Trypanosoma cruzi, the causal agent of Chagas Disease, affects more than 16 million people in Latin America. The clinical outcome of the disease results from a complex interplay between environmental factors and the genetic background of both the human host and the parasite. However, knowledge of the genetic diversity of the parasite, is currently limited to a number of highly studied loci. The availability of a number of genomes from different evolutionary lineages of T. cruzi provides an unprecedented opportunity to look at the genetic diversity of the parasite at a genomic scale.T. cruzi sequence data available in the public domain and obtained multiple sequence alignments in which one or two alleles from the reference CL-Brener were included. These data covers 4 major evolutionary lineages (DTUs): TcI, TcII, TcIII, and the hybrid TcVI. Using these set of alignments we have identified 288,957 high quality single nucleotide polymorphisms and 1,480 indels. In a reduced re-sequencing study we were able to validate ~ 97% of high-quality SNPs identified in 47 loci. Analysis of how these changes affect encoded protein products showed a 0.77 ratio of synonymous to non-synonymous changes in the T. cruzi genome. We observed 113 changes that introduce or remove a stop codon, some causing significant functional changes, and a number of tri-allelic and tetra-allelic SNPs that could be exploited in strain typing assays. Based on an analysis of the observed nucleotide diversity we show that the T. cruzi genome contains a core set of genes that are under apparent purifying selection. Interestingly, orthologs of known druggable targets show statistically significant lower nucleotide diversity values.Using a bioinformatic strategy, we have clustered T. cruzi at a genomic scale. The analysis covers an estimated ~ 60% of the genetic diversity present in the population, providing an essential resource for future studies on the development of new drugs and diagnostics, for Chagas Disease. These data is available through the TcSNP database (http://snps.tcruzi.org).This study provides the first look at the genetic diversity of  Trypanosoma cruzi is a protozoan parasite of the order Kinetoplastida, and the causative agent of Chagas Disease, one of the so called neglected diseases that disproportionately affect the poor. The disease is endemic in most Latin American countries, affecting in excess of 8 million people[T. cruzi[n people. Chagas [T. cruzi-5.T. cruzi species has a structured population, with a predominantly clonal mode of reproduction[T. cruzi, CL Brener) have a very high degree of heterozygosity but otherwise very homogeneous population structures with low intralineage diversity[The oduction, and a coduction-10. Throoduction-14, whiloduction-17 origioduction. Recentloduction,19. Lineiversity,21. The iversity.Knowledge of the genetic variation present in a genome  or in a species (i.e. in the population) is of central importance for a variety of reasons and applications: i) to understand the evolutionary forces underlying the biological and phenotypic properties observed in an individual; ii) to detect cases of apparent horizontal gene transfer; iii) to assess the potential for development of resistance when validating a target for drug development; iv) to prioritize targets for development of diagnostics or vaccines; v) in the design of constructs for genetic knockout experiments in order to increase the success rate when targeting specific alleles; and vi) as genetic markers in association studies or to further probe the population structure.T. cruzi was published in 2005[Trypanosoma brucei [Leishmania major (Leishmaniasis)[T. cruzi was a particular case for a number of reasons: it was obtained from a hybrid TcVI strain composed of two divergent parental haplotypes; and it was sequenced using a whole genome shotgun strategy[The genome sequence of the CL-Brener clone of  in 2005, togetheomiasis) and Leisaniasis). Howeverstrategy. This chstrategy. These dstrategy. As a coT. cruzi: the draft genomic sequence of the Sylvio X10 (TcI) strain[More recently a number of whole genome sequencing data have become available from different strains of ) strain, high-coTo take advantage of the hybrid genome of the CL-Brener strain, and of other genome and transcriptome datasets, we designed a bionformatics strategy to obtain information on the genetic diversity present in these data. As already observed for a significant number of molecular markers, each of the alleles identified in the majority of the polymorphic heterozygous site in strains from hybrid lineages TcV and TCVI can be observed in homozygosity in strains from either of the two proposed parental lineages (TcII and TCIII),21,29-31T. cruzi, and its functional analysis, focussed mostly on protein-coding regions of the genome.In this work we present an initial compilation of a genome-wide map of genetic diversity in T. cruzi we took advantage of available sequence data in public databanks, including the genome sequence of the CL-Brener and Sylvio X10 strains, expressed sequence-tags and other sequences submitted by independent authors to these databanks. Our strategy to map this diversity relied on the generation of multiple sequence alignments and on the scanning of these alignments to identify polymorphisms[T. cruzi genome was obtained using a whole genome shotgun strategy, from a hybrid clone (CL-Brener)[T. cruzi genome[loci. These were defined as those sequences represented by no more than 2 coding sequences from the CL-Brener (reference) genome in our sequence alignments (see below).To identify genetic variation in orphisms. As ment-Brener). Because-Brener),27. Thisi genome,33, we dloci; see Table T. cruzi, and were not considered further. Even after this stringent filtering, there were still a number of alignments that contained only two reference sequences from the CL-Brener genome, but that belonged to these large gene families – mucins, mucin-associated proteins (MASP), trans-sialidase-like proteins, etc. These correspond to cases where highly similar copies of members of a family were separated from their paralogs during the clustering or assembly steps. Finally, a number of alignments (~6%) had only one reference sequence from the CL-Brener hybrid. These cases may correspond to haploid regions in the hybrid genome or to cases where two highly divergent alleles were separated during the clustering step.Sequences used in this work include all the annotated coding sequences from the reference CL-Brener genome, and the corresponding coding sequences (CDS) from the Sylvio X10 genome, as well as other publicly available sequence data (see Table T. cruzi evolutionary lineages TcI (Sylvio X10), TcVI (CL-Brener), but also in lineages TcII and TcIII (represented by the variation found within the CL-Brener hybrid).We then scanned the multiple sequence alignments and identified columns containing sequence differences and/or indels. From the set of all alignments we identified 325,355 sites with variation , of which 28,316 corresponded to small indels  or to sequencing errors. To discriminate between these possibilities, we also analyzed the sequence neighborhood around each potential SNP. Based on this analysis we found 302,390 SNPs  located in regions with a low density of SNPs . To further assess the quality of the sequence around/in each SNP we used a statistical software package (PolyBayes) togetheThe great majority (99%) of the observed SNPs were bi-allelic (changing one specific nucleotide base for another), however there were 2,990 tri-allelic SNPs (0.98%) and 10 tetra-allelic SNPs . These are very interesting SNPs that can be exploited in the design of strain typing assays. One such assay, based on one tetra-allelic and a number of tri-allelic SNPs has just been developed using this information.All this information is available in the Additional filein silico, and to assess the quality of the SNPs and the probability of them being true SNPs (as opposed to sequencing errors) we performed a small scale re-sequencing study on 47 loci . The majority of these new CL-Brener SNPs escaped the initial in silico prediction because of artifacts in the assembly of the T. cruzi genome, which resulted, for example, in a missing allele for an hypothetical protein (TcCLB.511003.60) with high similarity to the yeast ERG10 gene (Acetyl-CoA C-acetyltransferase). In the T. cruzi genome database there is only one allele reported for this gene. As a consequence, the few polymorphisms identified by our computational strategy were derived from the comparison of this allele against a short CL-Brener EST sequence . However upon PCR amplification from CL-Brener DNA, we were able to uncover additional heterozygous polymorphisms . Both pieces of evidence suggest that there is a second allele that was probably merged or missed during genome assembly. Apart from this case, this small scale re-sequencing experiment confirmed the majority of the SNPs identified in silico, which is in agreement with the expected sequence coverage/quality of genomic and transcriptomic data used. A complete table listing all loci analyzed, and their SNPs is available in Additional fileTo validate the strategy used for this). This rdatabase) and 1,2gene and alignment interchangeably to refer to the genomic loci represented by these sequences.Based on the results from this re-sequencing experiment we decided to focus our analysis of genetic diversity on the subset of high-quality SNPs (P > 0.7) that are also located in regions of good sequence neighborhood. This subset was therefore used throughout the study. Because the candidate allelic copies of each reference coding sequence are now aligned in our dataset, we use the words T. cruzi was not exception. As observed previously for rRNA genes[In the subset of high-quality SNPs, we first looked at the types of changes observed at the DNA level: transitions and transversions. Theoretically, there are twice the number of possible transversions than transitions. However, because of the nature of the molecular mechanisms involved in the generation of these mutations transitions are found more frequently than transversions,37. And NA genes we obserWhen analyzing the subset of high-quality SNPs at the codon level, SNPs were more frequently observed at the 3rd codon position (53%), followed by the 1st codon position (25%) and the 2nd (21%).Using the set of high quality SNPs  we observed 76,452 silent (synonymous) SNPs, 99,552 non-synonymous SNPs and 161 non-sense SNPs – those introducing or removing stop codons in proteins (see Table loci) with clear nonsense polymorphisms, many of which correspond to hypothetical proteins , we observed a significantly higher number of nonsense SNPs arising from mutation of the 1st base of a codon  or as generating a read-through codon (therefore producing a longer product). The comparison of nonsense mutations in the available data suggest that in 3 cases  the ancestral state of the codon was most probably a STOP (readily identified in 5 out of 6 strains) that was changed into a read-through codon in one strain/lineage only. In other cases the situation might be similar , although the corresponding CDS was missing from one of the strains. In contrast, in 44 cases (39%) the nonsense mutation was only observed once, and can therefore correspond to the alternative case, in which the ancestral codon was replaced by a premature stop, therefore