white clover is demonstrated for sequences corresponding to genes encoding dehydration responsive element binding protein and acyl-coA oxidase.In total, 208,854 independent SNPs in 31,715 reference sequences were discovered, approximately three quarters of which were categorised as representing allelic or homeolog-specific variation using two inbred lines. This represents a significant resource for white clover genomics and genetics studies. We discuss the potential to extend the analysis to identify a “core set” of ancestrally derived homeolog specific variants in white clover. Trifolium repens L.) is an allotetraploid forage legume with a genetically determined gametophytic self incompatibility system [T. occidentale D.E. Coombe, and T. pallescens Schreber are the currently extant species that seem to best represent the diploid progenitors of the species [White clover . Most interestingly, it was possible to use the proposed ancestor sequences to assign putative homeolog specific variants (HSVs) into ancestral sub-genomes . This represents a robust way of both differentiating between allelic and non-allelic variants and also grouping them into consistent sub-genome classes (referred to as the O and P’ sub-genomes), but the process would be difficult to apply to many thousands of sequences.One potential approach for distinguishing homologous, homeologous  sequence variation in EST-derived sequences from outbreeders is the generation, cloning and sequencing of PCR amplicons based on the ESTs. This approach, which allows unambiguous reconstruction of the component haplotypes at a locus (or closely related homeo-loci) was examined by Hand et al. . For fouHomeolocus organisation over tens/hundreds of kilobases has been examined in greater detail for a restricted number of loci in white clover. Hand et al.  sequenceThe constantly increasing throughput of next generation sequencing (NGS) technology is revolutionising our ability to describe both genome structure and variation. Recently, numerous draft whole genome sequences have been developed in higher plants, based either in part or entirely on NGS-based sequencing strategies. Plant species, many of which have large complex genomes with a history of ancient and recent duplications are still challenging to sequence. NGS is also rendering the transcriptome more accessible, with technologies such as Illumina-based RNA-Seq allowing sampling to depths capable of detecting relatively low-abundance transcripts in specific tissues and environmental conditions. This is a useful tool in both functional genomics and the annotation of genome sequences, but also can also provide a potentially stand-alone resource in the face of difficult genomes where whole genome sequencing is problematic. Another potential use of large scale transcript sequencing datasets is in high throughput, genome-wide SNP discovery. This was previously possible using Sanger-derived EST sequences, but the cost and comparatively low throughput nature of this strategy meant that, in comparison to NGS-based strategies, it was really only effective in relatively well-resourced model and crop plant species, where EST data could be aggregated from numerous sources.The real potential of NGS for SNP discovery relies heavily on an ability to both discover and validate SNPs in an automated, high throughput manner. While the process of single nucleotide variant detection in allopolyploids is technically no more complex that the same process in homozygous diploids, the presence of multiple independently-segregating but potentially very similar sub-genomes requires extra layers of analysis in order to distinguish allelic and non-allelic variation. As illustrated by the above studies, this is certainly the case for allotetraploid white clover. While the approaches described in these studies ,12 were Brassica napus which allowed the discrimination of allelic single nucleotide variants (hemi-SNPs) and sub-genome-specific or homeolog-specific variants (inter-homeolog SNPs). The progenitor A and C sub-genomes of B. napus are very similar, with many genes expected to exist as homeolog pairs, the transcript sequences of which are expected to differ by only approximately 3.5% on average. As a result, such homeolog pairs will tend to co-assemble or cluster, even when relatively high similarity thresholds (~95%) are used. The strategy is based on the use of doubled haploid (DH) lines, in which the individual sub-genomes have been rendered homozygous. Allelic variants in a sub-genome will be fixed for one or the other alternative nucleotide. Nucleotide variants that distinguish homeolog pairs (inter-homeolog variants) originating from different subgenomes will still be present. When reads from a homeolocus in a single DH line are aligned relative to a reference sequence, nucleotide variants apparent between constituent reads of the alignment are either inter-homeolog variants or real allelic variants that differ between the subgenomes. In most alignment software, the consensus sequence generated from the alignments will represent such positions with ambiguity codes indicating the presence of more than one nucleotide. The key point in the strategy is the comparison of the consensus sequences for alignments from different DH genotypes.Recently, Trick et al.  describeThus, any set of genotypes compared in this manner will always have the same consensus nucleotide interhomeolog variant positions. For positions where real allelic variation is the source of the ambiguity code in one genotype, comparison to a second genotype may reveal that the same position has a normal nucleotide code rather than an ambiguity code. This is likely to be the footprint of allelic variation at this position. Since, in essence, all that is required is the comparison of the consensus sequences resulting from at least two genotypes for each locus (defined by a reference sequence) this approach is amenable to being scaled up for numerous loci. Platforms