Biological interpretation of genomic summary data such as those resulting from genome-wide association studies (GWAS) and expression quantitative trait loci (eQTL) studies is one of the major bottlenecks in medical genomics research, calling for efficient and integrative tools to resolve this problem.We introduce eXploring Genomic Relations (XGR), an open source tool designed for enhanced interpretation of genomic summary data enabling downstream knowledge discovery. Targeting users of varying computational skills, XGR utilises prior biological knowledge and relationships in a highly integrated but easily accessible way to make user-input genomic summary datasets more interpretable. We show how by incorporating ontology, annotation, and systems biology network-driven approaches, XGR generates more informative results than conventional analyses. We apply XGR to GWAS and eQTL summary data to explore the genomic landscape of the activated innate immune response and common immunological diseases. We provide genomic evidence for a disease taxonomy supporting the concept of a disease spectrum from autoimmune to autoinflammatory disorders. We also show how XGR can define SNP-modulated gene networks and pathways that are shared and distinct between diseases, how it achieves functional, phenotypic and epigenomic annotations of genes and variants, and how it enables exploring annotation-based relationships between genetic variants.http://galahad.well.ox.ac.uk/XGR.XGR provides a single integrated solution to enhance interpretation of genomic summary data for downstream biological discovery. XGR is released as both an R package and a web-app, freely available at The online version of this article (doi:10.1186/s13073-016-0384-y) contains supplementary material, which is available to authorized users. This ‘big-data-limited-knowledge’ discrepancy stems from the heterogeneous forms and handling of raw data , but is also attributed to imprecision in downstream interpretation , 2. DataUsing genomic summary data as a starting point for knowledge discovery is appealing. Cases in point are genome-wide association studies (GWAS) producing summary data on disease-associated genetic variants (GWAS SNPs) and expression quantitative trait loci (eQTL) mapping producing summary data on expression-associated genetic variants (eQTL SNPs). Firstly, it simplifies raw data  and captures the essential information content. Secondly, GWAS and eQTL summary data are publicly available and well curated in relational databases, such as the GWAS Catalog , ImmunoBDespite the availability and potential utility of this summary data, precise knowledge discovery itself is not trivial. It raises two critical issues: first, how to more systematically use widely distributed knowledge about genes and SNPs, much of which is unfortunately recorded in natural language; and second, how to achieve insights at the gene network level, which is desirable considering the interdependent and often synergistic nature of biological systems involving multiple players to complete the same task.Knowledge use and access via ontologies provides an effective and efficient solution to the first issue. Using ontologies to annotate genes and gene products dates back to the beginning of this century when the Gene Ontology (GO) consortium initiated efforts to digitise gene functions . Since tTo address the second issue, gene interaction data should ideally be generated experimentally for every tissue, in both normal and diseased conditions given the fact that gene interactions are highly context-specific. In reality, an achievable alternative to this is to assimilate available context-specific interactions into a less context-specific, so-called ‘ground-truth gene network’ representing unified interaction knowledge. This strategy can be seen in databases such as STRING  and PathXploring Genomic Relations, we develop the open-source software ‘XGR’ for enhancing knowledge discovery from genomic summary data. In addition to its comprehensive use of ontology and network information, we also show the uniqueness of XGR in 1) ontology tree-aware enrichment and similarity analysis and 2) cross-disease network and annotation analysis. Using real datasets [The above issues identify an emerging need for ‘enhanced interpretation’ , particularly at the SNP and genomic region level. To meet this need, and also within our vision of its general use in edatasets , 24, we Figure As a central part of the knowledge-driven interpretations, we have assembled currently available knowledge at the gene, SNP, and genomic region level (detailed below). All source data are represented uniformly as well-documented RData-formatted files, taking advantage of the R software open-development environment and its infrastructure packages such as igraph  and GenoConceptually similar to a dictionary, an ontology contains well-defined vocabularies  and their relationships to each other, and is readable by both humans and computers. Depending on how relationships between terms are organised, ontologies can be broadly categorised into two types: 1) structured ontologies, where terms are organised in a tree-like structure ), e.g. Gene Ontology , Disease2 > 0.8 with GWAS lead