generating a truncated protein product.Analysis of the these cases, revealed that the majority of them (77%) contained the nonsense SNP in the final 10% of the corresponding coding sequence, near the 3’ end of the other allele (see Table S3), and therefore may not be associated with large functional changes. However, in a few cases the identified nonsense SNPs are producing predicted disruptive changes.In the case of a putative GDP-mannose 4,6 dehydratase , the nonsense SNP, present only in strains from the TcI lineage, is located near the N-terminus of the protein, therefore theoretically resulting in a complete truncation. Although there is a downstream ATG that could be used to produce a product with only a 11% reduction of its size, this product would lack the conserved NAD(P) nucleotide binding motif GGxGxxG, and thetrans-membrane domains, signal peptides, globular vs unstructured regions. We reasoned that the selection acting on the gene might be different in these different regions or domains. Based on this idea, we performed a number of comparisons, evaluating differences in the density of synonymous and non-synonymous changes in one of these domains vs the rest of the protein. However, although some significant signal can be observed when performing pairwise comparisons , these differences are not significant when using the complete data that includes alleles from TcI, TcII , TcIII , and TcVI (CL-Brener).Next, we analyzed the distribution of SNPs along the coding region, and in the context of different sequence features: T. cruzi proteins (globular regions ranged from 10 to 90% of the protein). A comparison of the SNP density found in these regions showed no statistically significant differences .One of the features analyzed, was the presence of SNPs in natively unstructured domains. Several recent papers report an observation that natively unfolded domains can support higher non-synonymous substitution rates,42. BaseT. cruzi , which was first described in Leishmania[Another interesting gene showing a striking accumulation of non-synonymous changes in a natively unstructured domain is the A2Rel-like protein of eishmania. In thisBecause SNPs identified in this work represent variation observed within a species, we decided to use the nucleotide diversity indicator π as an estimate of selection,46. In oloci corresponding to singleton sequences , the remaining loci with nil values of π were those for which we could not identify high-quality SNPs . As seen in Figure π values were those of ribosomal proteins, histones and cytochromes among others.Not taking into account T. cruzi genome using the Molecular Function ontology (from the GO Gene Ontology); and the functional mapping of T. cruzi enzymes in metabolic pathways according to the KEGG Metabolic Pathways database  containing GO annotation into 27 broad classes as defined by their parent GO terms from the Molecular Function ontology. There were significant differences in the π values when comparing all classes using the non-parametric Kruskal-Wallis test  and retrotransposons. In all cases, we observed a significant dispersion of the π indicator for any one class. Secondly, we performed a similar analysis on genes with assigned EC numbers  that were mapped onto KEGG pathways. In this case, genes that participate in transcription and protein degradation (proteasome) showed less nucleotide diversity, similar to what we observed for GOSlim classes; whereas genes involved in glycan synthesis and degradation, metabolism of co-factors and vitamins, and xenobiotic metabolism showed a higher nucleotide diversity  is a nice example of this observation. Interestingly, current validated targets (e.g. the lanosterol demethylase and the C-24 sterol reductase) display low numbers of non-synonymous changes . However, at the same time, other enzymes of the pathway like the C-5 sterol desaturase  apparently not required by the intracellular amastigotes[The observed dispersion of the apparent selection pressure acting on a given metabolic pathway is not surprising, as the importance of different steps in the pathway  is not homogeneous. In the case of stigotes is accumT. cruzi druggability predictions allowed the identification of 173 loci with a druggability index > 0.6.Attractive targets for drug development have to meet a number of requirements. The most important of these is the essentiality of the target for survival of the parasite within the host. However, a number of other criteria are often used to prioritize drug targets, druggabT. cruzi genome we analyzed the apparent selection pressure in predicted druggable targets (druggability index >= 0.6) vs the rest of the genome – genes encoding products that are either not druggable (druggability index < 0.6) or for which there are currently no information about their druggability . For this analysis we used the nucleotide diversity indicator π, or the dN/dS indicator (not shown). We then analyzed the distribution of π in these two groups of genes . This may not be surprising, as many druggable targets are highly conserved genes with housekeeping cellular functions[In the context of the selection of drug targets for drug discovery, the evolutionary forces acting on a gene may be used as a surrogate marker for essentiality or to assess the risk of development of drug resistance. Taking advantage of the genetic variation identified within the unctions. Howeveret al.[T. cruzi targets (see strategy in Methods), using a genetic diversity score based on a normalized inversion of the nucleotide diversity value. With this scoring system, genes with a low π value will attain a higher score, and vice versa (see Methods). The result of this prioritization is available from Additional fileT. cruzi. The top ranked target in this list is a dihydrolipoamide dehydrogenase (EC 1.8.1.4), an enzyme usually involved in both energy generation and defense mechanisms against oxidative and nitrosative attacks. Selective inhibitors for this enzyme have been identified in T. cruzi[Mycobacterium tuberculosis[Following this reasoning, we suggest that it may be helpful to include genetic diversity information in target prioritization strategies, such as those discussed by Crowther et al.. We ther T. cruzi and Mycoerculosis. The allerculosis, a uniquerculosis, an inorT. cruzi genome. The scoring strategy used to evaluate putative SNPs in the absence of raw chromatogram data proved to work reasonably well, as we were able to confirm >= 96% of the SNPs having a probability value >= 0.7 by re-sequencing. The genome-wide data shows that the T. cruzi genome is highly polymorphic, with almost 5 polymorphic sites every 200 bp (2.4%). This figure is similar, though not identical, to that reported by El-Sayed et al. for the CL-Brener whole genome (2%)[In this study we identified hundreds of thousands of candidate SNPs located primarily in single-copy coding regions of the ome (2%). HoweverT. cruzi genome is highly repetitive[T. cruzi genome was no exception[loci . From a total of 47 loci that were selected for re-sequencing, one was discarded due to the presence of amplification products of different sizes, another was the result of an amplification of a tandem array containing two copies of the targeted gene , while in the third case the excess of heterozygous sites revealed the missing allele for a single-copy gene . Except for these cases, all other targeted loci showed the expected heterozygosity based on the in silico comparison of the two CL-Brener haplotypes. Notwithstanding this, the overall analysis presented in this paper is clearly focused on a core of genes that are single copy. Moreover, because of the way in which the bioinformatic strategy was devised, we are probably underestimating the number of highly polymorphic genes. Theoretically, single copy genes with highly divergent alleles could end up in separate clusters, and therefore in different alignments (or as singleton sequences), due to their high number of nucleotide differences. Our bioinformatics strategy, using a second BLAST stage to detect these cases worked well overall, but a number of such cases did escape the initial automated analysis, and have been corrected by manual merging of alignments. Therefore, we cannot rule out the possibility of missing additional cases.The petitive,33 with petitive, and thexception. This coxception. Howeverfixed (i.e. they are substitutions or mutations). In our particular case, we are analyzing changes within a species. Therefore we have chosen to call all observed nucleotide changes as polymorphisms or SNPs. These distinctions affect the choice of different estimates of selection, such as dN (the number of non-synonymous changes per non-synonymous site) and dS (the number of synonymous changes per synonymous site), or the nucleotide diversity (π)[loci, show that a significant percentage of the identified changes can be verified by re-sequencing a different strain from the same evolutionary lineage. This percentage can be as high as ~ 96% for changes identified between the CL-Brener haplotypes , strongly suggesting that the great majority of the polymorphisms identified in this study are actually fixed differences. This is also in agreement with recent comparisons of two T. brucei genomes[π indicator, do not change subtantially if we use the dN/dS ratio instead (data not shown). When comparing nucleotide changes between different species, it is mostly correct to assume that these differences are sity (π). In thissity (π), and our genomes. NonetheT. brucei brucei and T. brucei gambiense[Toxoplasma gondii strains from the 3 main evolutionary lineages available at ToxoDB[Schistosoma mansoni[T. cruzi and the other protozoan genomes through a large time of divergence. The fact that most of the allelic diversity observed within the CL-Brener hybrid has been acquired through the proposed hybridization events between ancestral individuals from lineages TcII and TcIII, imply that the observed genetic diversity within the CL-Brener clone corresponds to the expected diversity between T. cruzi DTUs TcII and TcIII. As reported recently in an analysis of 32 loci in an expanded panel of strains, the distances between the Esmeraldo-like (TcII) and non-Esmeraldo-like (TcIII) alleles of CL-Brener were the maximum observed within T. cruzi[When we analyzed the set of high-quality SNPs identified according to the type of change they introduce at the protein level, we observed a slightly higher number of non-synonymous SNPs. This result is in agreement with similar observations made in other protozoan organisms: i) the comparison of the genomes of gambiense; and ii)t ToxoDB. Howevera mansoni) where a T. cruzi. The esttrans-membrane domains, and cleaved C-terminal tails (C-termini that are cleaved after the addition of glycosylphosphatidyl inositol anchors). Although not statistically significant at the genome scale, we did find a number of cases in which there was striking contrast in the accumulation of non-synonymous changes between natively unstructured vs globular regions.The distribution of synonymous SNPs along the coding regions was uniform. However in the case of non-synonymous SNPs, we observed a tendency for accumulation in signal-peptides, Saccharomyces cerevisiae suggest that this accumulation is particularly associated with proteins whose function is related to adaptive processes[T. cruzi lags behind S. cerevisiae in functional