such as Illumina can produce tens or hundred of millions of 100nt transcript reads per genotype, providing a source of genotype-specific sequences to align to a reference sequence, at a depth of coverage that allows the robust detection of nucleotide variants in the alignments.B. napus, so a similar strategy could be implemented with suitable homozygous genotypes. To our knowledge, no doubled haploid lines of white clover have been developed. However, inbred lines based on starting material in which the single-locus gametophytic self-incompatibility system has been overcome, presumably by the presence of one or more self-fertility (Sf) alleles, have been developed [1 hybrid progeny that consistently exhibited heterosis for a range of characteristics, supporting the generally inbred nature of the lines. Two of the lines were subsequently used in the first molecular marker based genetic linkage map of white clover [2 population of a cross between a fourth generation derivative of one line and a fifth generation derivative of another (population F2(I.4RxI.5 J)). Marker segregation data revealed a high retention of heterozygosity, with up to 60% of simple sequence repeat (SSR) markers exhibiting bands suggesting retention of heterozygosity in the parents.The levels of nucleic acid identity for the subgenomes of white clover are similar to that of eveloped . Originae clover , which wIn the study described here, we sought to develop publicly accessible EST and SNP resources for white clover. In the first instance we exploited the longer read capabilities ~400nt) of 454-pyrosequencing to generate an EST-like reference transcript set of approximately 70,000 sequences in a white clover inbred line. This was then used as a reference for two rounds of SNP discovery using shorter (76-101nt) Illumina reads, the first of which was based on generating approximately 14 million sequences from mixed cDNA samples of 24 white clover genotypes from a wide geographical range. In the second round of SNP discovery, we used the eighth generation derivatives of two of the sets of white clover inbred lines described above to mimic the genomic constitution that would be expected for doubled haploid lines. These lines have undergone three to four further generations of inbreeding than the lines described by Michaelson-Yeates et al. [00nt of 4Trick et al.  applied Trifolium subterraneum chloroplast sequence (GenBank Accession Nr: EU849487). Two hundred and sixty-one contigs gave hits with more than 90% similarity at an E-value threshold of >10E-50. These contigs were removed and the remaining 40,649 (mean length: 783 nt) were used for further analysis. The length distribution of the assembly following these processing steps is summarised in Figure To provide a reference transcript set for white clover, cDNA from mixed above-ground material of a single pot-grown plant of the inbred line S1 was subjected to sequencing on the Roche GS_FLX platform using Titanium sequencing chemistry, generating a total of 768,512 cleaned reads (mean length 300.21 nt). Assembly of these reads using MIRA  resultedinfo_debrislist.txt file provided by the MIRA assembler. The sequences were cleaned to remove vector and adapter contamination, and low quality and low complexity terminal sequencess (like polyA and polyT stretches) using the SeqClean script [T. subterraneum chloroplast sequence and removed. The remaining set of 30,896 non-redundant singlet sequences was used for further analysis. The mean length of the non-redundant singlet sequence set was 267.35 nt . Hereafter we refer to the combined set of contigs and non-redundant singlets  as the reference transcript set.Sequences of 454 reads that were not incorporated into contigs were recovered by using the information given in the n script , resultiMedicago truncatula Gene Index (MtGI rel. 10.0) transcript database using the tblastx program of the BLAST package [In order to assess the overall quality of the 40,649 contigs and 30,896 non-redundant singlets, they were compared to the  package .tblastx results were filtered into 3 categories using a Perl script:The top hits of the (1) “Class I” hits: E-value < 1E-100, Score >200;(2) “Class II” hits: E-value < 1E-20, Score >100;(3) “Class III” hits: E-value >1E-20.Seventy-seven percent of the contig sequences fell into the top two categories  while the remaining 23%  were in Class III. For the singlet sequences the situation was reversed, with 66% of sequences  falling onto Class III, 33.7% into Class II and only 17 sequences (0.06%) in Class I  were largely similar in contigs and singlets  were subjected to functional annotation by comparison to a custom database of all available pipeline . Using tIn order to exploit the reference transcript set to identify SNP variation across the white clover genome we used two approaches. In the first instance, we generated approximately 14 million Illumina GAII reads from pooled cDNA of 24 very divergent heterozygous allotetraploid genotypes representing a pan-global geographic distribution of white clover, and thus likely to capture a significant proportion of the overall variation present in the cultivated genepool of the species. We aligned these sequences to the reference transcript set to identify SNP positions.After cleaning, 13,213,625 Illumina reads (length 76 nt) were available for alignment to the reference transcript set. Alignment, mapping and variant calling were performed as described in the Methods section. Single nucleotide variants were identified by applying a read depth threshold of 6 and a base quality threshold of 20. The process resulted in the identification of 89,503 SNPs, . Of the 89,503 SNP variants 77,305 were due to ambiguity codes in the read consensus of aligned Illumina reads, the remainder  were departures from the reference. In all, SNPs were detected in 20,159 reference sequences . The average read depth at the SNP positions was 25.69  in the case of the contigs and 27.35  in the case of the