Nurr1 (NR4A2) is a transcription factor that belongs to the orphan NR4A group of the nuclear receptor superfamily. Nurr1 plays key roles in the origin and maintenance of midbrain dopamine neurons, and peripheral inflammatory processes. PIASγ, a SUMO-E3 ligase, represses Nurr1 transcriptional activity. We report that Nurr1 is SUMOylated by SUMO-2 in the lysine 91 located in the transcriptional activation function 1 domain of Nurr1. Nurr1 SUMOylation by SUMO-2 is markedly facilitated by overexpressing wild type PIASγ, but not by a mutant form of PIASγ lacking its first LXXLL motif (PIASγmut1). This PIASγmut1 is also unable to interact with Nurr1 and to repress Nurr1 transcriptional activity. Interestingly, the mutant PIASγC342A that lacks SUMO ligase activity is still able to significantly repress Nurr1-dependent transcriptional activity, but not to enhance Nurr1 SUMOylation. A SUMOylation-deficient Nurr1 mutant displays higher transcriptional activity than the wild type Nurr1 only in promoters harboring more than one Nurr1 response element. Furthermore, lysine 91, the major target of Nurr1 SUMOylation is contained in a canonical synergy control motif, indicating that SUMO-2 posttranslational modification of Nurr1 regulates its transcriptional synergy in complex promoters. In conclusion, PIASγ can exert two types of negative regulations over Nurr1. On one hand, PIASγ limits Nurr1 transactivation in complex promoters by SUMOylating its lysine 91. On the other hand, PIASγ fully represses Nurr1 transactivation through a direct interaction, independently of its E3-ligase activity. Nurr1 (NR4A2) is a transcription factor with several functions, but highlights for its key role inducing and maintaining midbrain dopamine neurons of the mammalian central nervous system Previously, we suggested that Nurr1 is SUMOylated since we probed that Nurr1 interacts with PIASγ a SUMO-E3 ligase 363–598 were described previously ctt-cag-atg-ctc-ctg-ggt was changed by gac-gct-cag-atg-gcc-gcg-ggt and for mut2 atg-ctg-gat-gag-ctg-ctg-aag was changed by atg-gcg-gat-gag-gcg-gcg-aag). All mutagenesis and constructs were confirmed by sequencing and correct protein expression was checked by western blot.The hemagglutinin (HA)-Nurr1 expression vector (pCGN-Nurr1) encoding full-length rat Nurr1, the mutant HA-Nurr1-K91R and Myc-Nurr1The reporter plasmid NBRE-3X-tk-Luciferase COS-7, COS-1 and HEK293 cell lines obtained from American Type Culture Collection (ATCC) were cultured in Dulbecco's modified Eagle's medium (DMEM), supplemented with 10% fetal bovine serum (FBS) and maintained at 37°C and 5% CO2, and supplemented with 1% penicillin/streptomycin. PC12 cell line also was obtained from ATCC and cultured in DMEM supplemented with 5% FBS and 10% horse serum, and maintained in 10% CO2. Transfections were carried out using Lipofectamine2000 reagent (Invitrogen).Cells were grown and transfected in 100-mm plate and lysed with 0.9 mL of lysis buffer , supplemented with a cocktail of protease inhibitors and 20 mM of N-ethylmaleimide. Cell extracts were immunoprecipitated as follow: pre-cleared with 10 μL of protein A/G PLUS agarose beads (Santa Cruz Biotechnology) plus 0.5 μg of preimmune mouse IgG, during 1 hour at 4°C. The extracts were recovered by centrifugation and incubated with 1 μg of specific antibody during 3 hours and then with 15 μL of protein A/G PLUS agarose beads for an additional 1.5 hours. The beads were pelleted, and washed two times with lysis buffer and two times with PBS. Finally, bound proteins were eluted by boiling in 2X Laemmli sample buffer. Immunoprecipitated proteins were fractionated by SDS-PAGE and western blots performed with a monoclonal anti-HA antibody (Covance) or a monoclonal anti-Nurr1 antibody .1–158, wild type and mutants, were determined by densitometry on 12% poliacrilamida-SDS gel stained with coomassie blue and compared to a BSA concentration curve. All recombinants GST-PIASγ migrated at the expected molecular weight of about 46 kD (26 kD of GST and 20 kD PIASγ1–158) and a minor band of about 39 kD. Both bands were recognized by anti-PIASγ antibody. To test the interaction between GST-PIASγ1–158 (wild type and mutants) and Nurr1, whole extracts of COS-1 cells transfected with Myc-Nurr1363–598 were incubated with GST-PIASγ fusion proteins or GST alone (1.5 µg) bound to glutathione-agarose for 1 h at 4°C. After extensive washing, the retained proteins were eluted with sample buffer and resolved on 12% SDS-PAGE. Immunoblots were incubated with monoclonal anti-Myc (9E10).GST-Pull-down assays were performed essentially as we have described Cells were sowing in 24-well plates and transfected with 100 ng of the reporter plasmid and an equivalent molar amount of expression plasmids or empty vectors (pCGN or pcDNA3.1+). Total amount of DNA (400 ng) was kept constant by adding pBluescript SR (Stratagene). A reporter gene expressing the β-galactosidase cDNA driven by the cytomegalovirus promoter was cotransfected (20 ng) in all experiments as an internal control for transfection efficiency. Cells were harvested 48 hours after transfection. Luciferase activities were normalized to the activity of the internal control β-galactosidase. Each set of experiments was performed in triplicate and repeated at least three times.The immunofluorescence and co-localization analysis were performed essentially as we have described Statistical analyses – Results are expressed as mean ± S.E.M. from at least three independent assays. Statistical analyses were performed using the non-parametric Mann-Whitney U test.TM software, Nurr1 has four lysines with high score to be SUMO target: lysine 91 and lysine 577 that are in canonical ΨKXE motifs, and lysines 74 and 519 located in non-canonical SUMO sequences, but showing a high score for SUMOylation  in our experimental conditions. This small amount of Nurr1 SUMOylated does not match with the 30–60% increment observed in the transcriptional activity induced by the mutant Nurr1-K91R compared with the wild type Nurr1 . This paWe found that SUMO-2 is the only SUMO peptide post-translational modifying Nurr1. We cannot exclude that Nurr1 is a target of SUMO-1 or SUMO-3 in this or other cellular contexts. However, preferential SUMOylation by one of the SUMO peptides is increasingly being reported Two pieces of information allow us to indicate that PIASγ behaves as the SUMO-E3 ligase of Nurr1. First, overexpressing PIASγ, but not PIASγmut1 that is unable to interact with Nurr1, significantly enhanced Nurr1 SUMOylation. Second, Nurr1 SUMOylation was no longer observed, when PIASγC342A, mutant that lacks SUMO ligase activity, was overexpressed. In addition, we suggest that PIASγ gives specificity and more efficiency to Nurr1 SUMOylation process, since a stronger and better defined band corresponding to Nurr1-SUMO-2 in immunoblots was observed in the presence of PIASγ. Our results allow us to propose that PIASγ exerts two inhibitory mechanisms over Nurr1 transcriptional activity. One mechanism requires its SUMO ligase activity. The SUMOylation of Nurr1 in the lysine 91 by PIASγ would limit Nurr1 transactivity in complex promoters (see below). Indeed, the mutant PIASγC342A exerted lesser repression than wild type PIASγ in reporter assays using complex promoters. The other repressive mechanism is independent of PIASγ SUMO ligase activity; because the mutant PIASγC342A exerted still a strong inhibition of Nurr1-dependent transcriptional activity, and we have shown previously Data from literature indicates that SUMOylation can regulate several features of the transcription factors among them, the nuclear and/or subnuclear localization, stability and/or transcriptional activity Transcription factors interact among them giving synergic responses when they recognize multiple copies of their response element in the target promoters. This synergic regulation of transcriptional activity is restricted by SUMOylation of lysines within SC motifs present in some transcription factors Our study provides evidence that Nurr1 SUMOylation controls its transcriptional synergy in complex promoters. Dopaminergic gene targets of Nurr1, such as tyrosine hydroxylase


Next-generation RNA sequencing (RNA-seq) maps and analyzes transcriptomes and generates data on sequence variation in expressed genes. There are few reported studies on analysis strategies to maximize the yield of quality RNA-seq SNP data. We evaluated the performance of different SNP-calling methods following alignment to both genome and transcriptome by applying them to RNA-seq data from a HapMap lymphoblastoid cell line sample and comparing results with sequence variation data from 1000 Genomes. We determined that the best method to achieve high specificity and sensitivity, and greatest number of SNP calls, is to remove duplicate sequence reads after alignment to the genome and to call SNPs using SAMtools. The accuracy of SNP calls is dependent on sequence coverage available. In terms of specificity, 89% of RNA-seq SNPs calls were true variants where coverage is >10X. In terms of sensitivity, at >10X coverage 92% of all expected SNPs in expressed exons could be detected. Overall, the results indicate that RNA-seq SNP data are a very useful by-product of sequence-based transcriptome analysis. If RNA-seq is applied to disease tissue samples and assuming that genes carrying mutations relevant to disease biology are being expressed, a very high proportion of these mutations can be detected. The transcriptome consists of all RNA transcripts, coding or non-coding, expressed within a given cell or tissue. Its annotation and quantification has been the subject of extensive investigation for several decades. Studying the transcriptome in disease tissue can give important insights into the functional properties of specific RNA transcripts and thereby provide a clearer understanding of the underlying disease processes.Until very recently the predominant means of studying the transcriptome was using hybridisation based methods such as microarrays Next generation sequencing technologies have rapidly changed transcriptome analysis as researchers acknowledge the benefits of RNA sequencing (RNA-seq). This methodology, which allows the direct sequencing of cDNA libraries, allows for more accurate quantification of RNA transcripts in a given cell or tissue RNA-seq can be performed on RNA extracted from disease tissue or blood directly obtained from an individual. For a large number of disease studies it has become increasingly common to generate lymphoblastoid cell lines (LCLs) for patient samples using EBV transformation of blood lymphocytes. This not only provides an unlimited source of patient DNA but gives researchers a valuable source of RNA to use for gene expression/functional studies http://www.1000genomes.org/) project to expand on resources such as HapMap to include rare variation As with transcriptome analysis, array-based genome-wide analysis of DNA sequence variation is being superseded by next-generation sequencing, which offers the opportunity to detect all variants present and not just assay the variants targeted by pre-designed arrays. Whole-genome sequencing is being applied in the 1000 Genomes . In addition to generating data in-house on NA12878, we accessed RNA-seq data for HapMap samples NA12891  and NA12892  from the Short Read Archive . In order to compare these data to our in-house data, we trimmed the reads for these two samples to a length of 40 bp.Total RNA was extracted from the HapMap CEU lymphoblastoid cell line sample  for individual NA12878 using Qiagen’s RNeasy mini kit. Illumina sequencing libraries were prepared according to the Illumina mRNA Sequencing Sample Prep Guide (1004898 Rev. D). Briefly, poly-A-containing molecules were purified and fragmented, followed by double-stranded cDNA synthesis. The resulting double-stranded cDNA was end-repaired before ligation of Illumina-specific adaptors and finally was PCR enriched. Sequencing was carried out on an Illumina Genome Analyzer II to produce three lanes (A–C) of 40 bp single-end reads  see The first decision was if and when to remove identical duplicate reads from analysis. Duplicate reads in sequence data can occur during the PCR/library preparation steps, from sequencing artefacts such as poly-A and poly-N reads, noise in cluster detection and from cDNA fragmentation at the same location in different molecules Tophat version 1.4.1 was selected for alignment of the generated RNA-seq reads with two mismatches allowed per uniquely aligned read. We wanted to compare SNP calls generated when aligning reads to the reference genome compared to a transcriptome-only approach and therefore each sample was aligned using both duplicate read methods to the reference genome (NCBI build 36.1) and to the associated refFlat gene list from UCSC, which represents the transcriptome see . In addihttp://browser.1000genomes.org/index.html), and dbSNP build 132.To summarize, eight different variant calling strategies were carried out. In strategy A, identical sequences were removed pre-alignment and the remaining reads were aligned to both a transcriptome reference and a whole genome reference. Two SNP callers were then applied to the alignment files generated, resulting