characterization of its protein coding gene set, it is tempting to speculate that a similar trend is likely to be observed. Proteins carrying natively unstructured or disordered domains, such as the human transcriptional regulator p53, are often at the center of large signaling networks, and serve as organizing scaffolds for protein complexes[Leishmania donovani[Leishmania species causing cutaneous leishmaniasis. Recently, this protein has been shown to be upregulated during a temperature shift similar to that experienced by the parasite upon transmission from the insect to the vertebrate host  is obtained for TcIV strains. However, by sequencing TcII or TcIII strains, only a minimal number of additional SNPs are uncovered, corresponding to 3-4% of the total. This reflects the fact that the majority of these nucleotide changes have already been identified from CL-Brener alleles. The same low rate of discovery of new SNPs is observed when re-sequencing strains from the TcV lineage. Strains from this lineage show very limited genetic diversity when compared with the TcVI strains. Although these observations are based on a small scale re-sequencing study, the same trend can be observed when analyzing additional loci from the draft genomic data for TcI and TcII strains (data not shown). According to this analysis, the next step to significantly increase the coverage of the genetic diversity identified for T. cruzi, should be the analysis of complete genomic or transcriptomic data from a TcIV strain.The genetic diversity identified in this work gives a broad overview of the diversity present within the  per DTU, as well per DTU. This di per DTU. SencondT. cruzi, we have compiled an initial map of genetic diversity for this important parasite, focused mostly on protein coding, single-copy regions of the genome. The picture emerging from this analysis reflects the highly divergent nature of the ancestral haplotypes of the hybrid CL-Brener strain. However, the analysis also shows that there is a highly conserved core of the genome under apparent purifying selection, and highlights a number of genes and domains deviating from this extreme. The work represents the first genome-wide map of genetic diversity for T. cruzi, covering about half of the estimated nucleotide diversity of the species.By taking advantage of the genomic and transcriptomic sequence data from a number of strains representative of different evolutionary lineages of T. cruzi CL-Brener (DTU/genotype TcVI) and Sylvio X10 (DTU/genotype TcI) genomes [Data used for SNP identification included: the 00000000, and ADW00000000, respect(strain). To vali(strain).T. cruzi repetitive elements and vector (plasmid) sequences and the corresponding matching regions were masked[T. cruzi coding sequences from the genome project (CDS), and other nucleotide  sequences were mapped to the reference genome sequence scaffolds using BLAT[http://www.phrap.org). When more than one multiple sequence alignment was obtained for a group of clustered sequences, we manually merged the alignments if they met the following criteria: i) the sequences from the reference genome did not have an assembly warning note; ii) there were no more than two reference coding sequences from the CL-Brener genome in the merged alignment; and iii) the divergence between the reference sequences, as judged by manual inspection, would justify the merging . Genomic data from the Sylvio X10 draft assembly, and from reconstructed transcripts from the TcAdriana 454 RNA-seq data were mapped against these CL-Brener reference alignments. Finally, the quality of each MSA was assessed using two parameters: the % identity, and an estimate of the effective length of the alignment. This latter figure provides the percentage of columns in the alignment where the alignment depth >= 2. MSAs with a % identity > 95% were considered further. MSAs that did not meet these criteria were manually inspected to evaluate further manual editing . Because in some cases alignments might contain sequences that are shorter at one or both ends, the effective length of the alignment (ignoring regions covered by a single sequence) was used to compute alignment attributes, such as the SNP density.Before clustering, sequences were screened against a library of e masked. Sequencing BLAT. SequencMultiple sequence alignments were scanned using a custom Perl script to identify polymorphic columns. These putative SNPs were analyzed using PolyBayes to obtaiThe nucleotide diversity indicator π  was calcT. cruzi was prepared by extraction with DNAZol (Invitrogen), according to the protocol provided by the supplier. To select the regions for amplification, we started from a multiple sequence alignment containing putative polymorphic sites, and identified regions in the alignment that correspond to blocks of conserved sequence . Two such regions separated by a block of 400–600 bp and containing at least 2 putative polymorphic sites were selected in order to design the corresponding 5’ and 3’ amplification primers. The genes and oligonucleotides used for PCR-based re-sequencing are listed in the Additional file2 (Invitrogen) and 16.2 μl MiliQ water (Millipore). Amplification products were checked in 1.2% agarose gels stained with ethidium bromide and if a single amplification product was observed, an aliquot of the amplification reaction was treated with Exonuclease I (Fermentas) and Shrimp Alkaline Phosphatase (SAP) (Fermentas) and two sequencing reactions were prepared, each with one of the primers used for the amplification of the product. Sequencing was carried out in an Applied Biosystems 3130 capillary sequencer using a Big-Dye terminator cycle sequencing kit, according to the instructions of the manufacturer. Chromatogram data derived from these reactions was analyzed using PolyPhred[DNA from the CL-Brener and Sylvio X10 strains of olyPhred to identhttp://tdrtargets.org) we ran a query for each selected attribute to filter the genome and obtain a subset of genes . After running all queries we assigned different numerical weights to each subset of genes (see below), and calculated a weighted union of these lists. The final score of a gene is the cumulative sum of the weights derived from each list in which the gene was present. This possibility of obtaining weighted unions (instead of intersections) allowed us to make a comprehensive prioritization of the T. cruzi genome using a large number of criteria. The following attributes were evaluated , yeast (S. cerevisiae) or C. elegans) orthologs; absence of orthologs in mammals (human/mouse); presence of orthologs in other kinetoplastids ; proteomic evidence of expression in amastigotes and trypomastigotes ; target is present in multiple copies (> 2 copies per haploid genome) ; presence of a signal peptide (ER-secretory route) ; presence of a GPI signal sequence . To further improve on this prioritization, we assigned positive scores to genes that are likely to be under purifying selection . The score for each gene was calculated using a sigmoid normalization function:To prioritize targets, we used the functionality available within the TDR Targets database to assign different scores to genes depending on the presence/absence of different attributes or features,70. BrieriTrypDB); availak is a scaling factor (0.08 in this case). A list of genes with their corresponding scores calculated in this way was uploaded to TDR Targets as a tab-delimited text file, to finish the prioritization strategy. The result of this prioritization is shown in the Additional filehttp://tdrtargets.org/published/browse/423).where π is the nucleotide diversity and T. cruzi identifiers referenced in this work appear in their current shorter form, e.g. TcCLB.507853.10 .According to recent changes related to standardization in trypanosomatid locus identifiers used in community databases, all such http://snps.tcruzi.org. All TcSNP alignment identifiers mentioned in this work (e.g. tcsnp:438249) can be accessed using the following standard URI, and replacing the alignment identifier (e.g. 438249) in each case:http://snps.tcruzi.org/genes/alignment?assembly_id=438249.The sequences reported in this paper have been deposited in the GenBank database  and are also available from the TcSNP database atSNP: Single nucleotide polymorphism; DTU: Discrete typing unit.The authors declare that no competing interests exist.AA and LP assembled the alignments of CL-Brener coding sequences, LP mapped coding sequences from draft genome assemblies, AA and LP identified and characterized SNPs, performed statistical analysis, and drafted the manuscript. DOS participated in the design of the study. FA conceived the study, participated in its design and coordination, and wrote the manuscript. LP and ROC carried out the re-sequencing study to validate SNPs. All authors read and approved the final manuscript.Table S1. Complete list of alignments used for the detection of SNPs in this work. The table contains information on the alignments and the type and number of SNPs identified in each case. The Calc/Excel workbook contains two spreadhseets: the first contains a complete legend, describing all columns in detail; the second contains the data itself.Click here for fileTable S2. Experimental validation of identified SNPs by PCR-based re-sequencing. The table shows, for each loci analyzed, the number of SNPs identified in silico, as well as those that have been validated (V) or not (NV) by re-sequencing.Click here for fileTable S3. List of nonsense polymorphisms identified in this work. For each nonsense SNP identified we show the corresponding codon for each genome/strain analyzed, and N/A if the gene has not been identified in a dataset.Click here for fileFigure S1. Nonsense SNPs observed in the CL-Brener genome. (A) List of observed nonsense SNPs, sorted by descending frequency. Nonsense changes shown in black require only a single mutation to produce a stop codon. SNPs shown in color require changing two bases in the same codon . (B) List of observed nonsense mutations, by type (Ts/Tv). (C) Genetic code showing all possible single base changes that would produce a stop codon, and those observed in T. cruzi.Click here for fileFigure S2. SNP density in globular vs unstructured protein domains. The density of SNPs in globular vs intrinsically unstructured regions (predicted by IUPred) was compared for synonymous, and non-synonymous changes (left and middle panels). A third panel (right) showing all SNPs is shown for comparison. In all cases the differences between the distribution were not statistically different.Click here for fileFigure S3. A natively unstructured domain accumulating non-synonymous changes . Multiple sequence alignment showing the TcCLB.506553.20 gene, and its allelic counterparts in other strains . The gene has a globular N-terminal domain, and an intrinsically unstructured C-terminal domain, as predicted by IUPred[y IUPred. This fiClick here for fileTable S4. GO Slim annotation of alignments used for the detection of SNPs in this work.Click here for fileTable S5. KEGG pathway mapping of alignments used for the detection of SNPs in this work.Click here for fileTable S6. Drug target prioritization using a score based on the nucleotide diversity estimator (π) as one of the main driving criteria. The spreadhsheet contains the top 300 targets. The complete ranked proteome, as well as the individual queries can be accessed online at http://tdrtargets.org/published/browse/t/423.Click here for fileTable S7. Primers and amplification products used in the SNP validation experiment.Click here for file