singlets. Hereafter we refer the mapping assembly representing this dataset as the DL assembly.In the second instance, we generated approximately 50 million reads for each of two further white clover inbred lines on the Illumina HiSeq 2000 platform and aligned these to the reference transcript set.For identification of SNPs between the two white clover inbred lines, cDNA from individual plants was used. One of the lines, SC was nearly identical to the S1 line used for the reference transcript assembly, as it was sourced from the same batch of seeds produced by a single self-fertilisation event. Line J5 was derived from a different, genetically distinct background, with the expectation of reasonable levels of nucleotide polymorphism relative to SC confirmed by some preliminary analysis using two AFLP assays (results not shown).Illumina reads  were aligned to the reference transcript sequences using the pipeline described more fully in the Methods section. From a merged alignment that contained Illumina reads from both genotypes (hereafter referred as IL assembly) we separately called SNPs for each read group in positions where there was read depth of at least 6, applying a base quality threshold of 20. This produced two lists of variants (based on the SAMtools pileup files), one for each genotype. Conceptually, these variant lists contain positions where variants (ambiguity consensus codes or departure from the reference base) occur in both genotypes and positions where variants are observed in one genotype but not the other. We compared the two variant lists, and for the latter class we examined the merged alignment in regard to the opposing, non-variant genotype, and accepted the non-variant status where there was a read depth of at least 6 in the non-variant read group. This produced a “high confidence” set of variant calls over the two genotypes that were supported by a minimum read depth of 6 in each genotype. This variant set comprised instances in which variants were observed in either both genotypes, or exclusively in one genotype or the other.In addition, for completeness, we also recorded all instances in which there was a variant in one genotype and not the other, where the second genotype had a read depth of lower than 6, including those for which there was no read coverage (ie. read depth of 0 to 5). Finally, we recorded instances in there was no apparent variation in either SC or J5, but where there was an apparent departure from the reference sequence.This analysis identified 172,531 independent SNPs in alignments to 28,210 sequences from the reference transcript set sequences . No variants satisfying our filtering parameters were found in the remaining reference transcript set of 19,975 contig sequences and 23,360 singlet sequences. The positions and nucleotide composition of the variants identified in both the DL and IL assemblies are listed in Additional file In the case of the reference contigs, the mean read depth at the SNP positions was 47.99  for the J5 read type and 53.79  for the SC read type  Inter-homeolog SNPs (IH)representative of homeolog specific variation. These comprise variants with the same ambiguity consensus sequence in both the J5 and SC read types .(2) Hemi SNPs (HE)Asymmetric hemi-SNPs (AH).representative of allelic variation. In these cases, no variant can be detected in one of the read types (i.e. the consensus sequence is non-ambiguous in one of the read types), but the opposing read type exhibits variation (ambiguous consensus) at a given position (e.g. T/T/W). A subcategory of this class occurs for variants where the two read types have different ambiguous consensus sequences . We categorized these as (3) Simple SNPs (S)This class of variants represents inter-genotype polymorphism where the two read types display different unambiguous consensus nucleotides (e.g. T/T/A). This class could arise from several different sources, eg., effective diploid status at that locus or co-positional allelic variation in both homeolog genomes.(4) Irregular SNPs (X)In addition to the three main SNP classes, further minor variant classes were identified that could not easily be assigned to the above classes, but may represent real polymorphism, and thus we recorded these in a single category. This included: (i) Instances in which an ambiguity code was observed in one read type, but low (0 to 5 ) read depth occurred in the opposing read type; (iI) A class of variant position where both read types have the same unambiguous consensus sequence which differs from the reference sequence - in most cases the reference base was ambiguous at such position e.g. M/C/C; (iii) Positions where one or both read types exhibit consensus of more than two constituent bases (e.g. W/W/B or W/B/B).From the total of 172,531 variants identified, 73,571 (42.64%) were categorised as Hemi-SNPs, representative of allelic variation. A further 53,663 (31.10%) were categorised as Inter-homeolog SNPs and 19,953 (11.56%) were classified as Simple SNPs. The remaining 25,344 variants (14.69%) fell into the Irregular SNP category. Nearly three quarters of the latter  were of the variety in which there was an ambiguity code representing a variant in one genotype, but low or no coverage in the other, making it impossible to assign to a definitive category using our threshold criteria. The majority of the remainder involved positions where J5 and SC exhibited the same nucleotide but differed from the reference base. These results are summarised in Table One interesting feature of this study is that the reference sequence was generated from the same inbred lineage as one of the test genotypes (SC). As such, there are additional expectations in terms of the types of SNP configurations that should be observable in the partitioning analysis relative to a situation where the reference data were aggregated from numerous different sources. The most obvious expectation relates to the simple SNPs, in that the test genotype SC should