in four different variant call sets for strategy A: pre_trans_sam and pre_trans_gatk are the call sets generated following alignment to the transcriptome alone and pre_genome_sam and pre_genome_gatk are the corresponding files generated following whole genome alignment. Strategy B follows an identical protocol except the removal of duplicate reads post-alignment to the relevant reference. Similarly four variant call sets were generated for this strategy: post_trans_sam and post_trans_gatk following transcriptome alignment and post_genome_sam and post_genome_gatk from the genome alignments. The sensitivity and specificity of each SNP was assessed by the number of reads aligned at each individual base (providing this was greater than 3× coverage). SAMtools derived SNP calls were generated with default pileup settings and standard SNP filters. GATK derived SNP calls were filtered using standard GATK SNP filters. All calls were subsequently filtered to remove sites overlapping regions where no variant calls were attempted in the 1000 Genomes pilot study 2 RNA-seq reads are sampled from a gene in a manner proportional to its expression level in a given tissue. As a result, the coverage of variants varies greatly from gene to gene. In addition, mapping efficiency of individual nucleotide sites is dependent on a number of variables including regional GC content and uniqueness, and means that non-uniform base coverage across a gene or exon can occur, which will influence SNP discovery. As a consequence the definition of each gene in the transcriptome as expressed or not expressed based on median or mean base coverage per exon The different metrics of RNA-seq SNP detection that we wanted to measure were the number of SNPs called per sample and the specificity and sensitivity of those SNP calls. The 1000 Genomes pilot study 2 SNP calls were used to determine the accuracy of calls made using RNA-seq data. All 1000 Genomes variant sites with > = 3X coverage in our data were treated as expected calls for this individual and used to define both the specificity and sensitivity of cDNA derived variant calls. Specificity was calculated as the number of true positives divided by the number of true positives plus the number of false positives. A true positive was any SNP present in our sample data and the corresponding 1000 Genomes and/or dbSNP data that had the correct genotype or in the case of dbSNP had the expected alleles at that position. A false positive SNP call is where the genotype in our data did not match 1000 Genomes data or the variant was not present in dbSNP. As we were dealing with transcriptome data as opposed to full genome data this meant that we will only be able to detect SNPs at sites expressed in our samples. To perform sensitivity analysis we calculated the number of true positives divided by the number of true positives plus the number of false negatives. A true positive was where we detected the expected genotype and a false negative is where we either did not detect a SNP at that position, or did detect a SNP but the genotype did not match.SNPs identified in our RNA-seq data for which no corresponding variant was reported in the 1000 Genomes pilot study release were further examined in both dbSNP132 and the publicly available 1000 Genomes alignment (BAM) files. SNPs with a matching position and identical alternative allele call in dbSNP were considered as potentially true variants. In addition, corroborating evidence for the RNA-seq variant call was examined in the 1000 Genomes alignment files because not all true variants will have passed the filters employed in that study. For heterozygote calls we required the alternative allele to occur in between 20 and 80% of the 1000 Genomes aligned reads to provide potential evidence of a true variant occurring at a site. For homozygote non-reference calls we required >90% of reads in the 1000 Genomes alignment to carry the predicted alternative allele at a site.The overview The hg18 refFlat gene set of 23,147 unique genes is composed of 212,392 exons. Aligning 40,784,356 single-end 40 bp fragments resulted in at least one sequence read mapping to 17,014 genes and 139,143 exons. 82,091 exons had complete coverage at 1× or greater across their full length. The 23,147 genes in the refFlat gene set span 67,893,145 base pairs in the hg18 genome release. Consistently across all alignment methods employed here, 50% of these bases were not covered by a single read and 12% of the bases in the transcriptome have 1–2× coverage. The remaining 38% of sites had 3× or greater coverage and represent the total number of transcriptome spanning sites at which a genotype call was attempted in this study.The specificity of NA12878 SNP calls based on 1000 Genomes data for each of the eight methods at varying coverage depths are displayed in For each of the calling strategies we noted a substantial number of sites which had not been identified as variant in the available 1,000 Genomes data . AlthougTo perform sensitivity analysis we identified SNPs that had known genotypes from 1000 Genomes data for NA12878 and were located at sites covered by at least 3 reads in our RNA-seq data. To investigate if the results from our analyses were reproducible when applied to other RNA-seq datasets, we applied the same methods of SNP detection to online RNA-seq data for NA12891 and NA12892. Importantly, these two samples have also been whole genome sequenced to a deep coverage by 1000 Genomes so two comprehensive sets of SNP calls are available to compare against our SNP calls from the RNA-seq data. As methods employing the removal of duplicate reads post-alignment performed best for our in-house sample, we just present data from these four methods for the two online samples.More sequence reads were available for these two samples  compared to our in-house sample . When aligning to transcriptome, SNP numbers are similar in all three samples for all calling methods tested . HoweverWhen we calculate specificity and sensitivity across our range of methods, we find that the results for the two online samples are near identical . This hiThis study examined next-generation transcriptome sequencing (RNA-seq) as a method of expressed SNP detection in LCLs. Many disease samples are now biobanked as LCLs and RNA from these cell repositories are routinely used for scientific investigation. As well as data on expression and splicing, RNA-seq data can be used for SNP detection. It is therefore important to determine the best strategy for RNA-seq SNP analysis and we have addressed this question by quantifying the specificity and sensitivity of different alignment and SNP-calling methods. These results are also relevant to non-LCL derived RNA, e.g. from a different cell line type or from a disease tissue sample, because they inform on the parameters required for accurate SNP calling in RNA-seq data.We explored different strategies for RNA-seq SNP detection by dropping duplicate sequence reads either pre- or post-alignment and using different reference data for alignment (genome and transcriptome) and different SNP-calling algorithms , ENSEMBL  and UCSC (based on protein data from Swiss-Prot/TrEMBL (UniProt) and the associated mRNA data from Genbank). Each of these gene sets contains significant differences from the others with both the RefSeq and UCSC datasets being more conservative than the ENSEMBL predictions. Even allowing for the differences between these three datasets, a significant proportion of the SNPs called following alignment to the genome occur in regions outside any annotated gene region and would be missed by any analysis using the transcriptome alone. This finding is of particular relevance to variant calling studies in organisms without a fully sequenced genome. Transcriptome Significant overlap exists between the calls sets generated by the two SNP callers used in this study. On average 98% of GATK derived calls are found in the SAMtools call set generated on the same alignments, while approximately 91.5% of SAMtools calls overlap with those of GATK, reflecting the higher number of SNP calls by SAMtools. Comparison of results for both calling strategies employed in this study indicates that sensitivity and specificity calculations for variant calls made at > = 10× coverage using both SAMtools and GATK are virtually identical . At coveCirulli et al. Genome mappability is influenced by both the local genomic content and the sequencing strategy employed. A recent study has produced an algorithm for determining the mappability of each base in any reference genome using a user-specified read length and mismatch number What is the cause of false positive SNP calls in our data? The majority of these discordant genotypes occur at sites of <10× coverage . At >10×Our sensitivity analysis addressed the issue of what proportion of true variants will be detected in a RNA-seq SNP analysis. This again very much depends on coverage of the site irrespective of the overall quantity of data produced for a test sample. For all analysis methods tested here, overall sensitivity is >90% when there is >10X coverage but can drop as low as 40% if a site has 3× coverage. As expected, the majority of false negative sites in our RNA-seq sample occur at heterozygous sites where we have low coverage . At suchThere are a number of additional factors that affect both sensitivity and specificity. All calculations in this study are based on 1000 Genomes data being correct. Comparison of available HapMap and 1000 Genomes data for sample NA12878 identified 1,964,991 SNPs common to both datasets; however genotypes did not match for 2% of these SNPs. Other factors to consider are errors in the RNA-seq data (sequencing errors or artefacts of the EBV cell line transformation), allelic imbalance in the RNA, random mono-allelic expression in clonal cell lines We have shown that accurate variant detection is possible using cDNA derived sequencing reads at sites with >10× coverage. In our study of a LCL sample, SNP calling was possible at 38% of the annotated transcriptome. The prohibitive cost associated with sequencing to the required depth, to accurately call all expressed variants in a sample, means that while RNA-seq variant calling works well, it is not a viable alternative to exome or whole genome sequencing. Use of longer and/or paired-end reads will increase the base coverage within expressed genes in a sample and as a result increase the sensitivity of the SNP calls generated, but this will be limited to SNPs occurring in relatively highly expressed genes.In conclusion, SNP calls in RNA-seq data is a useful by-product of the technique and boosts the amount of data that can be generated from such experiments and potentially used in convergent functional genomics research. We found that in addition to the computational benefit of reducing the number of reads to be processed in downstream steps, removing duplicate sequences post-alignment will increase the quality SNPs called overall. We found that aligning sequence reads to a genomic rather than transcriptomic reference will increase the number of SNPs called, with the additional SNPs occurring at bases not currently curated as genic regions in human genome annotation sets and greater specificity is achieved when aligning to the genome. SAMtools identifies 8–10% more variants that GATK and thus detects more true SNPs than GATK (higher sensitivity) but also calls more false positives than GATK at low coverage (lower specificity). When we applied our methods to multiple RNA-seq datasets that had being prepared and sequenced with the same methods (online samples NA12891 and NA12892), the results for metrics of SNP detection were very reproducible. When we compared these data to our original in-house sample (NA12878), the main difference was the overall number of SNPs detected. This indicated that the number of sequence reads used in an analysis and original preparation of the sequencing library will influence SNP detection. Despite this large difference in SNP number, the only differences of note between the samples were found at lower coverages. At >10× coverage, specificity and sensitivity were very similar, again highlighting the reproducibility of the methods. Overall, using appropriate methods and filters, a very high proportion of SNPs called in RNA-seq data will be true variants and RNA-seq SNP analysis will identify the majority of variants present in expressed exons provided sufficient coverage is available.Figure S1Number of SNPs per method in RNA-seq data.(PDF)Click here for additional data file.Figure S2Specificity of the SNP calls from RNA-seq data for NA12891.(PDF)Click here for additional data file.Figure S3Specificity of the SNP calls from RNA-seq data for NA12892.(PDF)Click here for additional data file.Figure S4Sensitivity of the SNP calls from RNA-seq data for NA12891.(PDF)Click here for additional data file.Figure S5Sensitivity of the SNP calls from RNA-seq data for NA12892.(PDF)Click here for additional data file.Table S1Number of sequence reads per sample per method.(XLS)Click here for additional data file.Table S2Analysis results for all three RNA-seq sample datasets.(XLS)Click here for additional data file.