High-throughput sequencing is a common approach to discover SNP variants, especially in plant species. However, methods to analyze predicted SNPs are often optimized for diploid plant species whereas many crop species are allopolyploids and combine related but divergent subgenomes (homoeologous chromosome sets). We created a software tool, SNiPloid, that exploits and interprets putative SNPs in the context of allopolyploidy by comparing SNPs from an allopolyploid with those obtained in its modern-day diploid progenitors. SNiPloid can compare SNPs obtained from a sample to estimate the subgenome contribution to the transcriptome or SNPs obtained from two polyploid accessions to search for SNP divergence. The advent of high-throughput sequencing technologies is revolutionizing our ability to discover and exploit single-nucleotide polymorphisms (SNPs). Polyploidy occurs in many animals and plants but is particularly widespread in flowering plants, including many major crops. However, most methods used to discover and validate predicted SNPs are optimized for diploid species, so specific challenges related to polyploidy remain to be addressed.Coffea arabica), wheat (Triticum durum Desf.), cotton (Gossypium hirsutum L.), and peanut (Arachis hypogaea L.) are allopolyploids and contain two or more distinct genomes (homoeologous chromosomes) after interspecific hybridization between related diploid species and chromosome doubling. As a consequence, allopolyploid genomes hold different copies of the most of their genes and genomic merger and doubling leads to an extensive array of genomic effects, including alterations in the expression of these duplicate genes (“homoeologs”). In an allopolyploid, the chromosomes derived from different parental species do not pair at meiosis and the gene copies, “homoeoalleles” or “homoeologs,” derived from different parental species have no allelic relationships and can consequently be distinguished from true alleles. In other words, sequence variation between subgenomes coexists with allelic variation within subgenomes. Accurate identification of homoeoSNPs  in tetraploid sequence data is a challenge due to the coassembly of homoeologs. In a co-assembly, single nucleotide differences between the two subgenomes could be confused with SNP at a single locus.Many polyploid plants including coffee (The sequencing of transcripts using high-throughput sequencing methods (RNA-Seq) can provide fresh insights into polyploid biology . Typicalhttp://sniplay.cirad.fr/cgi-bin/sniploid.cgi.Here we present a new tool, SNiPloid, that can tackle the many aspects involved in the analysis of SNPs in the context of allopolyploidy. Based on the coassembly of homoeologs, SNiPloid compares either putative SNPs detected from an allopolyploid to those obtained in its parental genomes, or putative SNPs derived from two allopolyploid accessions to search for polymorphism. SNiPloid web server and source code (downloadable under the CeCILL public license) are accessible at https://main.g2.bx.psu.edu/) as described in Before interpreting the results of RNA-Seq data using SNiPloid, data preprocessing is required. Biologists can preprocess their data through the Galaxy public server  derived from unique single genotype or distinct accessions (diploid or polyploid) are separately aligned against a single diploid transcriptome reference corresponding to one of the parental diploids using dedicated mapping software such as BWA , Soap 33, or BowMapping alignment is a key step in data preprocessing and mapping parameters need to be adjusted and optimized to best fit the single diploid genome used as reference. Actually, since the reference diploid transcriptome is more closely related to one of the two subgenomes in the tetraploid, it might have collateral effects on the mapping efficiency and indirectly cause biases in the interpretation of the SNP, notably when analyzing the relative homoeologous gene expression represented by the contribution of subgenomes to total gene expression. UnifiedGenotyper module in the GATK toolkit [Depth Of Coverage module of GATK. Optionally, SNP discovery and subsequent SNiPloid analysis can be improved by running the GATK ReadBackedPhasing utility to determine potential associations between alleles and produce phasing.The SNiPloid utility uses the power of the Variant Call Format (VCF) which lists SNP variations and assigns alleles for each sequenced sample, by comparison with a reference sequence . The VCF toolkit  for SNP Inputs to the SNiPloid software consist of two different GATK outputs for each sample: (i) a VCF file listing putative SNPs and (ii) a coverage depth file . For eacSNiPloid comprises three main steps . The firIn the second step also for each sample, SNiPloid extracts alleles from the VCF file for SNP positions within the defined common regions. In the third step the differences observed between samples are compared and the situation is interpreted. Polyploid versus parental diploid”), SNiPloid offers the option to compare, interpret, and cluster SNPs. Based on the coassembly of homoeologs, SNiPloid is able to infer the SNP genome origin and distinguish interspecific SNPs and homoeoSNPs (or genome specific SNP = HSV) [Patterns 1 and 2 correspond to interspecific SNPs and are assigned if an allele is specific to one of the parental genomes. The mutation occurred after the polyploidization event .Pattern 5 corresponds to putative homoeoSNPs because the same variation is observed in tetraploids and between parental genomes . With this pattern, SNiPloid identifies in which subgenome the homoeoallele resides by using diploid sequence alleles. In the second step, by retrieving and combining allelic depths for the reference and alternate alleles provided in the VCF format, it can estimate the subgenome contribution to the transcriptome for each homoeologous genes.Patterns 3 and 4 are attributed when the variation observed in the tetraploid is not identified between parental genomes . The mutation may have occurred in one of the subgenomes of the allotetraploid after the polyploidization event. With a mixture of reads originating from two subgenomes in the mapping of an allotetraploid, pattern 3 or 4 cannot be attributed without haplotype information, and a pattern “3 or 4” is assigned. In addition, SNiPloid can benefit from the phasing information included in the VCF file derived from the allotetraploid to infer the origin of an allele and distinguish between a hypothetical evolution pattern 3 or 4. Indeed, the VCF format anticipates the coding of allele phasing information  in order to define haplotype blocks. Thus, if provided in the VCF, the phasing information can specify potential associations with SNP pattern 5 whose subgenome origin is known and thus distinguish between patterns 3 and 4. Basically, this process based on the haplotype makes it possible to identify putative subgenome specific SNPs.Using its main functionality (“P = HSV)  by compahttp://southgreen.cirad.fr) and is accessible at http://sniplay.cirad.fr/cgi-bin/sniploid.cgi as a specific utility of the SNiPlay application [SNiPloid is a component of the South Green Bioinformatics Platform  format as additional input, supplying the viewer program with the coordinates of gene models used as reference on the genome. The aim is to rapidly localize potential highly bias-expressed regions, introgressed genes, or homogenized regions within the genome.Coffea arabica by using the SNiPloid software for the analysis of the contribution of subgenomes to the transcriptome [C. arabica, a recent allopolyploid combining two subgenomes that derive from two closely related diploid species: C. canephora and C. eugenioides. Different samples of C. arabica obtained at contrasted temperatures and one C. eugenioides sample were mapped against the C. canephora reference transcriptome, analyzed for SNP discovery, before being compared with SNiPloid in order to estimate homoeologous gene expression and to highlight potential variation between growing conditions. Additionally, by mapping reads against the C. eugenioides transcriptome instead of C. canephora, this study showed that the relative homoeologous gene expression is slightly biased in favour of the genome used as reference, as anticipated above.A whole transcriptome analysis was conducted on the allotetraploid criptome . This stSampled from this study, an example of datasets is provided by the SNiPloid Web server to familiarize users with the correct input and expected results.The main functionality of SNiPloid is dedicated to RNA-Seq data and to polyploid species for which a diploid transcriptome reference is available for at least one of the parents.One limitation of the use of RNA-Seq for SNP detection and subsequent interpretation is that the transcript sequences represent only the expressed part of the genome and that the sequencing depth varies considerably across the genome due to the different gene expression levels. Thus, only SNPs in well-expressed genes can be detected and allele or homoeolog expression bias could make the detection of certain SNP difficult due to their low frequency in the transcriptome. However, NGS technologies and the use of appropriate read cutoffs allow to detect and interpret SNPs for a large number of genes distributed across the genome.Theoretically, even though the allele expression quantification would not be performed, a genome wide analysis would be also possible on genomic data. However from a technical point of view, whole genome analysis would be difficult to perform through our Web server, since it requires uploading VCF and depths file inputs that would be sizeable and should be computed by command line after having downloaded the SNiPloid package or through Galaxy.In terms of performance, in our practical experience two RNA-Seq samples derived from a polyploid and a diploid species first mapped against a complete reference transcriptome and then generating 600 000 putative SNPs each can be successfully compared by SNiPloid Web server in less than five minutes.Even though numerous SNP bioinformatics tools or pipelines exist for SNP calling (GATK , VarScanAn example of pipeline reported by Hand et al.  predictsThis approach is relevant and more advanced but can appear slightly more fastidious to operate. The main advantage of SNiPloid is its ease to be applied since it does not require preliminary work to establish homoeoSNPs database that can be time-consuming, and offers to non-bioinformaticians a ready-to-use Web server allowing to rapidly obtain subgenome attribution thanks to a “one click” analysis.In addition, our approach seems to be more appropriate for allopolyploid species for which the polyploidization event is relatively recent in the evolution such as Coffea or Spartina.To our knowledge, SNiPloid is the first Web tool dedicated and optimized for the SNP analysis of RNA-Seq data obtained from an allopolyploid species. By exploiting the well-organized information stored in the standard VCF format, SNiPloid helps to interpret putative SNPs detected in a whole transcriptome by a comprehensive SNP categorization. SNiPloid is appropriate for allotetraploids and opens new prospects for investigating allopolyploid genome structure or expression.