share the same nucleotide variant as the reference sequence in this situation (eg G/G/A). In fact, this expectation was met in the vast majority (96.8%) of the 19,953 simple SNP calls  fell into four non-problematic subcategories . Deviations from this expectation occurred in only 17 (0.03%) of 53,663 IH SNP calls.While, given our model assumptions, the origin of Hemi-and IH SNPs is relatively clear in terms of allelic vs. homeolog specific variation, the origin of Simple SNPs is perhaps less clear. Instances in which Simple SNPs are arising from loci for which no clear homeolog copy exists (effectively diploid loci), or from the transcriptional suppression of one homeolog copy in favour of another are likely to be represented in our dataset by reference sequences in which only Simple SNPs between the inbred lines are present. From 8577 reference transcript sequences containing simple SNPs, 2136 contained only Simple SNPs , meaning that up to one quarter of contigs containing simple SNPs could come from the aforementioned sources. In the majority of occasions, Simple SNPs occur in contigs also containing Hemi- and IH-SNPs, indicating that both homeologs are present and expressed in the genotypes studied. Hand et al.  found th2 mapping population suggested residual heterozygosity levels as high as 60% [One of the major assumptions underlying our attempt to partition SNPs into allelic and non-allelic variants is that the two genotypes under comparison are completely homozygous. In this study, we have used eighth generation inbred lines derived from self-fertile white clover genotypes J and S  in whichh as 60% . Thus, wde novo assembly phase, our process should generally achieve co-assembly of homeolog copies, although nucleotide similarity levels as low as 86% were previously reported [In addition to demonstrating that residual heterozygosity is limited in the inbred lines, this analysis suggests that, in general, the expectation that homeolog copies will tend co-assemble in the reference transcript assembly and subsequent mapping assemblies is valid. Available data suggest that the average nucleotide identity levels in white clover are as high as 97% . With itreported , and sucIn order to identify the total number of independent SNP positions identified in this study, we made a direct comparison between the variant lists arising from the divergent (DL) and inbred (IL) line mapping assemblies. Comparing the 172,531 SNPs from the inbred lines to the 89,503 SNPs from the divergent lines revealed that 52,730 SNPs were positionally common between the two datasets, with 119,801 variants occurring only in the inbred line dataset and 36,323 variants specific to the divergent lines  are representative of allelic variation, and thus one might expect that there are allelic variants represented in the IL assembly that are NOT present in the variation captured in the DL assembly. In order to test this, we identified all variant positions in the IL assembly supported by read depths of 6, 8 and 10 in each individual inbred line, for which there was matching read depth support in the DL assembly. We then identified whether variants were also present at these positions within the DL assembly. At the highest stringency level, which required read depth of at least ten in each inbred line and the DL assembly, there were 22,220 IH SNPs from the IL assembly for which there was sequence coverage in the DL mapping assembly. Of these, 19,626 positions 88.33%) were also variant in the DL assembly  showed clear expression of both (O and P’ sub-genome-derived) homeolog copies in both of the inbred lines using these criteria. Amongst the remaining 26% of reference sequences, many variants falling below the read depth criteria (Irregular category SNPs) were identified, indicating probable expression of both homeologs. Excluding sequences containing Irregular category variants for this reason, only 11% of reference sequences (3163 of 28210) clearly exhibit the footprint of possible homeolog-specific transcriptional suppression in one inbred line. In these cases, no IH SNPs were identified and all HE and S variants were derived from one line, indicating possible suppression in the other. This suggests that homeolog-specific transcript suppression is not a prevalent feature of this dataset, and that the analysis pipeline employed can identify potential cases, reducing the need for subsequent validation to account for this phenomenon.As previously outlined, Hand et al.  identifiThese alignments present the first opportunity to examine the sub-genomic origin of IH SNPs identified by our analysis. Of the 34 IH SNPs in the 4 transcript sequences, 17 had equivalent sequence coverage in the BAC-derived gene sequences. Nucleotide variation between the BAC-derived sequences from alternate sub-genomes matched IH variation in the inbreds on 12 occasions. At the remaining 5 comparable IH SNP locations, no variation was observed between BAC-derived sequences. This figure is not unreasonable given that Hand et al. ,12 foundDREB3) and acyl-coA oxidase respectively , and the read coverage in J5 and SC, it was most likely that we would be able to manually reconstruct the haplotypes for the full length of the underlying sequences. Reference transcript sequences wcd-12699 and wcd-2781 both possess 16 SNPs in the IL assembly , they could be subjected to the pipeline described in this study. Inter-homeolog SNP calls would in this case be based on only those ambiguity codes occurring in all of the genotypes under examination, eliminating homeolog specific variant sites at which allelic variation was also occurring, thus producing a core set of ubiquitously present homeolog specific variants. The success of the strategy would largely be determined by the number of lines examined, but, even using advanced generation inbred lines derived only from the four genotypes described originally [Hand et al. pointed iginally , it is pThe main goal of this study was to develop a significant publicly available SNP resource for white clover. We have developed, and partially annotated a reference transcript set of over 70,000 sequences for white clover, and used these to identify over 200,000 independent SNPs in approximately 45% of the reference transcript sequences. In recognition that the utility of these SNPs is partially based on whether they are truly allelic or sub-genomic in origin, we have attempted to partition a significant portion of them into categories related to these origins, and examined the most significant parameters likely to effect the accuracy of these categorisations.et al.