FST. The divergence separating the Florida and Louisiana Native (FST = 6.2%) was approximately 50% higher than the average divergence separating breeds developed within the same region of Europe (FST = 4.2%). This strongly indicated that the two Gulf Coast Native lines are sufficiently different to be considered separate breeds. PCA using small SNP sets successfully distinguished between the Florida and Louisiana Native animals, suggesting that allele frequency differences have accumulated across the genome. This is consistent with a population history involving geographic separation and genetic drift. Suggestive evidence was detected for divergence at the poll locus on sheep chromosome 10; however drift at neutral markers has been the largest contributor to the genetic separation observed. These results document the emergence of populations that can be considered separate breeds, with practical consequences for bio-conservation priorities, animal registration and the establishment of separate breed societies.Domestic animals are unique in that they have been organised into managed populations called breeds. The strength of genetic divergence between breeds may vary dependent on the age of the breed, the scenario under which it emerged and the strength of reproductive isolation it has from other breeds. In this study, we investigated the Gulf Coast Native breed of sheep to determine if it contains lines of animals that are sufficiently divergent to be considered separate breeds. Allele sharing and principal component analysis (PCA) using nearly 50,000 SNP loci revealed a clear genetic division that corresponded with membership of either the Florida or Louisiana Native lines. Subsequent analysis aimed to determine if the strength of the divergence exceeded that found between recognised breed pairs. Genotypes from 14 breeds sampled from Europe and Asia were used to obtain estimates of pair-wise population divergence measured as  Sheep were first domesticated around 11,000 years ago for access to meat, before specialised breeds were subsequently developed to match animals to their captive environments. Today there are more than 1400 sheep breeds Knowledge describing the strength of genetic division that exists between breeds has a number of important applications. Firstly, the degree of divergence can be used to direct prioritisation of resources available for the conservation of biodiversity Where substantial genetic separation has been identified between subpopulations within a breed, it becomes relevant to investigate if the divergence is sufficient to consider the subpopulations as separate breeds. This may trigger the establishment of separate breed societies to manage animal recording and accommodate divergent breeding objectives. It is important to note that the categorization of animals into breeds has a lot to do with non-genetic factors as diverse as human cultural identity, history and politics. This might explain why surprisingly little has been published concerning the minimum divergence required to declare subpopulations as separate breeds, given genetic distinction is not the sole determinant. While recognising non-genetic factors are important, the recent International Sheep Genomics Consortium's (ISGC) HapMap and Breed Diversity experiment offers the opportunity to explore this question at previously unattainable resolution through the use of 49,034 SNP In this study, we first evaluated the ability of a dense set of SNP markers to detect population substructure within the Gulf Coast Native breed while ignoring line membership. Further, we calibrated the strength of substructure detected against a series of population comparisons to evaluate if the lines could be considered sufficiently diverse to be declared separate breeds. In the process the results document the emergence of domestic breeds and provide insights into the mechanisms involved.AS) matrix, when visualised as an ordered heat-map, revealed two distinct groups of animals within the breed  HapMap and Breed Diversity experiment He) and the proportion of polymorphic SNP (Pn), is given in FST at four levels: a) selection lines within the same breed (e.g. Meat and Milk Lacaune); b) separate breeds developed in the same region of Europe (e.g. Merino and Rambouillet); c) separate breeds developed in different regions of Europe (e.g. Merino and Poll Dorset) and d) separate breeds developed in either Europe or Asia (e.g. Merino and Tibetan). SNP genotypes from a total of 921 animals were used in a single analysis to estimate the divergence between every combination of the populations. The breeds used, the number of animals and the FST separating each population pair are listed in FST, = 6.2%  and top 5% (2451 SNP) of markers. To ensure ranking on FST did indeed enrich for SNP explaining the divergence between lines, individuals were clustered using the top 5% and top 1% of FST ranked SNP  contained an excess compared to the number expected based on chromosome size (FST SNP which is more than double the proportion expected (3.8%). Chromosome 10 is of particular interest given it harbours the Poll locus responsible for the absence of horns Poll locus to compare haplotype frequencies present within the Australian Poll Merino, Merino, Florida Native and Louisiana Native. Genotypes at four SNP spanning 77 Kb at the Poll locus were phased into haplotypes, and their frequency is given in Analysis was performed to identify the subset of SNP which contributed most to the observed divergence between the Florida and Louisiana Native. SNP were ranked using nked SNP . This cht random . Plottinome size . ChromosThe population history of the Gulf Coast Native was explored by comparing them to a selection of four Spanish breeds, a meat type breed from the United Kingdom and two Asian breeds that served as outgroups. Allele sharing was calculated between 921 animals  and usedFST. To generate a type of calibration curve of FST values, other breeds were used that are expected to have increasing genetic separation. At one extreme pairs were examined using lines within the same breed while at the other extreme pairs were examined using breeds developed in Europe and Asia. These comparisons provided a guide to the percent values for FST to be expected when calculated using 50,000 SNP, and the values increased in a step-wise fashion for the four comparison types (FST = 6.2%) was higher than the average value separating recognised breed pairs developed in Mediterranean Europe  samples were from six breeders distributed across Florida, Georgia and Texas, while the Louisiana Natives samples were collected from six breeders in Arkansas, Louisiana, Missouri and Texas. Five animals were sampled from a Florida flock of the ‘cracker’ line, and these are annotated separately from the Florida Native and Louisiana native individuals in All DNA samples were genotyped using the Illumina Ovine SNP50 BeadChip as part of the ISGC HapMap and Breed Diversity experiment Pn) and gene diversity (He) from the full SNP dataset using PLINK v2.05 Ar), which measures the normalised number of alleles and private allelic richness (pAr), which gives a measure of population distinctiveness, were calculated using ADZE As) was calculated as (IBS2+0.5*IBS1/N) where IBS1 and 2 are the number of loci that share either 1 or 2 alleles and N is the number of SNP pairs tested. These values were calculated using PLINK v2.05 As values was used to generate an ordered heatmap and dendogram using R software package RColorBrewer. Principal components analysis (PCA) of As values was performed using smartpca implemented in Eigensoft FST and as global FST using the methods of Nicholson and colleagues Within breed diversity was calculated as the proportion of polymorphic SNP  and Merino populations  were collected as unrelated industry sires in Australia as part of the ISGC HapMap experiment OAR10_29469450, OAR10_29511510, OAR10_29538398 and OAR10_29546872) which span the Poll locus on sheep chromosome 10 SNP50 genotypes from four populations were used for the analysis of the Table S1Basic indices of genetic diversity measured within breed. Breeds are listed with decreasing expected heterozygosity or gene diversity (He). Other measures include the proportion of SNP displaying polymorphism (Pn); the inbreeding coefficient (F); allelic richness (Ar) and private allele richness (pAr). These results are taken from the ISGC HapMap and Breed Diversity Experiment (DOC)Click here for additional data file.