Aims. To explore whether Astragalus or its formulations could prevent upper respiratory infection in children with nephrotic syndrome and how best to use it. Methods. We transformed a common clinical question in practice to an answerable question according to the PICO principle. Databases, including the Cochrane Library , PUBMED (1966–2012.8), CBM (1978–2012.8), VIP (1989–2012.8), and CNKI (1979–2012.8), were searched to identify Cochrane systematic reviews and clinical trials. Then, the quality of and recommendations from the clinical evidence were evaluated using the GRADEpro software. Results. The search yielded 537 papers. Only two studies with high validity were included for synthesis calculations. The results showed that Astragalus granules could effectively reduce URTI in children with nephrotic syndrome compared with prednisone treatment alone . The dose of Astragalus granules was 2.25 gram  twice per day, at least for 3–6 months. The level of evidence quality was low, but we still recommended the evidence to the patient according to GRADEpro with the opinion of the expert. Followup showed the incidence of URTI in this child decreased significantly. Conclusions. Astragalus granules may reduce the incidence of URTI in children with nephrotic syndrome. Infection is one of the most common complications in children with nephrotic syndrome (NS). The annual incidence of invasive bacterial infection based on retrospective case series was shown to be about 1-2%, and the cumulative risk for the 10-year susceptibility period was around 10–20%  oWe managed to get the URTI rate in the two remaining studies , 18 by cHuangqi intervention versus conventional treatment alone was 0.56 . The origin of the herb was authenticated, and the material was examined for microorganisms, heavy metals, and pesticides, according to accepted standards  in China. The Astragalus was found to be of good quality, and the crude Astragalus had been extracted by TianJiang Pharmaceutical Co. Ltd., China. The Astragalus was extracted at a ratio of 1.5 g from 10 g of crude Astragalus. Freeze-dried extracts of Astragalus were analyzed by high-performance liquid chromatography using Astragaloside IV as an active component. According to the two studies included , 18 and Astragalus granules  twice/day for 2 years. During the Astragalus granule treatment period, prednisone was tapered gradually . When the patient had URTI, he was asked to stop taking Huangqi granules and to maintain his prednisone dosage. After 3 months of Astragalus granule treatment, he felt less fatigued and sweaty, and his serum immunoglobulin G reached normal levels. During the maintenance period on prednisone (10 mg/d), he suffered from URTI once, and this was alleviated in 3 days without relapse of NS. He had URTI twice; his symptoms were mild and lasted for no more than 7 days, without relapse of NS, 1 year after the cessation of prednisone. To date, he has had a total of three episodes of URTI in 2 years, compared with 8 times per year before Astragalus treatment, and no relapse of NS, compared with three relapses in 1 year before Astragalus treatment.The patient was administered 2.25 g Before 1940, mortality in children with nephrotic syndrome reached 40%, due, mainly, to infections . AlthougAstragalus has been shown to be able to correct the problems causing susceptibility to URTI in nephrotic patients. For example, Astragalus decreased soluble IL-2 receptor (SIL-2R) and IL-8 levels, while increasing IgA, IgM, and IgG levels in patients suffering from recurrent URTI [Astragalus formulation  to young BALB/c mice enhanced influenza virus-specific IgA antibody titers significantly in the nasal cavity. This may be one of the mechanisms by which Astragalus and its formulations reduce influenza virus infection [ent URTI . Experimnfection .Astragalus was also found to reduce overinhibition by immunosuppressants. In vitro studies showed that Astragalus reversed the inhibition of cyclophosphamide on T lymphocytes in experimental rats [Astragalus and its formulations have been used widely in patients on immunosuppressive treatment. Astragalus inhibits common viruses, such as coxsackievirus [Staphylococcus aureus [tal rats , 33. In kievirus  and infls aureus , that caAstragalus would seem to have a good biological basis for use in preventing URTI in nephrotic patients.Based on these findings, According to the GRADE method of bias evaluation, there were potentially large biases in the included studies, which could have had an impact on the outcome. For example, there was no description of adequate sequence generation, allocation concealment, or blinding, which could have produced bias in selection, implementation, and measurement. All eligible studies were carried out in China and published in Chinese; thus, positive results were more likely to have been reported than negative results . For theAstragalus with the patient was low, we had the aid of the GRADE instruments to make the clinical decision. First, the GRADE tools consider the recommendation strength, reflecting how well we could determine whether the intervention would do more good than harm [Although the level of evidence for using han harm . The benSecond, because each treatment has its own advantages and disadvantages, it is important for doctors to make clinical decisions by evaluating benefit, risk, and convenience from the patient's point of view . The patAstragalus therapy is much cheaper than other methods commonly used in China to prevent URTI in nephrotic syndrome, such as injections of gamma globulin or thymosins. Thus, it can be used with the vast majority of patients with nephrotic syndrome.Finally, another key factor in clinical decision making is the cost. The possibility of a strong recommendation for a very good treatment can be reduced because of its high cost and unavailability. Thus, one of the crucial factors in recommendation strength is evidence regarding the conditions and demands of its practical use . AstragaAstragalus membranaceus was the right herbal medicine for this syndrome. From the perspective of modern medicine, the serum IgG of the patient was slightly low, showing a decline in humoral immune function, and this may have been related to the frequent occurrence of upper respiratory tract infections. Astragalus can improve immune function and enhance the serum level of IgG. Thus, the use of Astragalus in the patient could prevent the relapse of NS through reducing the incidence of upper respiratory tract infection. The dose and duration of Astragalus treatment for the patient were determined from reports of clinical trials and the experience of Professor Huang. The Astragalus granules were from a pharmaceutical company that was GMP compliant. They were administered to the patient, while prednisone was slowly tapered, though maintained for a long enough time. When an upper respiratory tract infection occurred, manifested as a sore throat and fever, the Astragalus granules were stopped temporarily and then restarted when the infection was controlled and the symptoms disappeared. After 3 months of therapy, the syndrome of lung Qi deficiency in the patient was obviously improved, and the serum IgG returned to normal levels. In the 2-year period of prednisone maintenance and discontinuance after administration of Astragalus, his incidence of upper respiratory tract infection was diminished significantly (1.5 times per year versus eight times per year), and there was no relapse of NS.To summarize, we decided to administer Radix Astragali to the patient to prevent URTI, and the dose and course were referred to an expert for an opinion. According to Professor Huang's experience of syndrome differentiation in Chinese medicine (CM), the outstanding symptoms of the boy were fatigue, hidrosis, and being prone to suffer from upper respiratory tract infection, indicating a lung Qi deficiency.


Coxiella burnetii has the potential to cause serious disease and is highly prevalent in the environment. Despite this, epidemiological data are sparse and isolate collections are typically small, rare, and difficult to share among laboratories as this pathogen is governed by select agent rules and fastidious to culture. With the advent of whole genome sequencing, some of this knowledge gap has been overcome by the development of genotyping schemes, however many of these methods are cumbersome and not readily transferable between institutions. As comparisons of the few existing collections can dramatically increase our knowledge of the evolution and phylogeography of the species, we aimed to facilitate such comparisons by extracting SNP signatures from past genotyping efforts and then incorporated these signatures into assays that quickly and easily define genotypes and phylogenetic groups. We found 91 polymorphisms (SNPs and indels) among multispacer sequence typing (MST) loci and designed 14 SNP-based assays that could be used to type samples based on previously established phylogenetic groups. These assays are rapid, inexpensive, real-time PCR assays whose results are unambiguous. Data from these assays allowed us to assign 43 previously untyped isolates to established genotypes and genomic groups. Furthermore, genotyping results based on assays from the signatures provided here are easily transferred between institutions, readily interpreted phylogenetically and simple to adapt to new genotyping technologies. Coxiella burnetii, it is particularly important to compare typing results to other collections as C. burnetii collections are rare, sparse and not easily transferred due to select agent regulations and biosecurity concerns. In order to better understand epidemiological patterns we have therefore built upon an existing sequence based typing scheme to produce a few simple and rapid assays whose results are unambiguous, easily transferrable, and can be directly compared to the largest characterized collection of C. burnetii in the world.Sequence based DNA signatures are widely used for molecular typing as they provide unambiguous results that are easily transferred and compared between labs. In this era of rapid and inexpensive sequencing, whole genome sequence comparisons often reveal many polymorphisms that can be used to develop new assays for increased discrimination among samples and to better define phylogenetic relatedness. Despite drastic reductions in cost, whole genome sequencing is still expensive relative to other typing technologies. As well, the data handling, processing, and interpretation required for whole genome sequence analyses make sub-genome typing methods more viable when many samples need processing. For phylogenetic and population genetic inferences, a large sample size is also important as samples are compared to each other and accuracy of conclusions is directly tied to comprehensive sampling. Unfortunately, switching to new typing methods often results in lost information between old and new systems as data cannot be directly compared. As such, past data and efforts may be simply discarded or, when possible, old samples may be re-analyzed with the new typing scheme  C. burnetii. Most genotyping methods are cumbersome and require relatively large quantities of DNA. Before the very recent development of a cell-free growth procedure Despite the serious nature of Q fever, little is known about the prevalence and dissemination patterns of C. burnetii is maintained by the Rickettsial Unit in Marseille, France. As of February, 2011, this publicly available database (http://ifr48.timone.univ-mrs.fr/MST_Coxiella/mst) listed 170 samples that have been genotyped using multispacer sequence typing (MST) which involves sequencing ten intergenic regions for a total sequence length of ∼4,813 bp C. burnetiiC. burnetii. As such, this collection represents a foundation that can be built upon by typing other collections in a manner where results can be directly compared. This will ultimately increase the size and geographic distribution of samples available for epidemiologic analyses. Here, we aim to exploit past genotyping efforts by extracting SNP