[The SNP data processing and analysis pipeline presented in this study can easily be adapted to SNP discovery projects in other allopolyploid species. Based on the principles outlined by Trick et al. it diffeAn additional advantage of using SAMtools is that short reads from different genotypes can easily be mapped and aligned onto the same set of reference sequences, while variant calling can be performed both globally as well as specifically for any particular genotype, or for a set of genotypes from combined alignments. This can be effectively utilised in the separation of allelic and sub-genomic variants in two or more genotypes, and we have demonstrated how this type of analysis could be extended to identify a core set of homeolog specific SNP variants that would be useful for distinguishing the sub-genomic origin of white clover transcripts, while simultaneously providing an even more comprehensive collection of potential allelic variants for genome-based analysis in white clover.Three genotypes from two of the sets inbred lines originally described by Michaelson-Yeates et al.  were usesff file of the sequence provider, containing 768,512 reads. Vector ends, adapter sequences were stripped from the reads using SSAHA2 . Non-chloroplast contig sequences were compared to M. truncatula transcript sequences using the tblastx program of the BLAST package. Databases were built for standalone searches using the BLAST program [Fasta format sequences, quality scores and traceinfo-related xml information were parsed into separate files from the  (v2.5.3 ) and SMA (v2.5.3 ). Reads  (v2.5.3 ) with heT  terms via the command line version (B2G4Pipe) of the Blast2GO program [-m7 output format option (xml output). Result files were merged in batches of 10,000 files and loaded into the Blast2GO annotation pipeline. An e-value hit prefilter of 1E-6 was applied and an annotation cut-off of 55 and a GO weight of five were set. The primary ontology term annotations were augmented by Second Gene Ontology Layer data through the GO Annotation Toolbox [All protein sequences belonging to se 182.0 ). 1,763, program . Blastx  Toolbox  as implemerge command. SAM format alignments were indexed and converted to BAM format using SAMtools.Short reads were indexed and aligned onto the S1 reference sequences by the Burrows-Wheeler Aligner  , using tVariant calls from this merged alignment were performed in two subsequent steps:mpileup command. The resulting pileup file was further processed by the bcftools and vcfutils varFilter.pl programs of the SAMtools package allowing a maximum read depth of 200. The resulting variant list served for base quality recalibration and local realignment of the combined (J5/SC) BAM file using utilities of the Genome Analysis Toolkit (GATK) package [(1) A global variant call (i.e. including both read types at each variants position) was conducted using the SAMtools  package .pileup command.(2) For SNP discovery, read-type (J5 and SC respectively) specific variants were called from the cleaned J5SC BAM file in two independent steps using the SAMtools A similar pipeline was applied for mapping and aligning of short reads from the divergent genotype pool, except conducting of read group related processing steps.varFilter.pl utility by setting a minimum read depth threshold of six and a maximum read depth threshold of 200. Indel positions were skipped and final SNP calls were acquired by setting a Phred score quality threshold of 20. Using custom Perl scripts, the cleaned pileup files were compared to select variants with matching positions in two compared read groups. From the remaining parts of the pileup file lists, unique variant positions were created. These lists were used to call pileup information from the corresponding BAM format assembly file in regard to the opposing read group using the SAMtools pileup program by switching off the variant filtering option. The first eight columns  were extracted from the pileup files for each read type and were put into a general matrix containing all relevant variant information. Perl scripts were applied to compare the nucleotide consensus of each read type at each variant position based on intra- and inter-genotype polymorphisms, ambiguity type and relation to the reference in order to sort variants into different categories. Further custom Perl scripts were applied to produce ordered variant lists for each reference sequence and to calculate distributions and frequencies.Read-type specific raw variant lists were filtered by the SAMtools A BAM format alignment file consisting of the reference transcripts and the mapped J5 and SC Illumina reads was visualised in the IGV (v2.0.34 ) alignmeDrawtree program of the PHYLIP package [Short reads mapped on the selected contigs from the reference transcript set were extracted – separately for each read type - from the J5SC assembly and re-aligned onto the reference sequences using the FSA alignment program . In each package .The authors declare no competing interests.IN participated in planning the study, carried out all of bioinformatics analyses and participated in writing the manuscript. SB participated in the conception, planning and writing of the study, and directed some of the laboratory based experiments. MTA participated in the conception, planning and writing of the study. JMC carried out all in-house