Genome-wide association analysis is a powerful tool for annotating phenotypic effects on the genome and knowledge of genes and chromosomal regions associated with dairy phenotypes is useful for genome and gene-based selection. Here, we report results of a genome-wide analysis of predicted transmitting ability (PTA) of 31 production, health, reproduction and body conformation traits in contemporary Holstein cows.GNAS region for milk, fat and protein yields; BTA7's INSR region and BTAX's LOC520057 and GRIA3 for daughter pregnancy rate, somatic cell score and productive life; BTA2's LRP1B for somatic cell score; BTA14's DGAT1-NIBP region for fat percentage; BTA1's FKBP2 for protein yields and percentage, BTA26's MGMT and BTA6's PDGFRA for protein percentage; BTA18's 53.9-58.7 Mb region for service-sire and daughter calving ease and service-sire stillbirth; BTA18's PGLYRP1-IGFL1 region for a large number of traits; BTA18's LOC787057 for service-sire stillbirth and daughter calving ease; BTA15's CD82, BTA23's DST and the MOCS1-LRFN2 region for daughter stillbirth; and BTAX's LOC520057 and GRIA3 for daughter pregnancy rate. For body conformation traits, BTA11, BTAX, BTA10, BTA5, and BTA26 had the largest concentrations of SNP effects, and PHKA2 of BTAX and REN of BTA16 had the most significant effects for body size traits. For body shape traits, BTAX, BTA19 and BTA3 were most significant. Udder traits were affected by BTA16, BTA22, BTAX, BTA2, BTA10, BTA11, BTA20, BTA22 and BTA25, teat traits were affected by BTA6, BTA7, BTA9, BTA16, BTA11, BTA26 and BTA17, and feet/legs traits were affected by BTA11, BTA13, BTA18, BTA20, and BTA26.Genome-wide association analysis identified a number of candidate genes and chromosome regions associated with 31 dairy traits in contemporary U.S. Holstein cows. Highly significant genes and chromosome regions include: BTA13's Genome-wide association analysis identified a number of genes and chromosome regions associated with 31 production, health, reproduction and body conformation traits in contemporary Holstein cows. The results provide useful information for annotating phenotypic effects on the dairy genome and for building consensus of dairy QTL effects. Genome-wide association studies (GWAS) using single nucleotide polymorphism (SNP) markers provide a powerful approach for annotating phenotypic effects or mapping QTL of important dairy traits on the genome. Dense genome coverage allows detection of QTLs with greater accuracy than was previously possible -3. CombiIn this study, we conducted genome-wide association analysis of 31 production, health, reproduction and body conformation traits of contemporary U.S. Holstein cows. We used the bovine 50 k SNP panel to identify SNP markers, genes and chromosome regions on the 29 bovine autosomes and the X chromosome associated with these 31 economically important dairy traits. Production, health and reproduction are fundamental dairy functions while body conformation (type) traits are related to functionality of the cow's body and are related to value of the cow as a show animal.P value < 10-6.4) for each trait. Therefore, only the top 100 effects for each trait are reported for a total of 3,100 effects of 1,586 SNPs with 573 (36%) in 486 genes based on the Btau_4.0 and UMD 3.0 genome assemblies. Of the 45,878 SNPs that were genotyped, 16,516 (36%) were in 7,434 genes. The majority of the 1,586 SNPs each affected one trait, whereas 27 SNPs each affected 10 or more traits  autosomes and the X chromosome but the distribution was uneven, with certain chromosomes having large numbers of SNP effects, and different chromosomes generally were associated with different traits  and milk yield had the lowest significance (P < 10-12) (Table P < 10-21) and rump angle had the lowest significance (P < 10-11) (Table The top 100 SNPs for each trait accounted for 38% to 56% of the PTA variation Table . Signifi P < 10-1 (Table Pinsulin receptor (INSR) gene in this cluster was 1.5 kb from the SNP with the most significant effect on somatic cell score and daughter pregnancy rate, and was third most significant for productive life. A narrow 2.81 Mb region of BTA14 with approximately 125 genes  gene, and one SNP effect each for fat yield and protein percentage in the NIK and IKKβ binding protein (NIBP) gene. A SNP in the diacylglycerol O-acyltransferase homolog 1 (DGAT1) gene  gene in this cluster  gene and IGF-like family member 1 (IGFL1) gene in this cluster flanked a SNP that was highly significant for many traits. This SNP had the top effect for fat and protein yields, service-sire and daughter calving ease, and net merit; the eighth most significant effect for milk yield and service-sire stillbirth; the 16th most significant effect for productive life, and the 25th most significant effect for fat and protein percentages. The zinc finger protein 415-like gene (LOC787057) was most significant for service-sire stillbirth and second most significant for daughter calving ease. The 211.67 kb MOCS1-LRFN2 region of BTA23  locus of BTA13  gene (PHKA2) was highly significant for body size traits. The top SNP effects in the 80 to 90 Mb region of BTA11 predominantly affected stature, strength, body depth, rear udder height, teat length, rear legs (rear view), feet/legs score, and final score  at the top telomere region of BTA16 was highly significant for 12 traits (the largest number of conformation traits affected by one gene). The 65 to 75 Mb region of BTA16  gene region on BTA26  with the traits in terms of statistical significance and known relevant biology, making these regions and genes more likely candidates for causal effects. The following is a brief summary of such regions and genes.GNAS was highly significant for the three yield traits  with a highly significant effect for fat yield, fat percentage and protein percentage . Trait-specific regions include the MIR2353-STK39 and LRP1B (low density lipoprotein-related protein 1B) regions of BTA2 for somatic cell score, the 129 to 141 Mb region of BTA1, the 90 Mb region of BTA3 for daughter pregnancy rate, and the ATP1B4  and GRIA3 (glutamate receptor 3) genes on BTAX for daughter pregnancy rate.These three traits had two common regions and some trait-specific regions. The first common region for these three traits was the 15.4 Mb BTA7 gene cluster of approximately 1,166 genes Figure . AlthougProductive life measures the cow's longevity in the herd and is affected by production, health and reproduction. Somatic cell score is a measure of udder health and daughter pregnancy rate is a measure of cow fertility. The fact that productive life shared many common SNP effects with somatic cell scores and daughter pregnancy rate and did not share many SNP effects with production and calving traits indicates productive life was more genetically related with health and fertility traits than with production and calving traits.PGLYRP1-IGFL1 region and LOC787057 had the most significant effects for these three traits  on BTA23.Nine of ten SNPs in the 211.67 kb h Figure , which iHighly significant chromosome regions and genes for this composite trait involved the significant regions and genes for milk, fat and protein yields, protein percentage, service sire calving ease, daughter calving ease, and service sire stillbirth  associated protein 8 gene (GEMIN8) to the glycoprotein M6B gene (GPM6B)  Figure  includedlipin 1 , tribbles homolog 2 , odd-skipped related 1 (OSR1), aromatic-preferring amino acid transporter-like (LOC529399), and neuroblastoma amplified sequence (LOC521982) , depth (ranked #4) and rump width (ranked #1). This gene was located in the middle of a 70-kb gene cluster with five tightly linked genes: SRY (sex determining region Y)-box 13 (SOX13), ethanolamine kinase 2 (ETNK2), REN, KiSS-1 metastasis-suppressor (KISS1), and golgi transport 1A (GOLT1A)  gene was 70 kb downstream of these five tightly linked genes and had the ninth most significant SNP effect for strength.The pitrilysin metallopeptidase 1 gene (PITRM1), which also was linked to two of the top 20 effects for stature and three of the top 20 effects for body depth. A SNP on BTA23 at position 18,197,600 bp had the second most significant effect for body depth and was the third most significant for strength.The most significant BTA13 SNP effect for strength was ranked seventh for the trait and was in the GPRC5C (ranked 3rd), RNF157 (ranked 5th), SRP68 (ranked 8th), LOC789539 (ranked 11th), and OTOP3 (ranked 15th). The six most significant BTA19 SNPs were in a 1.8 Mb region of 57.2 to 59.0 Mb  of BTA1, myocyte enhancer factor 2C (MEF2C) of BTA7, and phospholipase A2, group IVF (PLA2G4F) and calpain 3 (p94) (CAPN3) of BTA10. For rump angle, BTA5 and BTA9 had the most significant effects  and SH3 and multiple ankyrin repeat domains 2 genes (LOC618649) on BTA29.Although both dairy form and rump angle are measures of the cow's body shape, these two traits likely involved different genes. The top 20 SNP effects for dairy form involved eight chromosomes  gene had the first and fourth most significant effects, and two BTA10 SNPs in the adenomatous polyposis coli (APC) and CAPN3 genes had the second and third most significant effects, respectively. The most significant effects for udder depth were located on BTA25, BTA22, BTA7, BTA26, BTA20 and BTA23. The SNP 1.5 kb downstream of INSR on BTA7 was the tenth most significant SNP for udder depth  and Wolf-Hirschhorn syndrome candidate 2 (WHSC2) genes. The same BTA6 and BTA7 SNP markers were also highly significant for teat placement traits, which indicated that udder cleft and teat placement involved some common genes. The tenth most significant SNP for udder cleft was on BTA19 SNP and was just downstream from a gene cluster that affected rump width and fore udder attachment  genes were the top two most significant SNPs for front teat placement and were among the top 20 effects for rear teat placement , was highly significant for both rear teat placement and udder cleft. The TAF1 RNA polymerase II, TATA box binding protein-associated factor, 250 kDa gene (TAF1) on BTAX had the second most significant SNP effect for rear teat placement and the 16th for udder cleft. The GPRC5C gene on BTA19  and one SNP in the abhydrolase domain containing 2 gene (ABHD2).Front and rear teat placements involved different and common SNP effects. Teat length and teat placement traits appeared to have been associated with different genes. Two BTA6 SNPs in the MGMT had the top three effects for foot angle, and another four BTA26 SNPs were also among the top 20 effects for foot angle  rather than the physical distances (Mb or kb) for QTL locations reported in this study. Because exact and complete translation of bovine genetic distances into physical distances is not available, the results in this study could be compared only with studies that reported genes or markers with known physical locations.DGAT1 was important for fat percentage , which was significant for somatic cell score (1st) and daughter pregnancy rate (1st), productive life (3rd), and net merit (78th)] as well as all 10 significant SNP effects on this chromosome for productive life, 15 of the 16 significant SNP effects for daughter pregnancy rate, and 6 of the 12 significant SNP effects for somatic cell score. B) BTA14 2.81 Mb gene cluster  of ~125 genes with a mean of 44 genes/Mb; NIBP (highlighted in green) was the largest gene (387.23 kb) in the cluster. This cluster included 19 significant SNP effects for fat percentage [SNP in DGAT1 was 1st and SNP in NIBP was 2nd], one SNP in VPS28 with effect for milk yield, and one SNP in NIBP with effects for fat yield and protein percentage. C) BTA18 15.82 Mb gene cluster (Btau_4.0:48755332-64574451) of ~1,322 genes with a mean of 83 genes/Mb. The PGLYRP1(PGRP)-IGFL1 (highlighted in green) region had the most significant SNP effects in the cluster . D) BTA3 gene cluster with significant SNPs for fat yield [BFGL-NGS-113990 (49th) and INRA-304 (53rd)]. E) BTA5 gene cluster with ARS-BFGLNGS-36745 (associated with SREBF2) and ARS-BFGL-NGS-71946 in LOC535121, which were among top 100 SNP effects for fat and protein percentages and service-sire calving ease and stillbirth, as well as other significant SNPS for service-sire stillbirth . I) BTA17 gene cluster with significant SNPs for milk yield [(ARS-BFGL-NGS-17192 (39th) and BTB-01992588 (63rd)] and fat percentage [ARS-BFGL-NGS-34106 (19th) and Hapmap40427-BTA-41914 (78th)]. J) BTA21 gene cluster with significant SNP effects for service-sire calving ease [BFGL-NGS-113671 (87th) and service-sire stillbirth [BFGL-NGS-116152 (46th), Hapmap39755-BTA-52639 (73rd), and BFGL-NGS-113671 (83rd). K) BTA23 gene cluster with ARS-BFGL-NGS-72191 in ZNF192 and BTA-68781-no-rs, which were among top 100 SNP effects for fat yield, protein yield and percentage, service-sire calving ease, and net merit. L) BTA26 gene cluster with BFGL-NGS-111739, the 14th most significant SNP for service-sire stillbirth. M) BTA29 gene cluster with ARS-BFGL-NGS-24998 in LOC787296, 49th most significant SNP for daughter stillbirth. N) BTAX gene cluster with ARS-BFGL-NGS-42972 in TIMP1, 45th most significant SNP for service-sire stillbirth.Click here for fileFigure S5. Examples of gene regions associated with significant effects of SNPs for daughter stillbirth and milk yield. A) The 211.67 kb MOCS1-LRFN2 region of Bos taurus (BTA) chromosome 23 with nine highly significant effects (red arrows) for predicted transmitting ability (PTA) for daughter stillbirth. B) The most significant SNP effect (red arrow) for daughter stillbirth was 23.9 kb upstream from the second CD82 gene on BTA15. C) The BTA13 region with the most significant SNP effect (19.7 kb downstream from GNAS2 or GNAS) for PTA for milk yield.Click here for fileFigure S6. Examples of gene regions associated with significant SNP effects for body conformation traits; a boxed gene contained at least one top-100 effect, and numbers above red arrows (significant SNP effects) indicate rank of SNP effect. A) The 1 Mb region of Bos taurus (BTA) chromosome X with the most significant SNP effect (LOC515732 is PHKA2) for predicted transmitting ability (PTA) for stature. B) The 10.2 Mb region of BTA 11 with eight of the top 20 SNP effects for stature. C) The 1 Mb region of BTA16 with the most significant SNP effect (REN) for PTA for strength. D) The 1.75 Mb region of BTA19 with five genes with highly significant SNP effects for rump width and udder cleft. E) A BTA21 gene cluster with three genes associated with teat length.Click here for fileTable S3. Output file of 725 SNP markers with top 100 effects per trait from least squares analysis and weighted least squares analysis. MY, milk yield; FY, fat yield; PY, protein yield; FPC, fat percentage; PPC, protein percentage; SCS, somatic cell score; DPR, daughter pregnancy rate; PL, productive life; SCE, sire calving ease; DCE, daughter calving ease; SSB, sire stillbirth; DSB, daughter stillbirth; NM, net merit. Subscript 'w' indicates P-values from weighted least squares analysis. Trait name with subscript 'w' indicates tests using weighted least squares, and trait name without subscript indicates tests using least squares.Click here for file


Fagopyrum esculentum (common buckwheat), the occurrence of dimorphic flowers, namely short-styled and long-styled flowers, is associated with a type of self-incompatibility (SI) called heteromorphic SI. The floral morphology and intra-morph incompatibility are both determined by a single genetic locus named the S-locus. Plants with short-styled flowers are heterozygous (S/s) and plants with long-styled flowers are homozygous recessive (s/s) at the S-locus. Despite recent progress in our understanding of the molecular basis of flower development and plant SI systems, the molecular mechanisms underlying heteromorphic SI remain unresolved. By examining differentially expressed genes from the styles of the two floral morphs, we identified a gene that is expressed only in short-styled plants. The novel gene identified was completely linked to the S-locus in a linkage analysis of 1,373 plants and had homology to EARLY FLOWERING 3. We named this gene S-LOCUS EARLY FLOWERING 3 (S-ELF3). In an ion-beam-induced mutant that harbored a deletion in the genomic region spanning S-ELF3, a phenotype shift from short-styled flowers to long-styled flowers was observed. Furthermore, S-ELF3 was present in the genome of short-styled plants and absent from that of long-styled plants both in world-wide landraces of buckwheat and in two distantly related Fagopyrum species that exhibit heteromorphic SI. Moreover, independent disruptions of S-ELF3 were detected in a recently emerged self-compatible Fagopyrum species and a self-compatible line of buckwheat. The nonessential role of S-ELF3 in the survival of individuals and the prolonged evolutionary presence only in the genomes of short-styled plants exhibiting heteromorphic SI suggests that S-ELF3 is a suitable candidate gene for the control of the short-styled phenotype of buckwheat plants.The different forms of flowers in a species have attracted the attention of many evolutionary biologists, including Charles Darwin. In  Fagopyrum esculentum (common buckwheat) is an agronomically important species that exhibits heteromorphic SI , which has been observed in 28 angiosperm families, is associated with distinct variations in floral features, such as style length, stamen length, pollen size and intramorph incompatibility rphic SI . The floS/s genotype produce only pollen that exhibit the S phenotype and not the s phenotype. Previous studies pointed out that heteromorphic SI is unlikely to be related to homomorphic sporophytic SI based on the phylogenetic independence between these two types of SI, and on the difference in timing of pollen rejection between these two systems The molecular mechanism underlying plant SI has been investigated over the past few decades. Three homomorphic SI systems, in which no flower morph variations accompany SI, have been examined in detail. These include sporophytic SI, which is based on the SP11/SCR-SRK-mediated signaling cascade, in the mustard family PrimulaTurneraPrimula, in which at least three genes are present at the S-locus S-locus, including two genes differentially expressed between the two morphs, were identified S-linked markers were successfully assembled using P. vulgaris bacterial artificial chromosome (BAC) genomic libraries Turnera, a high-resolution linkage map S-locus were constructed. Positional cloning using the S-linked markers furthermore identified BACs containing the genomic region of the s haplotype Recent molecular biological and genetic studies in the heterostylous S-locus F. homotropicum, which has an hS allele at the S-locus, generated self-compatible lines (KSC2 and Kyushu PL4). An analysis of the floral morphs and self-incompatibility responses of KSC2 suggested the presence of multiple genes at the S-locus Fagopyrum display heteromorphic SI and only a few species display homomorphic SC Regarding buckwheat heteromorphic SI, random amplified polymorphic DNA (RAPD) markers and amplified fragment length polymorphism (AFLP) markers were identified around the S-LOCUS EARLY FLOWERING 3 (S-ELF3).Here, we sought to determine the molecular basis of buckwheat heteromorphic SI by identifying the primary factor(s) involved in this process. By integrating a variety of genetic and molecular approaches, including transcriptome analysis, mutagenesis screening and evolutionary genetic analysis, we identified one candidate gene, S-haplotype-specific and/or short-style-specific gene (SSG) by subtracting genes expressed in long-styled plants (s/s genotype) from genes expressed in short-styled plants (S/s genotype), RNA was isolated from each of the two distinct floral types of a sib-mating line of buckwheat. The plant line utilized was derived from a single pair of short-styled and long-styled plants and sib-mating was conducted for generations (BC1-F5); therefore, the genetic difference between plants of different morphs was largely reduced, which facilitated the screening for differentially expressed genes at the S-locus. Furthermore, this line was previously used to construct a BAC genomic library S-locus genomic region of the S haplotype derived from a single chromosome. Total RNA isolated from the two distinct types of floral styles was then separately subjected to high-throughput sequence analysis using an Illumina GAII sequencer. The analysis yielded 7,371,322 pairs of 50mer reads for short-styled plants, from which 41,599 contigs of various length  were assembled by the Velvet program. Analysis of RNA isolated from long-styled plants yielded 2,522,996 pairs of 50mer reads and 3,938,668 pairs of 51mer reads, which were used to examine if the fragments assembled for short-styled plants contained the reads obtained from long-styled plants. This in silico subtraction procedure eliminated most of contigs and only 15 contigs remained as SSG candidates. RT-PCR analysis of these 15 contigs showed that only four were exclusively expressed in short styles. We tentatively named these four genes SSG1–SSG4 , this result suggests that somatic deletion of the region that includes SSG2 and SSG3 occurred in the chimeric plant, and raises the possibility that these genes are located at the S-locus, which determines the floral phenotype. Interestingly, the presence of SSG3 in the plant genome with short- but not long-styled flowers was not limited to the chimeric mutant, but was also observed in a normal plant of the Kitawase cultivar, as illustrated by Southern blot analysis , which consists of 661 amino acids . PCR analysis of 47 short-styled and long-styled pairs of buckwheat landraces and modern cultivars collected from Asia to Europe showed that there is a complete association between the presence of this gene and the type of flower exhibited; all plants with short-styled flowers but none with long-styled flowers possessed SSG3 (S-haplotype-specific and located at the S-locus (see S-LOCUS EARLY FLOWERING 3 (S-ELF3). Analysis of nucleotide sequence of 20 alleles of buckwheat S-ELF3 revealed 42 polymorphic sites among 4,087 nucleotide sites. Nonetheless, no apparent destructive mutations were observed for all 20 S-ELF3 alleles and all S haplotypes were presumed to contain a functional S-ELF3. Subsequent RT-PCR analysis revealed that S-ELF3 is expressed specifically in the pistils and stamens of short-styled flowers, but not in the vegetative tissues, such as the leaves, roots, and stems  genomic libraries and subsequent chromosome walking allowed us to construct an S-haplotype-specific contig . However, a homologous gene similar to the one identified above was present and expressed in the plants with long-styled flowers  that contained a large deletion. The plant that harbored the allele did not have any apparent defects and this cast doubt on the significance of SSG2 in heteromorphic SI. Furthermore, all pairs of primers tested failed to amplify SSG2 homologs from other Fagopyrum species and Southern hybridization analysis did not detect any homologs of this gene in other species, such as F. cymosum. Overall, the current results do not support a role for SSG2 in heteromorphic SI.Linkage analysis of 1,373 plants failed to identify any recombination events between  of SSG2 . These fFagopyrum species examined that exhibit heteromorphic SI, only short-styled plants possess S-ELF3 and the current evidences collectively suggested that S-ELF3 is located at the S-locus. Nucleotide sequence analysis of the protein-coding region of S-ELF3 of the three Fagopyrum species showed that the number of synonymous nucleotide differences per synonymous site is 0.27∼0.29 between the two anciently diverged groups   . Using 1 lineage . The lonS-ELF3 contains few functional protein-coding genes and that the functional involvement of genes other than S-ELF3 in heteromorphic SI remains highly speculative, S-ELF3 is the most promising candidate gene for controlling heteromorphic SI in buckwheat. Note that the presence of a functional S-ELF3 gene only in the S haplotype is in agreement with the dominance of the haplotype and the diallelism of heteromorphic SI, which can be attained simply by the absence/presence of functional alleles. Floral organ-specific expression of S-ELF3, as indicated by RT-PCR analysis  and its relatives are well-known for possessing identifiable sex chromosomes Koeniga and PersicariaPolygonaceae in which heteromorphic SI was observed Polygonaceae, dioecy and heterostyly were observed in plants of several different evolutionary lineages of Polygonoideae. In this regard, it is imperative to study the evolution of the heteromorphic SI system in relation to the evolution of dioecy and/or sex determination S-ELF3 in