signatures from MST loci and targeting them using rapid and inexpensive SNP assays. These SNP data allow us to assign isolates to previously described genomic groups The most diverse published collection of We further analyzed the phylogenetic results reported by Glazunova et al. http://ifr48.timone.univ-mrs.fr/MST_Coxiella/mst/group_detail) and for each locus alignments of alleles were made using the Clustal W alignment algorithm in MegAlign . We also determined the MST allele sequences in silico for the seven C. burnetii whole genome sequences available from NCBI (http://www.ncbi.nlm.nih.gov/genomeprj/16724). These alleles were blasted against the MST database and novel allele sequences were added to the alignments described above. We used these alignments , following the tissue lysis protocol with proteinase K lysis performed at 56°C overnight. For the 12 melt-MAMA assays, 1 µL of DNA was used in a total PCR reaction volume of 10 µL that contained 1× SYBR® Green PCR Master Mix , 300 nM consensus primer, and variable amounts of allele-specific primers see . ThermalFor the two TaqMan minor-groove binding dual-probe assays, 1 µL of DNA was also used in a total reaction volume of 10 µL that contained 1× TaqMan® Genotyping Master Mix , 900 nM of each primer and 200 nM of each probe . Thermalhttp://ifr48.timone.univ-mrs.fr/MST_Coxiella/mst), included a novel sequence type (ST), and used a parsimony approach rather than UPGMA. There were 91 SNPs (eight of which were homoplastic) and 21 indels (four of which were homoplastic)  and African Q  also revealed previously published alleles at all loci except Cox56 which is absent from these genomes. For these two genomes, the combination of known alleles at the 9 present loci directly matched a single genotype in the Marseille database each, suggesting that these samples are most likely genotypes 8 (MSU Goat) and 2 (African Q), or at most, single-locus variants of these genotypes  revealed new alleles at 8/10 MST loci and thus created a new branch on the tree . A subsequent maximum parsimony phylogenetic analysis of only the non-homoplastic SNP loci produced a single tree on which the homoplastic SNPs and all indels were added using maximum parsimony criteria . This trplastic) . We wereplastic) . For fouenotypes . Finallythe tree  not prevFor assay development, we selected 14 SNPs that were likely to provide discrimination amongst the major genomic groups described by Hendrix et al. in silico SNP analysis of these 14 markers matched SNP calls determined using our assays for 7/8 samples. The exception was the isolate African Q (RSA 334). In silico analysis narrowed this sample to MST genotypes 1–7 or 30 (genomic group IV) whereas laboratory analysis placed this sample in MST genotypes 16 or 26 (genomic group I) with differences at 5/14 assays . Of the 63 strains and 7 whole genome sequences genotyped against 14 SNP loci, there was only a single instance of homoplasy (1/972 data points), confirming the evolutionary stability of these signatures. As SNPs from multiple branches are assayed against each isolate, this provides a level of redundancy that makes it easy to spot suspicious results that arise if two assays on different parts of the phylogeny place the same isolate in two exclusive clades, as was seen with sample L 35 and assay Cox18bp166.in silico analysis of the whole genome sequence of Dugway 5J108–111 in this work revealed novel alleles at 8/10 loci, resulting in a novel genotype, but also see Mediannikov et al. Of note is that the typing scheme presented here is designed to be fully comparable with MST genotype data and not to identify novel genotypes. However, it is reasonable to assume that novel genotypes may be found. For example, The typing scheme presented here is compatible with current as well as emerging genotyping technologies. SNP based assays are highly amenable to adaptation to different platforms C. burnetii isolates into the phylogenetic context of six genomic groups and 35 MST genotypes, allowing for comparison of existing and new isolates. As these are sequence based signatures, data collected using these assays will remain useful even as platforms and technologies change and can be queried using in silico methods as more C. burnetii isolates are whole genome sequenced.In summary, while whole genome sequencing of every sample is currently impractical, the method we describe here can serve as a bridge between conventional PCR based genotyping and whole genome sequencing. We have developed 14 assays whose data can be used to place Figure S1Alignments of MST alleles. Clustal W alignments of all MST alleles per locus, including in silico derived alleles from the whole genome sequence of Dugway 5J108–111 (CP000733.1). To the left of each alignment, the allele names from Glazunova et al. in silico analysis of Dugway 5J108–111 are listed as MST spacer name followed by Dugway_5J108–111 (example: Cox2.Dugway_5J108–111). With the exception of the Cox22 and Cox37 alleles, all alleles from Dugway 5J108–111 were novel; Cox22 matched allele 6, Cox37 matched allele 4. Nucleotide position per allele is shown above the alignments. Shaded regions indicate areas of identity; unshaded regions denote polymorphisms and have either a dot if the nucleotide matches that of the base found in allele 1 or the polymorphic base call: A, C, G, T or a dash to denote a deletion.(PDF)Click here for additional data file.Figure S2Phylogenetic tree of MST genotypes with labeled branches. Panel A: complete tree as in (PDF)Click here for additional data file.Table S1Phylogenetic characterization of the 112 polymorphisms from MST sequence comparisons.aLocus name is given as the MST locus followed by the base position in the alignment (bBranch indicates the phylogenetic location(s) of each polymorphism on the phylogenetic tree . eAssays presented in this work.lignment . bBranchtic tree . cLocus (XLS)Click here for additional data file.Table S2Epidemiological and results data for samples used in this study.aExpected MST genotype based on overlap of samples with Glazunova et al. b) or from in silico analysis of whole genome sequences from this work (denoted with c). Samples with a listing of n/a were not reported in Glazunova et al. in silico analysis. Thus we could not predict the genotype based on other studies. dObserved MST genotype(s) based on the results from 14 SNP assays presented here. eVaccine strain. fSample contained mixed genotypes as determined by both alleles amplifying at four loci. gAllele determined to be homoplastic based on allele calls at the 13 other loci. Abbreviations and symbols: WGS, Whole genome sequences where sequences and epidemiological data were used from http://www.ncbi.nlm.nih.gov and SNP data was derived using in silico techniques; EP, egg passage; -, epidemiological data is missing or unknown; ?, alleles were absent when genotyped using in silico methods; u, data was undetermined; nt, assay was not tested.(XLSX)Click here for additional data file.


Pummelo cultivars are usually difficult to identify morphologically, especially when fruits are unavailable. The problem was addressed in this study with the use of two methods: high resolution melting analysis of SNPs and sequencing of DNA segments. In the first method, a set of 25 SNPs with high polymorphic information content were selected from SNPs predicted by analyzing ESTs and sequenced DNA segments. High resolution melting analysis was then used to genotype 260 accessions including 55 from Myanmar, and 178 different genotypes were thus identified. A total of 99 cultivars were assigned to 86 different genotypes since the known somatic mutants were identical to their original genotypes at the analyzed SNP loci. The Myanmar samples were genotypically different from each other and from all other samples, indicating they were derived from sexual propagation. Statistical analysis showed that the set of SNPs was powerful enough for identifying at least 1000 pummelo genotypes, though the discrimination power varied in different pummelo groups and populations. In the second method, 12 genomic DNA segments of 24 representative pummelo accessions were sequenced. Analysis of the sequences revealed the existence of a high haplotype polymorphism in pummelo, and statistical analysis showed that the segments could be used as genetic barcodes that should be informative enough to allow reliable identification of 1200 pummelo cultivars. The high level of haplotype diversity and an apparent population structure shown by DNA segments and by SNP genotypes, respectively, were discussed in relation to the origin and domestication of the pummelo species. Citrus maxima (J. Burman) Merrill] is an important cash crop that is widely cultivated and consumed in the world, and particularly in China and other Southeast Asian countries. Pummelo cultivars are easy to be mis-identified for the following reasons: first, there are hundreds of pummelo cultivars, and the origin and morphological traits of many local cultivars have been poorly documented and hence largely unknown to the outside world; second, pummelo cultivars have been traditionally distinguished by a few morphological traits, mostly fruit traits, and trees without fruit are often difficult to distinguish; third, unlike many other poly-embryonic species in Citrus, true pummelos are mono-embryonic and could have been both sexually and asexually propagated during domestication, adding more difficulty to the cultivar identification problem. A reasonable solution to the pummelo cultivar identification problem is to use DNA molecular data to help identify the cultivars.Pummelo , sour orange (Citrus aurantium L.) and grapefruit (Citrus paradisi Macfayden) that are known to be derived from crosses between pummelo and other citrus species Zeaxanthin epoxidase (ZEP), Phytoene synthase (PSY), Phytoene desaturase (PDS) andβ-carotene hrdroxylase (CHX) genes that were 1604, 837, 764 and 2200 bp long respectively were cloned by PCR using the respective gene-specific primers from Guanximiyou and Lingnanshatianyou Two strategies were used to obtain pummelo SNPs. First, pummelo haplotypes were inferred from the EST sequences of sweet orange  was used to choose the most likely value for K.The total number of genotypes and the common genotypes shared by two or more cultivars were analyzed using Dropout 2) of linkage disequilibrium (LD) between SNPs was analyzed using PowerMarker V3.25 2>0.1, p<0.05) were combined as a super locus, and haplotypes and haplotype frequencies were inferred using PowerMarker V3.25. The probability of identity between two random individuals (PI), between two random sampled siblings (PIsibs), and between a random selected pair of a parent and an offspring (PIpar-off) were calculated using GenAlEx The statistical power in identity analysis was calculated for the set of 25 SNPs used in the total sample and in different groups or populations respectively. The level , nucleotide diversity (π) and haplotype diversity (Hd) were obtained by using DnaSP v. 5.10.01 The number of cultivars (n) which could be reliably discriminated by the selected SNPs or DNA segments was calculated using the following formula: Citrus reticulata Blanco var. Nanju) whole genome resequencing data (our unpublished data) using PAUP 4.0 The haplotypes inferred from the 12 gene segments were used to construct Neighbour-joining tree together with the corresponding sequences of the haploid sweet orange genome A total of 60 putative pummelo SNPs were obtained using the two strategies described in materials and methods, and out of them, a set of 25 SNPs (hereafter referred as Set1 SNPs) were selected and used to identify pummelo cultivars . Mapping on the sweet orange reference genome showed that these SNPs are interspersed on 8 of the 9 citrus (2n = 18) chromosomes  except tThe genotypes of the pummelo accessions were revealed by using high resolution melting analysis of amplicons. On all the 25 Set1 SNPs, heterozygotes were clearly distinguishable from homozygotes by shapes of their respective melting curves and derivative melting curves. The Tm difference