plant and laboratory experiments associated with the study and contributed to the writing of the manuscript. DM conceived and planned the study, directed the work, contributed to analyses, and participated in the writing of the manuscript. All authors have read and approved the final version of the manuscript.Teagasc - Crops, Environment and Land Use Programme, Oak Park Research, Carlow, Ireland. Department of Molecular Biology and Genetics, Aarhus University, Forsøgsvej 1, DK-4200 Slagelse, Denmark. Institute of Biological, Environmental & Rural Sciences, Aberystwyth University, Penglais, Aberystwyth, Ceredigion, SY23 3DA , UK. Current address: International Institute of Tropical Agriculture, PMB 5320, Oyo Road, Ibadan, Nigeria.Summary table of the GO annotations.Click here for fileA multi fasta file containing 71,545 transcript reference sequences. Contigs are marked with the prefix wcc or wcd, singlets have the prefix wcs. The header of each sequence entry contains the description resulting from the annotation procedure (if any) as well as the number of assigned GO terms and the number of detected SNPs.Click here for fileFull list of variants identified in the IL and DL assemblies. A list of contigs and singletons from the reference transcript assembly with the number, type, position and nucleotide variants of all SNPs identified in the DL and IL assemblies. A full description of the fields in the table is given in the file header.Click here for fileTranscript reference sequences with homology to published sub-genomic sequences . et al. ).Click here for fileSequence alignments of reconstructed haplotype-specific consensus sequences and published sub-genomic sequences. Acyl-coA oxidase: The transcript reference sequence wcd-2781 and the reconstructed haplotype-representative consensus sequences  and GU443966_17 (ACYL_P’) respectively. Dehydration responsive element binding protein “DREB3”: The transcript reference sequence wcd-12699 and the reconstructed haplotyp- representative consensus sequences  were aligned to the published sequences GU443966_13 (DREB3_O) GU443966_21 (DREB3_P’).Click here for file


R2). We compare the accuracy of prediction with and without information from SNPs.Though recently they have fallen into some disrepute, genome-wide association studies (GWAS) have been formulated and applied to understanding essential hypertension. The principal goal here is to use data gathered in a GWAS to gauge the extent to which SNPs and their interactions with other features can be combined to predict mean arterial blood pressure (MAP) in 3138 pre-menopausal and naturally post-menopausal white women. More precisely, we quantify the extent to which data as described permit prediction of MAP beyond what is possible from traditional risk factors such as blood cholesterol levels and glucose levels. Of course, these traditional risk factors are genetic, though typically not explicitly so. In all, there were 44 such risk factors/clinical variables measured and 377,790 single nucleotide polymorphisms (SNPs) genotyped. Data for women we studied are from first visit measurements taken as part of the Atherosclerotic Risk in Communities (ARIC) study. We begin by assessing non-SNP features in their abilities to predict MAP, employing a novel regression technique with two stages, first the discovery of main effects and next discovery of their interactions. The long list of SNPs genotyped is reduced to a manageable list for combining with non-SNP features in prediction. We adapted Efron's local false discovery rate to produce this reduced list. Selected non-SNP and SNP features and their interactions are used to predict MAP using adaptive linear regression. We quantify quality of prediction by an estimated coefficient of determination ( Persistent idiopathic elevated blood pressure (BP), or essential hypertension (HTN), is quantitatively the major risk factor for untoward cardiovascular outcomes, with wide-ranging prevalence of 29% in the U.S. Although pathogenic pathways that lead to HTN remain poorly understood, much of the risk of HTN is believed to be genetic. Therefore, genetic investigations may lead to our improved understanding of traits related to HTN and ultimately to identifying new molecular targets for drug therapy. Genome-wide association studies (GWAS) are the most recent form of such efforts and have been employed to interrogate the genetic architecture of other complex diseases as well as essential HTN From a clinical point of view, we are interested in how much new information the genome-wide SNPs provide in prediction of a BP trait in addition to what is achievable from traditional risk factors, such as blood cholesterol level and glucose level. To this end, population-based cohort studies that include rich data on non-SNP clinical biomarkers, if combined with whole-genome SNP data, could complement case-control studies such as GWAS in a clinically relevant fashion. This approach shifts the focus of studies from finding genes that are causally associated with disease status  toward assessing predictive power for the unobserved phenotype of interest, which can be modeled by regression with many predictors.r2 of 0.81 and 0.84 in our study sample, consistent with what has been described by others In this article we quantify the impact on mean arterial blood pressure (MAP), an obvious quantitative phenotype, of more common genetic variation above and beyond that of other conventional risk factors , in the cohort of first-visit Atherosclerosis Risk in Communities (ARIC) women. ARIC was a prospective study, conducted in four U.S. communities, supported by the U.S. National Heart, Lung and Blood Institute (NHLBI). What makes the ARIC cohort an ideal study sample is that there were many measurements gathered in clinic, not least blood pressure measured according to the ARIC protocol locfdr) framework 2R). This number is compared with 2R estimated from stage (a) in order to quantify improvement in prediction