SNPs are retained.SNP annotations are based on the Experimental Factor Ontology (EFO). EFO standardises GWAS traits from the NHGRI GWAS Catalog using well-defined terms . SNPs asUnlike coding genes that are well annotated using ontologies, non-coding genomic regions lack such annotations. Interpretation of these regions relies largely on functional genomic data generated experimentally and on comparative genomic data predicted by computational methods. Genomic annotations currently supported in XGR include a broad spectrum of genomic and epigenomic data including, transcription factor binding sites, DNaseI hypersensitivity sites, histone modifications, expressed enhancers, and genome segmentations Table . Each geXGR supports networks of different interaction types , of varying interaction quality , and of two interaction directions (directed versus undirected). Networks are mainly sourced from the STRING database  and the p value, hypergeometric test < Fisher’s exact test < binomial test. To further investigate the property of the statistical test, we simulated a random set of genes  and estimated how often each enriched term in the real data would be expected from a null distribution based on the simulated data. As seen in Additional file Enrichment analysis (or ‘Enricher’) is based on conventional statistical tests  to identify enriched ontology terms using either built-in or custom ontologies. The Fisher’s exact test establishes the independence between, for example, a user-defined gene group and a group of genes annotated by a term, and compares sampling only to the left part of the null background (without replacement). The hypergeometric test is to sample at random (without replacement) from the null background containing annotated and non-annotated genes. Finally, and in contrast to the hypergeometric test, the binomial test is to sample at random (with replacement) from the null background with the constant probability. As to the ease of reporting the significance level of a term . The function ‘xEnrichDAGplotAdv’ takes this comparison further, highlighting which terms are shared and which are unique in the ontology tree.Annotation analysis (or ‘Annotator’) aims to interpret a list of user-defined genomic regions in two ways: either via annotations of nearby genes by ontologies or via co-localised functional genomic annotations. Thanks to the diversity of source data available and the generalisation of data representation (see above), XGR enables multifaceted interpretation of poorly annotated genomic regions.The function ‘xGRviaGeneAnno’ takes as input a list of user-defined genomic regions, defines the nearest genes within a user-specified distance gap, and conducts enrichment analysis using nearby gene annotations. Similar to enrichment analysis at the gene level, this function gives the choice of structured and non-structured ontologies, producing informative enrichment results that can be visually displayed/compared. Alternatively, both functions ‘xGRviaGenomicAnno’ and ‘xGRviaGenomicAnnoAdv’ conduct region-based enrichment analysis using co-localising functional genomic annotations. The function ‘xGRviaGenomicAnno’ uses the binomial test for estimating the significance of overlaps at base resolution. The function ‘xGRviaGenomicAnnoAdv’ estimates the significance of the observed overlaps against the expectation under the null distribution, which is generated through random sampling from background genomic regions. By default, the background uses annotatable genomic regions (depending on which genomic annotations are used). However, it is advisable for the user to specify this background according to experimental settings. Enrichment results (as ‘eTerm’ objects) from annotation analysis can be visualised and compared using functions ‘xEnrichBarplot’ and ‘xEnrichCompare’.Similarity analysis  calculates semantic similarity between two genes (or between two SNPs) based on their ontology annotation profiles. More precisely, it assesses the degree of relatedness in meaning of annotation profiles from a structured ontology. The function ‘xSocialiserGenes’ conducts similarity analysis for genes using annotations by structured ontologies, while the function ‘xSocialiserSNPs’ conducts SNP-based similarity analysis using annotations from EFO.10(frequency of SNPs annotated by this term). Semantic similarity between each pair of terms is pre-calculated, usually quantified as IC at the most informative common ancestor (MICA) of the two terms. Finally, semantic similarity SIM between two SNPs, S1 and S2, is derived from pairwise term similarity, using best-matching (BM) based methods: average  of a term is defined to measure how informative it is when used to annotate SNPs: –logMS1, S betweenrage Eq. . For a trage Eq. . By defaThe function ‘xCircos’ displays the similarity results using a circos plot, in which the degree of similarity between two SNPs is indicated by the coloured link. This function can be used to display the most similar links, or those links involving a specific SNP only. Two functions, ‘xSocialiserDAGplot’ and ‘xSocialiserDAGplotAdv’, are specially designed to explore the basis of similarity seen in the circos plot. The function ‘xSocialiserDAGplot’ is used to visualise the ontology annotation profile for an SNP, i.e. as a DAG plot of terms used to annotate the SNP, including original annotations (rectangular nodes) and inherited annotations . The function ‘xSocialiserDAGplotAdv’ uses a DAG plot to compare annotation profiles between two similar SNPs.T1 is a set of n1 EFO terms used to annotate S1, T2 is a set of n2 EFO terms annotating S2, MICA is the IC of the MICA of two terms t1 and t2, operators MAX, MIN, and U denote, respectively, maximum, minimum, and union.where Network analysis (or ‘Networker’) identifies the subset (gene subnetwork) from a gene interaction network with nodes/genes labelled with significance information. Depending on how the node/gene significance information is provided, there are two types of network analyses supported in XGR: gene-based network analysis and SNP-based network analysis.The node/gene information is directly provided, e.g. differentially expressed genes with significance measured by false discovery rate (FDR). Given a gene interaction network with nodes/genes labelled with significance, the function ‘xSubneterGenes’ searches for a maximum-scoring gene subnetwork enriched with the most significant (highly scored) genes but allowing for a few less significant genes as linkers . The search for this maximum-scoring subnetwork is achieved via heuristically solving a prize-collecting Steiner tree problem; this approach has been demonstrated to be superior to other state-of-the-art methods. If required, an iterative procedure is applied to identify the subnetwork with a desired number of nodes/genes. For details please refer to our previous publication .p values. The function ‘xSubneterSNPs’ is designed to identify a gene subnetwork that is likely modulated by input SNPs and/or their LD SNPs. It consists of three steps , and (for LD SNPs) LD strength R2.SNP scoring Eq. , which cGene scoring Eq. , which sNetwork scoring, using the function ‘xSubneterGenes’ to identify a maximum-scoring gene subnetwork (with the desired number of nodes if required).We extend the network analysis to the SNP level, allowing node/gene information to be indirectly provided (i.e. derived from the input), e.g. via GWAS SNPs along with eps Fig. :Fig. 3InPSNP is the SNP p value, Pthresh is the significance threshold , and R2 is the LD strength.where ScoreSNP is the SNP score calculated using Eq. d is the gene-to-SNP distance within a maximum of the distance window D, λ is the decay exponent controlling the decaying influence of an SNP on a nearby gene as the distance increases, Ω stands for collections of SNPs (input SNPs and LD SNPs), and MAX denotes maximum scoring scheme used here to only keep the most-informative SNP when a large number of interdependent SNPs are located within the same genetic region.where p values either controlling the FDR (by default) or controlling the family-wise error rate (FWER). FDR is a less stringent condition than FWER. The user can choose how to account for multiple testing.Where a large number of tests are involved, we adjust We rely on the package ‘ggplot2’  for variWe use a next-generation Perl web framework ‘Mojolicious’ , under wWe demonstrate the application of XGR to interpret three commonly encountered types of genomic summary data: 1) gene sets resulting from differential expression studies; 2) GWAS SNPs from GWAS summary data; and 3) eQTL SNPs from eQTL summary data. We first illustrate the functionalities supported in XGR to interpret differentially expressed genes induced by innate immune stimuli . At the This demo illustrates the power of XGR to interpret the output from differential expression studies, with the focus on how to carry out ontology-based enrichment analysis to achieve more informative results.We use Disease Ontology (DO) to interpret differentially expressed genes induced by 24-h interferon (IFN)-γ treatment of primary human monocytes [When using non-structured ontologies such as a collection of pathways, we develop a post-enrichment filtering procedure to identify redundant terms for removal Fig. . The goaThis demo showcases the power of XGR to interpret GWAS SNPs, including network and annotation analysis.Unique to XGR is its ability to identify SNP-modulated gene networks. To do this, XGR first defines and scores genes that are likely under the genetic influence of GWAS SNPs Fig. . When apPTPN22 and MHC genes for polygenic autoimmune diseases and NOD2 for polygenic autoinflammatory diseases. Comparing network genes identifies one gene, STAT3, common to all three categories; a few genes, including TNFSF1A, TNIP1, and two interleukin (IL) genes (IL23R and IL2RA) are shared by two categories, and many genes are unique to one group, suggesting that each disease category has its own specialised network architecture ’ not using EFO ; 2) ‘EFO (+) & Tree (-)’ using EFO but without respecting the ontology tree; and 3) ‘EFO (+) & Tree (+)’ using EFO and also respecting the ontology tree. Using EFO identifies disease terms that would otherwise be missed with conventional analysis. However, without respecting the ontology tree, the redundant disease terms identified would become a burden for interpretation. Compared to conventional analysis, our