between the two different homozygote amplicons of each SNP was between 0.6 and 2.0°C, and accordingly, the amplicons' melting and first derivative curves were shifted away from each other along the horizontal axis .HRMA of small amplicons was supposed to be less powerful in distinguishing different homozygotes of A/T or G/C SNPs since there is no difference in hydrogen-bonds between the two homozygotes. However, the SNP chr125991020_A/T which was an A/T SNP showed ∼1°C of difference in Tm values between the two different homozygotes, making them clearly distinguishable . CurrentTo our surprise, two or more different heterozygous melting curves were observed on 3 SNPs during our initial SNP screening work as shown by SNP chr4_18094735A/G in To find how reliable our HRMA genotyping results were, 36 cultivars were repeatedly analysed (two to three times) at all Set1 SNP loci, and a total of 6 mismatches were identified. Detailed analysis indicated that these mismatches were resulted from experimental errors, i.e., sample loading errors, which were identifiable and correctable by either repeating HRMA analysis or inspecting manually the melting curves. In addition, genotyping by sequencing was also done for 1∼25 accessions at Set1 SNP loci to verify the HRMA data. In the end, it was found the results from both sequencing and HRMA were fairly consistent.Genotyping of the 260 accessions were conducted on all Set1 SNPs . As a reFrom this study, a DNA fingerprint database was established for the analyzed pummelos , and canThe 25 SNPs have an average PIC value of 0.271 in the total sample . The samBy using Bayesian clustering program STRUCTURE version 2.3.4 with K = 1∼8, the average ln likelihood values peaked at K = 4 for either the CUL+UNKNOWN or the CUL+UNKNOWN+MYANMAR, indicating most likely there were 4 populations in our analyzed samples. The 4 supposed populations of the CUL+UNKNOWN+MYANMAR had a population differentiation value of 0.16 (Fst), and were designated as P1, P2, P3 and P4. And 29, 46, 38 and 43 of the 156 CUL+UNKNOWN+MYANMAR genotypes were clustered into P1, P2, P3 and P4, respectively .2>0.1, p<0.05) was discovered in three groups of SNPs. The first group contained two SNPs, chr2_30594899T/C and chr2_30595627T/C, which were closely spaced on chromosome 2. The second group contained three SNPs, chr5_12963514T/C, chr5_13684450T/C, and chr5_15275826A/G, that located on chromosome 5. The third group, chrUn_5023005A/G and chrUn_19904498A/G were on different scaffolds having not yet been assigned to any of the sweet orange chromosomes. The 3 groups of SNPs were therefore regarded as three super loci, and hence the 25 Set1 SNPs were actually treated as 21 loci in our statistical analysis  of the 12 gene segments varied from 0.0004 to 0.0061 and averaged at 0.0029 (2 = 0.53) the number of SNPs found on the segment (varied from 1 to 10 SNPs).For the 20 true to type pummelo cultivars remained, the nucleotide diversity  the barcodes can be easily recorded and shared; 2) the identification results are rather precise with statistical support. With such a system, to identify a cultivar needs only to analyze its genotypes at the loci used for genetic barcodes and compare the results to those in the database. In this study we investigated the possibility of using combinations of either SNPs or DNA segment sequences in the identification of pummelo cultivars that are easily misidentified by morphological traits, and to our satisfaction, both methods were shown to be powerful enough to identify our study samples. Therefore, the SNP genotypes listed in Cultivar identification is a prerequisite for a more efficient breeding activity and a more successful cultivation for a crop. The problem is that it is often difficult to identify cultivars solely by morphological traits High PIC and low level of LD between markers have been the two important factors to be considered in selecting markers for high efficient identification of cultivars Eucalyptus species by Correia et al. Fst values  were required for SNPs in order to avoid ascertainment bias The transferability of the SNPs discovered from certain samples to other samples may be limited, which is known as ascertainment bias Taking together, the Set1 SNPs should be very usable for pummelo cultivar identification. And this conclusion was verified by the genotyping results of the 260 accessions. First, only 13 SNPs were needed to discriminate all the discovered genotypes , and eveThe accuracy of the genotyping method influences the efficacy of cultivar identification In due course of verifying the Set1 SNP genotyping results by sequencing, we noted that a high haplotype diversity existed in the sequences. This prompted us to investigate if it was possible to use directly the gene sequences to identify cultivars, which is similar to the traditional DNA barcoding technology used in identifying species. We set out to sequence a total of 12 gene segments for 24 representative accessions. At a first glance, the total PIC value of the 54 Set2 SNPs, for being in 12 tightly linked groups, must be lower than that of the same number of independent SNPs, but in fact the high SNP density also increased the haplotype diversity. The resulted PI value suggested that for the purpose of cultivar identification the 12 gene segments were already powerful enough. It must be pointed out that we did not intentionally select the target segments, yet they showed a surprisingly high discrimination power. Therefore, it should be possible to select even fewer high PIC segments to identify pummelo cultivars. Apparently, more DNA segment sequences are needed to identify pummelo cultivars compared to the DNA barcoding technology used in species identification that uses only 1 or a few DNA segment sequences For fruit trees like citrus, the genetic difference may be either very large between cultivars derived from inter-specific or inter-genic hybridizations or very small between cultivars originated from somatic mutations The origin and dispersion of the pummelo species remain controversial for no wild pummelos have been unequivocally recognized. South China, Southeast Asia and Northeast India were independently proposed to be the origin place since pummelos were highly polymorphic in these areas Figure S1Verification of the primer-template mismatch suspected in HRMA analysis (bottom panels in ) by sequencing. A. Direct sequencing of one homozygote (Ho1) and the two heterozygotes (He1 and He2) on SNP chr4_18094735A/G (black arrows). The forward and reverse HRMA primers were indicated by blue and green arrows, respectively. A previously unknown SNP  was discovered in the reverse primer region and He2 was found to be heterozygous for this SNP. B. Diagrams showing how the extra SNP (chr4_18094754T/C)identified in panel A influenced the PCR amplification efficiency of He2 template. Three haplotypes  were reconstructed for chr4_18094735A/G and chr4_18094754T/C from analyzing melting curves and sequencing data. Ha3 was also identified in the sweet orange reference genome. The SNP induced mismatch on Ha3 (marked by a red cross) reduced the primer-template annealing temperature and thus reduced the PCR amplification efficiency for the He2.(TIFF)Click here for additional data file.Figure S2Assignment of 156 pummelo accessions (CUL+UNKNOWN+MYANMAR) to four populations by STRUCTURE version 2.3.4. P1, P2, P3, and P4 were represented by blue, violet, green and red, respectively. Accession IDs were the same as those in (TIFF)Click here for additional data file.Figure S3Neighbour-joining trees based on inferred pummelo haplotypes on 12 gene segments. Nanju was used as a representative of mandarin. Inferred pummelo haplotypes and mandarin haplotypes were marked in green and orange, respectively. On the top of each tree was the gene ID designated in sweet orange reference genome (PDF)Click here for additional data file.Table S1178 genotypes of the 260 accessions on the 25 Set1 SNPs. Note: Red and blue background indicate the UNKNOWN group and the CUL group, respectively.(XLSX)Click here for additional data file.Table S2PI, PIpar-off and PIsib for all 178 genotypes. Note: PI for a given genotype is the probability that a randomly sampled individual is identical to the given genotype, as is the same case for PIsibs and PIpar-off (XLSX)Click here for additional data file.


Bos primigenius taurus chromosome 14 (BTA14), has been previously shown to be associated with stature by multiple independent studies, and contains orthologous genes affecting human height. A genome-wide association study (GWAS) for BW in Brazilian Nellore cattle (Bos primigenius indicus) was performed using estimated breeding values (EBVs) of 654 progeny-tested bulls genotyped for over 777,000 single nucleotide polymorphisms (SNPs).Birth weight (BW) is an economically important trait in beef cattle, and is associated with growth- and stature-related traits and calving difficulty. One region of the cattle genome, located on GC = 1.34 × 10-9), located at BTA14:25376827, explained 4.62% of the variance in BW EBVs. The surrounding 1 Mb region presented high identity with human, pig and mouse autosomes 8, 4 and 4, respectively, and contains the orthologous height genes PLAG1, CHCHD7, MOS, RPS20, LYN, RDHE2 (SDR16C5) and PENK. The region also overlapped 28 quantitative trait loci (QTLs) previously reported in literature by linkage mapping studies in cattle, including QTLs for birth weight, mature height, carcass weight, stature, pre-weaning average daily gain, calving ease, and gestation length.The most significant SNP  also affect birth weight and size in zebu cattle (Bos primigenius indicus).This study presents the first GWAS applying a high-density SNP panel to identify putative chromosome regions affecting birth weight in Nellore cattle. These results suggest that the QTLs on BTA14 associated with body size in taurine cattle ( Birth weight (BW) is an economically important trait in beef cattle, and is usually the first characteristic measured in a calf. Birth weight is associated with growth-related traits , mature Despite the beef industry’s pursuit of animals with rapid growth, yielding heavier carcasses, selection for these objectives needs to be properly balanced against selection for reproductive traits, which have great economic importance in beef cattle production systems ,6. WhileBos primigenius indicus), with an average of 29.8 ± 2.7 kg )Bos primigenius taurus (BTA) autosome 6 [NCAPG and LCORL, which have been also found to be associated with adult height in humans [PLAG1, CHCHD7, RDHE2, MOS, RPS20, LYN, PENK and TGS1, that were previously found to affect stature in both cattle and humans [Bos primigenius taurus, known as taurine cattle), and GWAS in the humped bovine subspecies  are only now emerging, especially because the first SNP microarrays were optimized for taurine cattle [In particular, two regions of the bovine genome associated with stature and growth have been highlighted recently. The first, located on tosome 6 ,23, sheln humans ,25. The n humans ,21-23, cd humans ,24,26-28e cattle .In this paper, results from a genome-wide scan for SNPs associated with BW variation in Nellore cattle using EBVs of progeny-tested Brazilian bulls are reported. As EBVs take into account information from performance of the individual, progeny and parents, pedigree relationships, and systematic management and environmental factors, they can be used as composite phenotypes for proceeding with association analyses . The obSNP) < 0.98 and Fisher’s exact test P-value for Hardy-Weinberg Equilibrium (HWE) < 1 × 10-5 were 122,611 (16.7%) and 13,194 (1.8%), respectively. Five individuals were removed due to low Call Rate (CRIND). The final dataset included data for 649 individuals and 434,020 SNPs.From the initial set of 777,961 SNPs, 42,669 (5.5%) were non-autosomal markers. Fifty four autosomal SNPs with redundant genomic coordinates were identified and excluded from further analyses. The identity by state (IBS) check revealed no unexpected sample duplicates. A total of 223,309 (30.4%) markers were excluded due to minor