sorrel and other Polygonaceae species may therefore shed light on the evolutionary basis of dioecy and/or sex chromosome differentiation. Although the current analysis is the first step in answering questions that have been posed since the time of Darwin, further characterization of the biosystems will provide deep insight into the diversity of plant reproductive systems.It is remarkable that no recombination was observed between ch other , indicatFagopyrum esculentum of the BC1-F5 generation, direct descendants of a sib-mating line used to construct a BAC library F. esculentum cultivar Kitawase was a gift from the National Agricultural Research Center for Hokkaido region, Japan. The self-compatible line of F. esculentum Kyushu PL4, in which the hS allele was introduced from F. homotropicum, was kindly provided by Dr. Matsui of the National Agricultural Research Center for Kyushu Okinawa Region, Japan. Worldwide landraces of F. esculentum, F. cymosum, and F. urophyllum, as well as of F. tataricum, collected from 1983 to 1991 by O.O.  in a dose range of 75 to 100 Gy. The linear energy transfer (LET) of 20Ne10+ was 63.4 keV/µm. Flower morphology was observed for 1,400 M1 plants grown in a closed experimental room and a chimeric plant with both types of flower was detected.Seeds were irradiated with accelerated F. esculentum was separately prepared by the method described in in silico subtraction to select contigs that lacked a 32mer that was present in cDNA isolated from long styles. RT-PCR analysis was conducted using cDNA isolated from short and long styles  estimated by the modified Nei-Gojobori method for the genes of two Arabidopsis species (GENBANK GI numbers 30682945 and 297822050) that diverged about 5.4 MYA  was used to identify repetitive elements. BLAST programs Phylogenetic analysis of the ELF3 homolog and population genetic analysis of In the phylogenetic analysis of the conserved region (64 amino acids) of ELF3-related amino acid sequences, the maximum likelihood tree, which was based on the JTT amino acid substitution model The details of 5′ and 3′ RACE, RT-PCR, Southern hybridization, chromosome walking, and sequencing analyses are described in S-ELF3), AB641421-AB641423 (S-ELF3), AB642167 (ELF3), and AB668583-AB668598 (SSG2). The results of high throughput sequence analysis of stylar RNA and pyrosequencing of artificial chromosomes are deposited in the DDBJ Sequence Read Archive (DRA) database under accession number DRA000431.The DNA sequences have been deposited in the DDBJ/EMBL/GenBank DNA databases under accession numbers AB641416-AB641418  The deduced amino acid sequences of buckwheat S-ELF3 (SSG3). The conserved peptide motif is shown in bold, the residues that are polymorphic among the 20 alleles examined are shown in red italics, and the predicted monopartite nuclear localization signal, as determined by cNLS Mapper, is underlined. (B) The deduced amino acid sequence of buckwheat ELF3. The conserved peptide motif is shown in bold. (C) Phylogenetic tree of ELF3 homologs. The tree is drawn to scale, with branch lengths measured in the number of substitutions per site. The percentage of replicate trees in which the associated proteins clustered together in the bootstrap test (500 replicates) is shown next to the branches. GENBANK GI numbers of amino acid sequences are indicated in parentheses. The homologous sequences from spikemoss Selaginella moellendorffii were used as the outgroup.(TIF)Click here for additional data file.Figure S2A contig map of artificial chromosomes around S-ELF3 in buckwheat. The contig is of the S haplotype and contains several gene fragments, mostly pseudogenes(*). Detailed maps of the region surrounding three genes are shown below. Arrows indicate the direction of transcription. 1: homolog of Arabidopsis AT2G26520, 2: SSG2, 3: homolog of hypothetical protein RCOM_0938660*, 4: vacuolar H+-pyrophosphatase*, 5: intron of chloroplast trnA-UGC*, 6: cysteine desulfurase*, 7: integral membrane transporter family protein*, 8: S-ELF3, 9: homolog of Arabidopsis AT3G55760*, 10: flagellin-sensitive 2*, 11: embryo-defective 2734*.(TIF)Click here for additional data file.Figure S3RT-PCR analysis of the gene that is homologous to Arabidopsis AT2G26520. The actin gene was used as a positive control. LS, plant with long-styled flowers. SS, plant with short-styled flowers.(TIF)Click here for additional data file.Figure S4PCR survey of SSG2 in 47 buckwheat landraces and modern cultivars. The numbering corresponds to that shown in (TIF)Click here for additional data file.Figure S5Neighbor-Joining trees of 20 buckwheat alleles of (A) S-ELF3 and (B) SSG2. The trees were obtained using MEGA5 and drawn to scale, with branch lengths indicated below. The p-distance estimated from 4,087 and 755 nucleotide sites was used for S-ELF3 and SSG2, respectively, and the complete deletion option was applied. The sum of branch lengths was 0.010 for the S-ELF3 gene tree.(TIF)Click here for additional data file.Figure S6Breakdown of association between the presence of S-ELF3 and floral phenotype by recombination. Contiguous and dotted lines indicate the frequency of individuals with a positive marker at a locus neighboring S-locus in short (xS) and long-styled (xL) plants, respectively, under the assumption that the initial population (0th generation) contains only two types of individuals, i.e., long-styled plants, which are homozygotes of the S-ELF3− – s haplotype and short-styled plants, which are heterozygotes of the S-ELF3+ – S and S-ELF3− – s haplotypes, with equal frequency.(TIF)Click here for additional data file.Table S1Contigs obtained by in silico subtraction and pairs of primers used for RT-PCR.(DOC)Click here for additional data file.Table S2(A) Heteromorphic and self-incompatible plants used in the study. (B) Homomorphic and self-compatible plants used in the study.(DOC)Click here for additional data file.Table S3Primers used for PCR and sequence (seq) analyses.(DOC)Click here for additional data file.Table S4Primers used for chromosome walking.(DOC)Click here for additional data file.Text S1Supporting information on Results.(DOC)Click here for additional data file.Text S2Supporting information on Methods.(DOC)Click here for additional data file.


Salmo salar), these goals are currently hampered by the lack of a high-density SNP genotyping platform. Therefore, the aim of the study was to develop and test a dense Atlantic salmon SNP array.Dense single nucleotide polymorphism (SNP) genotyping arrays provide extensive information on polymorphic variation across the genome of species of interest. Such information can be used in studies of the genetic architecture of quantitative traits and to improve the accuracy of selection in breeding programs. In Atlantic salmon (SNP discovery was performed using extensive deep sequencing of Reduced Representation (RR-Seq), Restriction site-Associated DNA (RAD-Seq) and mRNA (RNA-Seq) libraries derived from farmed and wild Atlantic salmon samples (n = 283) resulting in the discovery of > 400 K putative SNPs. An Affymetrix Axiom® myDesign Custom Array was created and tested on samples of animals of wild and farmed origin (n = 96) revealing a total of 132,033 polymorphic SNPs with high call rate, good cluster separation on the array and stable Mendelian inheritance in our sample. At least 38% of these SNPs are from transcribed genomic regions and therefore more likely to include functional variants. Linkage analysis utilising the lack of male recombination in salmonids allowed the mapping of 40,214 SNPs distributed across all 29 pairs of chromosomes, highlighting the extensive genome-wide coverage of the SNPs. An identity-by-state clustering analysis revealed that the array can clearly distinguish between fish of different origins, within and between farmed and wild populations. Finally, Y-chromosome-specific probes included on the array provide an accurate molecular genetic test for sex.This manuscript describes the first high-density SNP genotyping array for Atlantic salmon. This array will be publicly available and is likely to be used as a platform for high-resolution genetics research into traits of evolutionary and economic importance in salmonids and in aquaculture breeding programs via genomic selection. Salmo salar) is a species of great economic, environmental and scientific importance, with a worldwide production of approximately 1.4 million tonnes per annum . Such miscalled heterozygotes can be identified using ‘OTV_Caller’ which is part of the SNPolisher (an R package available from Affymetrix). The Expectation-Maximization (EM) algorithm is used with the posterior information to identify which samples should be in the OTV cluster and which samples should remain in the AA, AB, or BB clusters. In this study, only SNPs from categories (i) and (iv) were included in further analyses. These filtered SNP data were analysed for allele frequency distribution and Mendelian inheritance using the software Plink .To map a subset of the QC-filtered SNPs to chromosomes, a sire-based linkage analysis was performed for a subset of offspring in the two ‘SalMap’ families   Two commercial organisations  were involved in the development of the Atlantic salmon SNP array and preparation of the manuscript. Among the authors, AT and AH work for Landcatch Natural Selection Ltd, AleD and FB work for Affymetrix Ltd and FB holds shares in Affymetrix Ltd. The array will be made available for purchase as a catalogue array from Affymetrix Ltd from March 2014. This project was funded by a Technology Strategy Board grant promoting industry-academia collaboration.Conceived, designed and managed the experiments: RDH, JBT, RT, SCB, ALA, JEB, DJP, FB, AET, KG, AH. Performed the laboratory experiments: JBT, NRL, AliD, DJP. Analyzed the data: RDH, TC, MB, AleD. Wrote the manuscript: RDH. All authors read and approved the final manuscript.Table S2. Details of the microsatellite markers used for the linkage analysis to anchor SNP markers to chromosomes.Summary of the number of SNPs discovered in single and multiple sequencing experiments for candidate SNPs included on the array and for final QC-filtered SNPs. Click here for fileDetails of the 96 samples used for testing the ‘ssalar01’ array. List of genotyped samples including the population, phenotypic sex (if known), sire (if genotyped), dam (if genotyped), and the SNP discovery experiment which the sample was also used (if applicable).Click here for fileList of the SNP markers mapped to chromosomes/linkage groups using sire-based linkage mapping.Click here for filePartial sequence of the Atlantic salmon sdY gene used for Y-specific probe design.Click here for fileSequence details for the Y-specific probes placed on the array to provide a molecular genetic test for sex.Click here for file