due to SNPs. We validate the entire procedure by 10-fold cross-validation.We consider the synergistic impacts of SNPs and other features with initial hypothesis that variation in MAP can be explained largely by non-SNP features together with SNPs, and the synergistic effects of their union. To begin, we assess the ability of non-SNP features to predict MAP for white female subjects. Predictive analysis of SNPs on MAP is conducted with a novel regression technique that has two stages: (a) “main effects” and “interactions” are discovered for non-SNP features by applying classification and regression trees (CART) combined with the bootstrap for which the sampling units are individual subjects; this selection of non-SNP features and their interactions enables assessment of accuracy in predicting MAP; (b) the enormous list of SNPs genotyped is pared down to a manageable list for the purpose of combining with non-SNP features in prediction; to that end, we adapt the local false discovery rate . The full list and the characteristics of these variables for the sample of subjects included in our analyses are presented in Among the 8861 individuals, all of whom are self-reported whites, genotyped among the 15,792 ARIC individuals, we selected 3138 females who were premenopausal or naturally postmenopausal at the first visit. We concentrate on women rather than men because the genetics of what drives blood pressure is different in men than women. We focus on pre-menopausal and naturally post-menopausal women because we do not want medical interventions to interfere with the joint relationship of predictors and outcome. For each individual, we used 377,790 unimputed SNPs on the autosomal chromosomes. As non-SNP clinical features, we used 44 variables that include morphological and biomarker measurements. The 44 clinical features were chosen subjectively because it was thought that they would be most predictive among available ARIC features. Readers can see what features were available from ARIC  y is a vector of length n representing MAP; 1 is a vector of n ones; Z is a data matrix for the main effects of the non-SNP features; f(Z) is a data matrix for the interactions among the non-SNP features; Λi is a column vector of length n having entries 0, 1, or 2 representing the number of minor alleles at the i-th SNP; and g is a data matrix for the interactions among the SNPs and the non-SNP features, with Λ = . μ is the overall mean of MAP; u is a vector of non-SNP main effects; v is a vector of non-SNP interaction effects; αi is the main effect of i-th SNP; β is a vector of interactions among the SNPs and the non-SNP features; ε is a vector of residuals.We employed an additive model in which the phenotype of interest (MAP) is a linear function of particular non-SNP features, their interactions, additive genetic effects of SNPs, and the interactions among the SNPs and the non-SNP features. The additive model can be writtenZ and Λ. This approach is in contrast with the prevailing “components of variance” approach to understanding genetic data It bears mention that this paper is about variable selection and thus is in the spirit of data mining. Note that model (1) is conditional on data matrix Z, not to speak of being conditional also on available SNPs: μ is a one-dimensional constant; ν, the α's, and β are  constants. It is fundamental that analyses given in this paper are all conditional on values of these parameters and data matrices. The source of randomness in our paper is relevant to bootstrapping discussed in the next section. Apart from the error ε, it arises from the joint empirical distribution of When model selection is the issue, as it is here, Gaussian assumptions on relevant criteria and their implications for the distribution of MSE do not apply. Therefore one way to judge “significance” is from some sort of internal validation of an entire process. The question is one of validation, not of subjective choice of predictive features or combinations of them. The remainder of Methods deals with this issue.We used CART to select main effects and interactions among the 44 non-SNP features. The use of CART as an interaction selector has been advocated widely adjacent nodes, i.e., parent-child pairs and grandparent-parent-grandchild triples. With this approach, for each interaction term the corresponding main effects are also chosen. Among the chosen main effects and the interaction terms, we selected those that occurred more than a certain percentage of the maximum 300 occurrences .For each bootstrap sample, we fit CART using the default pruning method. For each of the 300 trees, we chose main effects along every path from the root to the leaf node by picking a feature if it was ever the feature on which a split was made. For each path, we chose two- and three-factor interactions as Trees have been used previously, albeit in ways different from their use here, in order to find and quantify particular interactions of amino acids at various sites of genomes. See, for example, D#ij is the total number of simultaneous occurrences of features i and j, #i and #j are there respective number of individual occurrences. For each cluster C, we selected a centroid feature using a minimax criterionStill, features come in groups that are close to collinear. If a pair of frequent features does not occur simultaneously in a single bootstrap run of CART, they may compete due to collinearity , defined as the squared correlation coefficient between the predicted responses and the actual responses. In this way, we validate the adaptive algorithm and its predictive power, but not a single model with fixed predictors.We applied the LASSO adaptive regression in order to assess the overall predictive power, possibly eliminating features with low predictive value. Note that CART is not used to directly fit MAP; it is the LASSO that is fit using the features selected by boostrapped CART and the co-occurrence matrix-based clustering. We