allele frequency (MAF) < 0.02. The number of SNPs excluded due to SNP Call Rate (CRP = 0.415), and no outliers were observed. Average accuracy was 0.87 ± 0.11, with minimum, median and maximum accuracies of 0.51, 0.91 and 0.99, respectively. After fitting the regression in formula (2), there was no evidence against the hypotheses of normally distributed (P = 0.57) and homoskedastic residuals . These findings suggest that the dependent variables used were reliable and did not violate possible assumptions of the statistical analyses used hereafter.Based on the results for the Shapiro-Wilk test, there was no evidence that EBVs deviated from normality (P = 5.41 × 10-34) between the k-means assignments  and breeding program subgroups  was found. For the k-means groups, BW average was 0.434 ± 1.306 in cluster 1 and -0.216 ± 1.295 in cluster 2. For the breeding program subgroups, the trait averages in subgroup 1 and subgroup 2 were 0.683 ± 1.276 and -0.119 ± 1.255, respectively. When considering either k-means clusters or breeding program subgroups as subpopulation labels, the EBVs showed homogeneity of variance (P > 0.05), but the trait mean was significantly different between groups . Thus, population substructure was a potential confounder in the genotype-EBV association analysis, which justified the inclusion of eigenvectors from the PCoA as fixed effects in the linear model.The Principal Coordinates Analysis (PCoA) revealed genetic stratification among the Nellore samples Figure . After uT2 values deviating from the expected values were interpreted as SNPs departing from the null hypothesis. The genome-wide deflated P-values are shown in Figure A total of 32 eigenvectors from the PCoA were significantly correlated with the phenotypes, which together explained 15.23% of the genotypic variability. Residuals from the weighted regression on these significant eigenvectors were used as the dependent variable for the SNP association analysis. The quantile-quantile (Q-Q) plot  was detected on BTA14, comprising 5 SNPs (Table 2 = 0.728 ± 0.12). The most significant SNP , located at BTA14:25376827, had an estimated allele substitution effect of 0.452 kg , with lower and upper limits for the 95% confidence interval (CI) of 0.306 kg and 0.598 kg, respectively, and the percentage of the variance in sires EBVs explained by the SNP was 4.62% (with a 95% CI of 2.12-8.09%). The overall rs133012258 A allele frequency was 0.274, whereas the breeding program subgroups 1 and 2 had frequencies of 0.351 and 0.184, respectively. Figure A peak crossing the boundary for Bonferroni significance (α = 1.15 × 10PGC = 1.62 × 10-8), is located within intron 2 of the gene Kell blood group complex subunit-related family, member 4 . Thirteen genes were found within 500 kb of the most significant SNP  and PENK (Table Homo sapiens - HSA), pig (Sus scrofa - SSC) and mouse (Mus musculus - MMU) autosomes 8 (HSA8), 4 (SSC4) and 4 (MMU4), respectively , whose surrounding region has been shown to contain many QTLs, genes and variants affecting stature-related traits in cattle by several independent studies [PLAG1, CHCHD7, RDHE2, MOS, RPS20, LYN and PENK have been found to influence both human and cattle height [Five SNPs on BTA14 were identified as associated with BW in Nellore cattle  [The BTA14 region pointed out by the present study has also been shown to be associated with reproductive traits. Cole et al.  reportedet al. ,30, but et al. ,32, age us luteum,33, blooXKR4 gene in the present study. Lindholm-Perry et al. [XKR4 associated with feed intake and gain in crossbred steers. Bolormaa et al. [XKR4 associated with rump fat thickness measured at the P8 position (CHILLP8) in seven breeds of cattle, including taurine, indicine and composite breeds. The authors found that four of these SNPs were also associated with CHILLP8 in a confirmatory sample of 1,338 animals, including Angus, Hereford and Brahman cattle. Furthermore, Porto Neto et al. [XKR4 and found that although the SNP effect may vary depending on the breed, the variant rs42646708 (BTA14:24573257) explain around 1.3% of CHILLP8 variance in cattle. This SNP is also located within intron 2 of XKR4, only 17.6 kb apart from the intronic SNP detected in the present study, which strongly suggests XKR4 as a candidate gene for being further explored in future studies of weight and carcass traits in Nellore cattle.A significant SNP was found within intron 2 of the y et al.  identifia et al.  found fio et al.  performePGC = 1.34 × 10-9) was found to explain 4.62% of the variance in sires EBVs, with a 95% CI of 2.12-8.09%. One hundred and eighty loci associated with human adult height explain only 10% of the phenotypic variance together, while individual loci account for 0.4% or less [MOS, CHCHD7 and PLAG1 described by [The most significant SNP  with major untyped  causative variants affecting BW and other height-associated traits in Nellore cattle, and further studies would be needed to determine if the QTNs reported by  are alsoBos primigenius) before subspecies formation.The high identity found in the alignment of this BTA14 region against other mammalian species genomes suggests that these orthologous genes are located in a conserved syntenic block which may have arisen and been maintained after speciation from a common ancestor of the mammal clade. Moreover, the evidence for variants associated with growth and stature within this BTA14 region in both taurine and zebu cattle raises two hypotheses: 1) these variants have been introgressed into Nellore via historical admixture with taurine Creole cattle in the maternal line, and was maintained in the breed in spite of several generations of backcrossing; 2) these are ancient polymorphisms, probably already segregating in the founder population of wild Aurochs  greater than or equal to 1 × 10-5  or 3) Call rate (CRSNP) of at least 98%. After the SNP pruning, individuals exhibiting call rate (CRIND) below 90% were also removed. These procedures and many others described later were performed in the R v2.15.0 environment [R base, locally developed scripts, and the GenABEL v1.7-2 package [A total of 654 progeny-tested Nellore bulls were genotyped with the Illumina® BovineHD Genotyping BeadChip assay, according to the manufacturer’s protocol. Genotype calls (i.e. successfully determined genotypes) were defined as genotypes with GenCall Scores greater than 0.70, using the validated standard cluster file provided by the manufacturer. As chromosomes X, Y and mtDNA present different mode of inheritance from the rest of the genome, only autosomal markers with unique genomic coordinates were included into the analyses. After this initial screening, potential duplicated samples were determined by calculating the proportion of alleles identical by state (IBS) shared between all pairs of individuals. Any pair of samples with IBS ≥ 0.95 for 2,000 randomly sampled markers was considered unexpected duplicates, and resulted in the exclusion of both members of the pair. Individual SNPs were removed from the dataset if they did not exhibit: 1) minor allele frequency (MAF) greater than or equal to 0.02, 2) Fisher’s exact test ironment , using c package .Aliança database [Sires genotyped in this study were known to belong to one of two major breeding program subgroups in the database  that havdatabase ,43:L is the total number of loci used for the calculation, pl is the reference allele frequency for locus l, and gl,i and gl,j are the locus l genotypes for individuals i and j, respectively . Calculations were based on 10,000 randomly sampled markers using GenABEL [n x n symmetric matrix  were then transformed to squared Euclidean distances, and the dissimilarities between the subjects within the matrix were captured in n-1 dimensional spaces of n observations (eigenvectors), via classical multidimensional scaling [Where  GenABEL . The cal scaling .R[2 with Yates’ continuity correction in order to see if the algorithm could reproduce the known breeding programs subgroups. Additionally, an F-test for homogeneity of variance between subpopulations and a t-test for difference between subpopulation means, defining subpopulations either as k-means assignments or breeding program of origin, was performed to determine if there was confounding due to stratification.A clustering analysis was applied to the two eigenvectors that explained the largest proportion of the data variance using the k-means algorithm  implemenR. IndividIn order to reduce computation time, the ideas of  were absi is the EBV of sire i, μ is the overall mean, Xij is value i (corresponding to sire i) in the eigenvector j calculated in the PCoA, βj is the estimated effect of eigenvector j, and ϵi is the residual effect for animal j. Only eigenvectors significantly (P < 0.05) correlated with the dependent variable, as assessed by Pearson correlations, were included in the model. Next, the residuals were obtained from the fitted model in (2):Where yand had their homoskedasticity and normality tested by using the studentized Breusch-Pagan test and the Shapiro-Wilk test, respectively. Then, these residuals were used as the new dependent variable for a single-marker linear regression:βg is the marker regression coefficient  and gi is the genotype  of the sire i. For each SNP, βg and its respective standard error (SEg) were estimated using ordinary least squares. The association between the SNP and the trait was assessed via a test statistic, calculated as:Where 2 distribution with one degree of freedom under the null hypothesis. To assess the validity of this assumption, the deviation of the distribution of the test statistics from the expected theoretical quantiles was examined via 1) a quantile-quantile (Q-Q) plot, and 2) calculation of the inflation/deflation factor:The test statistics are assumed to asymptotically follow a χP-values were derived from the χ2 cumulative distribution function for the corrected test statistics. Finally, markers within the smallest 0.1% P-value percentile  were considered for re-analysis with the full model:If λ < 1.1, the inflation was considered acceptable, and the Genomic Control (GC) correction was applied to adjust for that inflation . Then, Pβg = 0, i.e. there is no association between the SNP and the EBVs), which resulted in an adjusted significance of α = 1.15 × 10-7.The EBVs were again weighted by their respective accuracies. The conservative Bonferroni adjustment for multiple testing  was used to reject the null hypothesis  for the estimated allele substitution effect size , pig  and mouse  genome builds were inspected in the Ensembl Comparative genomics alignments and Comparative genomics synteny tools in order to determine if any homologous genes were present in the putative region.The genomic region containing the most significant SNP of a peak was explored by inspecting a 1 Mb window around the location of this SNP using the database  to interdatabase  was alsoBos primigenius taurus; GWAS: Genome-wide association study; IBS: Identity by state; MAF: Minor allele frequency; HWE: Hardy-weinberg equilibrium; PCoA: Principal coordinates analysis; Q-Q plot: quantile-quantile plot; GC: Genomic control; CI: Confidence interval; HSA: Homo sapiens; SSC: Sus scrofa; MMU: Mus musculus; QTL: Quantitative trait locus; CHILLP8: Rump fat thickness measured at the P8 position; QTN: Quantitative trait nucleotide; LD: Linkage disequilibriumBW: Birth weight; EBV: Estimated breeding value; SNP: Single nucleotide polymorphism; BTA: The authors declare that they have no competing interests.JFG conceived and led the coordination of the study. JS, JM, JBC, CPVT, FSS, MVGBS, LRPN and TSS contributed to the study design and coordination. ASC directed the genotyping work and contributed to the data analysis. RC and HHRN provided EBVs and assisted the data analysis. YTU led the data analysis and the manuscript preparation. MCM, LBZ and AMPO contributed in the data preparation and analysis. JBC, FSS, LRPN, TSS, CPVT, JFG, RC, HHRN, ASC and YTU interpreted the results and contributed to edit the manuscript. All authors read and approved the final manuscript.