GWAS has facilitated greatly the discovery of risk SNPs associated with complex diseases. Traditional methods analyze SNP individually and are limited by low power and reproducibility since correction for multiple comparisons is necessary. Several methods have been proposed based on grouping SNPs into SNP sets using biological knowledge and/or genomic features. In this article, we compare the linear kernel machine based test (LKM) and principal components analysis based approach (PCA) using simulated datasets under the scenarios of 0 to 3 causal SNPs, as well as simple and complex linkage disequilibrium (LD) structures of the simulated regions. Our simulation study demonstrates that both LKM and PCA can control the type I error at the significance level of 0.05. If the causal SNP is in strong LD with the genotyped SNPs, both the PCA with a small number of principal components (PCs) and the LKM with kernel of linear or identical-by-state function are valid tests. However, if the LD structure is complex, such as several LD blocks in the SNP set, or when the causal SNP is not in the LD block in which most of the genotyped SNPs reside, more PCs should be included to capture the information of the causal SNP. Simulation studies also demonstrate the ability of LKM and PCA to combine information from multiple causal SNPs and to provide increased power over individual SNP analysis. We also apply LKM and PCA to analyze two SNP sets extracted from an actual GWAS dataset on non-small cell lung cancer. Rapid progress in high throughput genotyping technology has facilitated greatly the discovery of risk single-nucleotide polymorphisms (SNPs) associated with complex disease It is possible that joint tests of multiple SNPs in linkage disequilibrium (LD) are more powerful than testing each SNP individually. There are several reasons for the superiority. Firstly, the number of tests is reduced if SNPs are tested by group rather than individually. Secondly, as the true causal SNP may not be genotyped directly, combining the information from multiple genotyped SNPs in LD with the causal SNP may be more effective than testing each SNP individually p-value of a SNP set is the individual SNP analysis. This method uses the smallest p-value of all SNPs, corrected for the number of effective tests, as the p-value for the entire SNP set Several SNP set analysis methods have been proposed. The simplest way to determine the In this article, we compare the performance of LKM and PCA by using simulated datasets. The structure of this article is as follows. The procedures of LKM and PCA will be briefly described. Results of several simulation studies are provided to compare test power and type I error rate between the two methods. We then demonstrate the application of these two methods to two SNP sets extracted from a Lung Cancer GWAS dataset, and finally discuss the implications of our results.p SNPs from n individuals, let ith individual’s genotypes. The disease outcome is denoted by D.For a SNP set includes ith individual, LKM takes the form ofFor the α0 is the intercept, ix1, ix2,…, imx denote m covariates to be adjusted for and α1 …, mα are their coefficients. The association between the SNP set and the outcome is modeled by a function h(.), which is defined by γ1, γ2,…, nγ. K is a kernel function which measures the similarity between zi and zi’. Some choices for K include the linear, identical-by-state (IBS), weighted linear and weighted IBS In the above equation, h(.) could be regarded as a subject-specific random effect with a mean of zero and a variance of τK. Thus a score test on τ = 0 could be used to test the null hypothesis of no association.Liu et al. described the connection between LKM and generalized linear mixed model (GLMM) Vpp×. Let p p-dimension eigenvectors of Vpp×, and p corresponding eigenvalues, in which λ1>λ2>…> pλ. For the ith individual, the principal components areWe first standardize each of the genotype by re-scaling it to have a mean of 0 and a standard deviation of 1. The variance-covariance matrix of the standardized SNP set is denoted as el is selected to maximize the variance of PCl with the constraint that l and PCl’ is zero for l≠l’. lλ measures the variation explained by PCl. As SNPs in the set are always highly correlated with each other, the first few eigenvalues will be much greater than the others. This makes it possible to use the first few PCs to capture most of the variation in the SNP set. To do this, we only need to select the first k PCs with cumulative contribution p SNPs, we will use the first k PCs in the multiple logistic model by:By definition, k-df likelihood ratio test can be used to test the significance of the SNP set. For simplicity, we will use PCA(Z%) to denote the PCA with the PCs explaining at least Z% of the total variation. As example, if the top 2, 3 and 4 PCs explain 63%, 75% and 80% of the total variation, the corresponding ks for PCA(60%), PCA(70%) and PCA(80%) are 2, 3 and 4, respectively.A We use simulated datasets to evaluate the performances of LKM and PCA. Measurements for comparison include empirical type I error rate and test power. All the causal SNPs are assumed to improve the risk. We assume the disease model isC is the number of causal SNPs, jβ is the log genetic relative risk (RR) of the jth causal SNP. We use a log-additive inheritance model in our simulation by imposing the assumption that risk multiplies with each additional allele. We let C = 0 to 3 in our simulations, denoting null, single causal SNP, two or three causal SNPs model.Here, CLPTM1L gene, a 27.35kb-long-gene located at 5p13.33. It encodes cleft lip and palate trans-membrane protein 1-like protein. Two SNPs in this gene, rs31489 and rs401681, were reported to be associated with non-small cell lung cancer (NSCLC) CLPTM1L gene, are used as the template of the simulated sequence. We use HAPGEN (version 2) to generate the simulated datasets Datasets are simulated on the basis of the CLPTM1L gene (scenarios A1–A11). Parameters of the simulations are described by C = 0), in which the outcome is independent of the loci. The empirical type I error rate is calculated as the proportion of rejecting the null hypothesis in the 5,000 simulated datasets. Scenarios A2 and A3 are simulated to compare the powers of LKM and PCA when there is only one causal SNP in the region. We set the genetic RR as 1.1 and 1.2 at scenarios A2 and A3, respectively. In both scenarios, each of the 31 SNPs in the CLPTM1L gene is set to be the causal SNP in turn. For each causal SNP, 1,000 datasets are simulated. The test power is calculated as the proportion of p-values less than 0.05. To make the simulations more realistic, only 8 of the 31 SNPs are used by LKM and PCA, which are directly genotyped by the Illumina 610k Quad chip.We conduct 11 scenarios of simulations based on the R2 between the causal SNP and the genotyped SNPs. Details of these scenarios are presented in the first 6 columns of We also examine the ability of these methods on utilizing information from multiple loci assuming that there are 2 or 3 causal SNPs with RR = 1.1 (scenarios A4 to A11). Both of the two causal SNPs are genotyped in scenario A4. In scenarios A5–A8, only one of the two causal SNPs is genotyped. No causal SNPs are genotyped in scenarios A9 and A10. Besides the number of genotyped causal SNPs, the difference among scenarios A4–A10 is reflected by the different median ASAH1, acid ceramidase 1, is a 28.5-kb-long gene located at 8p22. It was reported to be associated with prostate cancer and Farber disease CLPTM1L gene. Four scenarios are simulated, in which scenario B1 evaluates the type I error and B2 to B4 evaluate the test power. In scenario B1, 5,000 datasets are simulated with no association between the disease outcome and the SNPs. In scenario B2, each of the 154 SNPs is set to be the causal SNP with a RR of 1.2 in turn. There are 1,000 simulated replicates for each of the causal SNP. Again, although the simulated datasets are generated using the overall 154 SNPs, only 39 genotyped SNPs are used in the analyses. Scenarios B3 and B4 both assume 2 causal SNPs with RR = 1.1. Both of the two causal SNPs are in strong LD with the genotyped loci in B3, while one causal SNP is in weak LD with the genotyped loci in B4. The causal SNPs in scenarios B3 and B4 are all common SNPs (MAF≈40%). The aim of these two scenarios is to evaluate the performance of LKM and PCA when causal SNPs are in the same or different LD blocks in a relatively large SNP set.CLPTM1L gene. The second one is an 116kb region, which includes 21 SNPs in 15q24-25. Genes in this region include the CHRNA3, CHRNA5 and CHRNB4. These 3 genes were reported to be associated with nicotine addiction, smoking behavior and NSCLC We apply the two methods to test the significance of 2 regions extracted from a GWAS dataset studying the genetic susceptibility of non-small cell lung cancer (NSCLC). Details of participant recruitment for the study have been described previously SKAT package (version 0.73) is used to conduct LKM analysis. For the weighted kernel functions, we use the default weight of MAF is the SNP’s minor allele frequency, a1 = 1 and a2 = 25, Beta is the density function of a β distribution. We also perform individual SNP analysis for each SNP set. To determine the p-value of a SNP set, a logistic regression model is fitted for each locus. The p-value of the set is then determined by the minimum p-value of all the loci, corrected by the effective number of tests R package (version 2.13) is used to analyze both of the simulated and actual datasets. The CLPTM1L gene, the mean numbers of PCs to explain at least 80%, 60%, 40% and 20% of the total variation are 3.00, 2.00, 1.00 and 1.00, respectively. For the ASAH1 gene, the corresponding mean numbers of PCs are 6.85, 3.06, 1.92 and 1.00, respectively. The result indicates that type I error rate is independent of the number of PCs included in the model. The individual SNP analysis is conservative in both scenarios. The empirical type I error rate of LKM, PCA and individual SNP analysis are presented by CLPTM1L gene is a small gene with a simple LD structure, only a very small number of PCs are needed to explain a large proportion (>40%) of the total variation. Thus the results from PCA(40%) and PCA(20%) are exactly the same. PCA(40%) is more powerful than the other PCAs when the causal SNP is at one of the 5th, 6th, 10th–13th and 15th–28th loci, as shown in st–4th and 29th–31st loci, where PCA(80%) is dominant than LKM and the other PCAs. It is worth noting that although LKMs with linear or IBS weighted kernel do not have good performance in general, they are more powerful than the others when the MAF of the causal SNP is low  is more powerful than LKM and the other PCAs since more PCs are needed to “capture” the information that differentiates cases and controls. This is true except when the causal SNPs have low MAFs. It is not surprising that PCA(40%) and LKM with linear kernel have better performance than the others, shown at the right part of these 2 plots.The top 2 panels in nd, 29th or 30th locus with a RR of 1.2, the individual SNP analysis has greater power than the others. We believe this is not surprising because the inclusion of un-associated SNPs may “dilute” the effect of the causal.When compared to the individual SNP analysis, PCA and LKM are more powerful in most situations. However, when the causal SNP is in the 2ASAH1 gene is more complex than that of the CLPTM1L gene, we also show the results from PCA(20%). The LD plot at the bottom of st to the 119th and the 120th to the 154th). In the first block, the power from LKM with linear kernel and PCA(20%) is dominant than the others. Whereas in the second block, it is interesting to find that the power from PCA(60%) ≈ PCA(40%)>PCA(80%)>PCA(20%). PCA(60%) and PCA(40%) are also more powerful than LKMs. The influence of MAF, LD structure and number of PCs in the model on test power is also demonstrated by the bottom panel of Results of scenario B2 are shown by ASAH1 gene from scenarios B3 and B4 generate similar results . The p-value of the LKM with linear kernel is 3.16E-6, the least of all LKMs. The least p-value of PCA (6.83E-6) happens when the PCs in the model explain 20% or 40% of the total variation. For SNP set 2, PCA (60%) is dominant than the other methods. LKMs with weighted kernels are less powerful than the other methods due to the fact that the possible associated SNPs are common ones .The results of the analysis are shown in st PC from SNP set 1 and the 2nd PC from SNP set 2, which are significant, are then presented by st PC has heavy loadings on rs31489  and rs401681 .To understand how PCA utilizes the information from multiple SNPs, we also examine the coefficient of each SNP in the top PCs. For each SNP set, we regress the disease outcome on the top PCs. The 1In this article, we compare the performance of logistic kernel machine based test and principal component analysis based test for the analysis of GWAS. Both of these two methods have the ability to test the association between a continuous or discrete outcome and a set of SNPs grouped by biological knowledge or genomic characteristics. We conduct extensive simulation studies using datasets generated based on the haplotypes downloaded from the International HapMap Project. We also apply these two methods to two SNP sets extracted from a GWAS data on NSCLC. The results demonstrate that both methods can correctly control the type I error. If the causal SNP(s) is/are in strong LD with the genotyped SNPs, both methods are superior to individual SNP analysis due to the ability of borrowing information from the genotyped SNPs in LD with the causal SNP(s). Furthermore, if there are two or more causal SNPs in a SNP set, LKM and PCA can combine their information and provide higher test power than individual SNP analysis.th, 8th and 14th SNPs).Thus LKM with weighted kernel may be a good choice when there is evidence that the