cross-validated the entire procedure of CART, the bootstrap, the co-occurrence method, and the LASSO in order to estimate the coefficient of determination , and the covariates set to the non-SNP features chosen by bootstrapped CART and the co-occurrence clustering. SNPs that have low local false discovery rates In order to assess the marginal effect of SNPs, so in addition to the predictive contribution of the non-SNP features, we computed the nominal P-value of each SNP for the model (1) with We explored the full model (1) by repeating the procedure of Section 2.3, but with the chosen SNPs added to the CART interaction selector. We validated the increase in predictive power of this approach using the same cross-validation sets as those used for validating non-SNP features.R2 of 24% to 27% for medication-adjusted The adaptive prediction algorithm chose 14 to 30 non-SNP features see , and achBecause prediction is better for adjusted MAP than for unadjusted MAP, plausibly a search for features predictive of MAP would yield more of them if the outcome was adjusted. Thus, plausibly, a search for features using adjusted MAP is generous approach to finding them. If a feature is not discovered when MAP is adjusted, it may have particularly limited predictive value.R2 either for medication-adjusted MAP or for unadjusted MAP  from 0.2 to 0.5, to allow more SNPs into the adaptive regression. For adjusted MAP, inclusion of them as main effects resulted in 625, 642, 597, and 697 SNPs in union over the 10-fold cross-validation, respectively for non-SNP cutoffs 1%, 5%, 10%, and 20%, where 268 of those were common.  Contrary to one popular view, that use of more markers leads to improvement of predictive performance, we observed that R2, compared to up to 27% of “other features first” approach under the same condition, although adding “other features” recovered the predictive power of approximately 26%  exists. Of course, it should be stressed that these results are limited to the genotyped SNPs. Perhaps including other SNPs  may be more informative.R2 for “SNPs first” (and ∼26% for “other features first”) is relatively low compared to the similar figure of merits as low as 33% for mean cellular hemoglobin (MCH) for the predictive analysis of heterogeneous stock mice from eight inbred strains That “SNPs first” approach was not as good as “other features first” may hint at the amount of information shared by the SNPs and the non-SNP features (within the aforementioned limit). Admittedly much of the information in non-SNP clinical features is genetic. That adding the non-SNP features recovered the predictive power of the “other features first” approach may be an indication that virtually no new genetic information, or at least predictive utility, can be obtained from the markers considered in the study. As a quantitative trait, ∼13% of R2 of 18.37% (standard deviation 3.58%) for cutoff 1%; 18.29% (3.56%) for cutoff 5%. With SNPs (mFDR = 0.2) added as main effects the improvement in R2 was −0.45% (0.82%); with interaction, it was −0.38% (s.d. 1.1%). This result is similar to that of CART in that the contribution of the SNPs in predictive power was not significant. Note that MARS was only used as an interaction selector. If it subsumed the LASSO component, then MARS may have performed better than CART subsuming the LASSO, but it is unlikely that the overall predictive power be significantly higher than the best value achieved by the combined CART and LASSO approach.We realize that there may be a degree of bias associated with our results. First, all of the genetic variants were chosen from the ARIC dataset and so are likely to fit the prediction set, also chosen from the same dataset, better than they might in independent samples. We consider this to be of minor concern since our focus is on the added predictive ability of genome-wide predictors, which turns out to be insignificant. Second, although our model is unconditionally highly non-linear in the data because it involves a choosing of features based on their appearances in a succession of cross-validation trees, our model is more restrictive in comparison with more general models such as generalized additive models (GAMs). Such an approach may reduce biases at the expense of added variability in the chosen model. Another possibility may be to divide MAP into intervals  much like the prediction of disease status discussed earlier. However, a major point of our paper is that we are trying to predict actual MAP, and the medical basis for such division is of question. Third, for a quantitative trait such as MAP, an adaptive spline model, which is by design smoother than CART, could be a more efficient alternative than a tree. The adaptive spline model could also suggest interactions in the first stage. We carried out an additional experiment in which CART is replaced by Multivariate Adaptive Regression Splines , we think that our cross-sectional analysis demonstrates reasonably the role of SNPs and other features in a clinically meaningful fashion.R2 measure used to quantify the predictive ability of our algorithm is a population value. Particular individuals at high risk may not be identified using our method.Finally, we also acknowledge that the Table S1List of the ARIC clinical variables and their characteristics used as candidate non-SNP main effects for our adaptive prediction algorithm.(DOC)Click here for additional data file.Table S2Non-SNP features chosen by our adaptive prediction algorithm. For each cutoff fraction of occurrence in the bootstrapped CART, average coefficient and the number of times the corresponding feature is selected over the 10-fold cross validation is presented. (DOC)Click here for additional data file.Table S3SNPs chosen by our adaptive prediction algorithm. For each cutoff fraction of occurrence in the bootstrapped CART, the union of SNPs selected over the 10-fold cross validation is presented as the entry “Y”. (DOC)Click here for additional data file.