MAF of the causal SNP is low. Meanwhile, when analyzing the same SNP set, if the weighted LKM generates a positive result while the un-weighted LKM does not, it is possible that the causal SNP in the region may have low MAF. Recent studies also indicate LKMs with weighted kernels has good performance when the disease is associated with rare variations Linear kernel performs well in most occasions in our simulation studies. Although slightly inferior to linear kernel, IBS kernel is claimed to have better performance when there is epistatic effect in the SNP set CLPTM1L gene, if the causal SNP resides in the region in high LD with most of the genotyped SNPs, only the 1st PC is needed to capture the information of the causal SNP. Including more PCs will decrease the power. While in the simulation based on the ASAH1 gene, more PCs should be included in the model to increase the power if the causal SNP resides in the right side of the SNP set  if the LD structure of the SNP set is complex. A possible way to improve the power of PCA is to exclude the PCs independent of the causal SNPs. Although we can perform a feature selection procedure on PCs, such as only including significant PCs in the model, we risk increasing the type I error rate as the feature-selection procedure is ignored by the likelihood ratio test. A permutation procedure could be used to build the sampling distribution of the test statistic for the PCs after the feature-selection. However, this procedure is not efficient as one may need thousands of permutation samples to get a p-value small enough. More theoretical research should be taken for a solution to build a corresponding asymptotic distribution to improve the efficiency.Thus it is critical to examine the LD structure of the SNP set before performing a LKM or PCA analysis. We suggest the SNP set to include SNPs in a LD block. In this situation, LKM and PCA with only a few top PCs may have good performance. However, if the SNP set covers a wide region containing several LD blocks, we suggest using PCA with PCs explaining a large proportion of variation to capture the information of the causal SNP(s). Chen et al. proposed a supervised PCA procedure for pathway-based analysis, in which only SNPs mostly associated with the disease outcome are used to extract PCs Both of LKM and PCA are capable of handling epistasis. PCA can also easily handle gene-environmental interactions, while it is still an issue for LKM. A possible limitation of PCA is the difficulty in interpretation of the results. However, just like LKM, a significant SNP set in PCA can be followed by a fine mapping or deep sequencing to identify the true causal SNP as the causal one should reside in the region in or close to the significant SNP set. Meanwhile, by checking the loading of the significant PCs on each SNP, we can identify which SNPs are more associated with the disease. By combining LKM or PCA with a moving window strategy, it is possible that we can have more precise information on where the causal SNP resides.We acknowledge that our study has several limitations. Firstly, only LKM and PCA are evaluated, although there are several other SNP set based methods. In the LKM’s original paper Figure S1The relationship between the test power and the medianR2 between the causal and the genotyped SNPs in scenario A11. The x-axis in the top plot denotes the 84 three-SNP combinations, ordered in ascending by the median R2 between the corresponding causal and the genotyped SNPs. The y-axis denotes the test power under each three-SNP combination. The bar plot in the bottom represents the median MAF of each combination.(DOCX)Click here for additional data file.Table S1Standard error of the empirical type I error rate for LKM and PCA.(DOCX)Click here for additional data file.Table S2Standard error of the empirical power for LKM and PCA in scenarios A4–A10.(DOCX)Click here for additional data file.Table S3Standard error of the empirical power for LKM and PCA in scenarios B3–B4.(DOCX)Click here for additional data file.


Plant cell wall polysaccharide composition varies substantially between species, organs and genotypes. Knowledge of the structure and composition of these polysaccharides, accompanied by a suite of well characterised glycosyl hydrolases will be important for the success of lignocellulosic biofuels. Current methods used to characterise enzymatically released plant oligosaccharides are relatively slow.Arabidopsis thaliana mutants affected in xylan synthesis. The product profiles of specific xylanases were also compared in order to identify enzymes with unusual oligosaccharide products.A method and software was developed allowing the use of a DNA sequencer to profile oligosaccharides derived from plant cell wall polysaccharides . An ABI 3730xl, which can analyse 96 samples simultaneously by capillary electrophoresis, was used to separate fluorophore derivatised reducing mono- and oligo-saccharides from plant cell walls. Using electrophoresis mobility markers, oligosaccharide mobilities were standardised between experiments to enable reproducible oligosaccharide identification. These mobility markers can be flexibly designed to span the mobilities of oligosaccharides under investigation, and they have a fluorescence emission that is distinct from that of the saccharide labelling. Methods for relative and absolute quantitation of oligosaccharides are described. Analysis of a large number of samples is facilitated by the DASHboard software which was developed in parallel. Use of this method was exemplified by comparing xylan structure and content in The DASH method and DASHboard software can be used to carry out large-scale analyses of the compositional variation of plant cell walls and biomass, to compare plants with mutations in plant cell wall synthesis pathways, and to characterise novel carbohydrate active enzymes. The plant cell wall is a complex structure, consisting of polysaccharides, lignin and protein, and forms the majority of biomass on the planet. Enzymatic saccharification of cell wall polysaccharides (lignocellulosic biomass) is likely to be an excellent source of sustainable energy, particularly when fermented to produce liquid biofuels . A majorThe major polysaccharide of the cell wall is the semi-crystalline glucan, cellulose. In addition, there are hemicelluloses (such as xylan and xyloglucan) and pectins  which coat the cellulose fibrils. There is large variability in the structure of these polymers and in the composition of biomass from different plant organs, genotypes, species and between crops grown at different times , and theCurrently, many GH enzymes are assessed for their saccharification efficiency by colourimetric assays that quantify total sugars released, such as the dinitrosalicylic acid (DNS) assay, or 3-methyl-2benzothiazolinonehydrazone (MBTH) . These mAnalysis of cell wall polysaccharides is also a challenging task due to their heterogeneity in monosaccharide composition, linkage and glycoside sequence. A variety of techniques have been applied to the problem. For example, Fourier-Transform Infrared analysis, FT-IR, to image intact cell walls, can identify broad architectural alterations in cell walls . HoweverAnother approach is to use specific GHs to digest the plant polysaccharide of interest to produce oligosaccharides which retain enough structural linkage information to determine the polysaccharide fine structure. For example, high performance anion exchange chromatography with pulsed amperometric detection (HPAEC-PAD) has been applied successfully to separate oligosaccharides . HPAEC-PSome of these disadvantages were overcome by the development of the PACE  method, in which the reducing end of plant polysaccharide-derived oligosaccharides (released by hydrolases or other carbohydrate active enzymes) are labelled with a fluorophore by reductive amination, and then separated by polyacrylamide gel electrophoresis . The metAn additional electrophoretic analytical technique uses capillary electrophoresis with laser-induced fluorescence (CE-LIF). CE-LIF is a powerful technique offering high detection sensitivity and good resolving capacity for the analysis of 9-aminopyrene-1, 4, 6-trisulfonate (APTS) labelled carbohydrates -15. The Transfer of CE-LIF to a DNA sequencer provides several possible advantages. First, samples can be analysed in parallel, and some models allow concurrent CE of 96 samples. Second, the instruments can be adjusted to detect up to 5 specific fluorescent emission wavelengths potentially allowing spectral separation of mobility markers, standards and sample oligosaccharides. Most studies have investigated application to glycans larger than five or more residues and have incorporated glycan clean-up steps of HILIC or size exclusion chromatography to remove noise from unincorporated APTS -23. HoweArabidopsis xylan synthesis mutants was analysed. Additionally, DASH was used to investigate the substrate specificity of GHs, and to classify them by their product profiles.Here, we present a high throughput method for quantitative analysis of plant cell wall polysaccharides using a 96 capillary array DNA sequencer (ABI 3730xl). We call this method DNA sequencer-Assisted Saccharide analysis in High throughput (DASH). In order to demonstrate the use of DASH for comparison of polysaccharide structures in plant biomass samples, the xylan structure of We first investigated the capability of CE-LIF in the ABI 3730xl DNA sequencer to resolve oligosaccharides prepared from plant cell wall polysaccharides. β-1,4 xylan oligosaccharides with degree of polymerization (DP) 1 to 6 were mixed with a dextran  ladder, and labelled with a single APTS at their reducing end. After dilution of samples to ~1 pmol in a 96-well microtitre plate, the samples were electrophoresed in parallel. The injection and running conditions were as described in Table n-1/Pn) were determined with DASHboard software (see methods). The variation in these ratios between 12 samples run in parallel was determined, and the coefficient of variation (CV) was calculated for oligosaccharides with a range of abundances  above which the response is saturated (data not shown). To determine the lower fluorescence unit limit for reproducible quantitation of oligosaccharides, a dextran ladder comprising a range of different quantities was analysed. To obtain comparable values between different CE traces, the peak area ratio and the peak height ratio between all adjacent DP peaks . For greater oligosaccharide quantities, it is likely that saturation occurred due to the decrease in available APTS. Therefore, in the labelling protocol the quantity of all reducing sugars must be less than 200 nmol. Unknown amounts of reducing sugars can be determined relative to quantitation standards (QS) labelled together with these sugars. However, even in the presence of excess APTS, we found that labelled xylo-oligosaccharides gave more fluorescence than corresponding quantities of gluco-oligosaccharides , variation is also observed in the difference between elution times of pairs of analytes (for example through compression of regions of traces). In order to overcome these problems mobility markers (MMs) can be introduced to normalize the electropherogram traces. APTS-tagged glycan mobility markers that bracket the region of interest have been explored previously by various groups, but the traces are not aligned with sufficient accuracy for confident assignment of all analytes ,29. TracTo investigate the accuracy of trace alignment, eight MMs labelled with DY-481XL were selected to span dextran oligosaccharides DP1 to DP6. These were added to each APTS labelled glycan sample prior to electrophoresis. The MMs and APTS-labelled sugars were monitored in two different channels, shown with red and black colours respectively in Figure Arabidopsis xylan synthesis mutants GlcA(Xyl)3). This is noteworthy as the two structures only differ in the presence of a 4-O-methyl ether leading to a size difference of only 14 Da. The CjXyn10A fingerprint of the irx mutants show only three major products: Xyl, (Xyl)2 and MeGlcA(Xyl)3. Unmethylated GlcA is absent, which is consistent with previous data from MS GlcA are well-characterised products of glucuronoxylan digestion with xylanases in CAZy families GH10 and GH11.Click here for fileStructure of DY-481XL fluorophore label of MMs.Click here for file