ontology tree-aware analysis identifies an additional term (‘immune system disease’) that summarises the overall enrichments, illustrated by visualising the enrichment results in the EFO tree  and to include more annotated SNPs: GWAS-reported SNPs  and inherited SNPs from its child terms (‘inherited annotations’). By convention, SNP-based enrichment analysis considers LD SNPs. The benefit of using EFO and justification of our ontology tree-aware enrichment analysis is demonstrated using the disease part of EFO to interpret tes Fig. . We consree Fig. .Fig. 7Necis-eQTLs that were frequently enriched for disease risk loci [cis-eQTLs, whether in the naïve state or upon immune stimulation, are consistently overrepresented in autoinflammatory diseases  as expected, but also linked to Parkinson’s disease (PD).We previously reported context-specific induced isk loci . Using ocis-eQTLs induced by 24-h IFN-γ treatment. A circos plot displays the similarity results for all cis-eQTL SNPs, which can be reduced to display the similarity links involving a specific SNP, in this case rs11150589 (GWAS SNP in UC). The DAG plots clearly show why this SNP is most similar to rs10500264 (GWAS SNP in IBD), and has greater similarity to rs3957148 (GWAS SNP in MS) than rs2066807 (GWAS SNP in PSO). Together with knowledge of eQTL-containing genes such as ITGAL cis-regulated by rs11150589 and CNPY2 by rs2066807  their annotation by EFO terms organised as a DAG, 2) specificity of terms, quantified by information content (IC) indicative of their frequency of annotation , and 3) term–term similarity measured as IC at the MICA of two terms. Figure 807 Fig. , diseaseIn the current era of high-throughput genomics, the volume of data relating to complex human disease is growing at an unprecedented rate. The NHGRI-EBI GWAS Catalog contains 2546 studies at the time of writing (October 2016) , and theAll results described above are generated using the R package. To target users who are unfamiliar with R, we also develop a user-friendly web interface for each of the analyses supported by XGR Fig. . In the As well as software, XGR is also a resource incorporating diverse data types, thereby enabling comprehensive investigation of a gene or SNP set through enrichment, network, similarity, and annotation analysis. User input is not limited to the gene or SNP-centric data types. XGR can also be used to analyse genomic regions directly Fig. , or indeIn the “We evaluate the performance of XGR in generating more informative results than conventional analyses. In particular, we show the necessity of respecting the ontology tree-like structure during enrichment analysis, either for genes or SNPs Figs.  and 7. IWe are actively engaged in, and have a long-term commitment to, ensuring XGR is updated and expanded on a regular basis  as the field advances. For example, the built-in data include a number of structured ontologies, e.g. GO, DO, Human Phenotype Ontology, and EFO. The hierarchical nature of ontologies provides additional information concerning the relationships between terms, which we leverage to enhance downstream biological discovery and increase the informativeness of the outputs generated. SNP-level analysis supported in XGR is unique in its ontology tree-awareness through mapping of GWAS Catalog traits to EFO and the ability to calculate semantic similarity, but is currently restricted to use of this single ontology. As additional resources become available for orthogonal knowledge domains, these will be incorporated into XGR to expand its capacity for multi-layered investigation of genomic summary data. Other than the data expansion, future efforts will focus on increasing and enabling the user base (including deployment to community-driven genomics projects), evaluating predictive use of SNP similarity in chromosomal interactions (such as promoter interactomes ), and exThe publicly available XGR R package and web-app Fig.  presenteProject name: XGRProject home page (web-app): http://galahad.well.ox.ac.uk/XGRR package: http://cran.r-project.org/package=XGROperating system(s): Linux, Mac OS X, WindowsProgramming language: RLicense: GNU GPLAny restrictions to use by non-academics: None.


Mycobacterium tuberculosis within tuberculosis patients undergoing treatment with different combinations of antibiotics.Combination therapy is one of the most effective tools for limiting the emergence of drug resistance in pathogens. Despite the widespread adoption of combination therapy across diseases, drug resistance rates continue to rise, leading to failing treatment regimens. The mechanisms underlying treatment failure are well studied, but the processes governing successful combination therapy are poorly understood. We address this question by studying the population dynamics of M. tuberculosis populations subject to less drug pressure show markedly different dynamics, including cases of acquisition of additional drug resistance.By combining very deep whole genome sequencing (~1000-fold genome-wide coverage) with sequential sputum sampling, we were able to detect transient genetic diversity driven by the apparently continuous turnover of minor alleles, which could serve as the source of drug-resistant bacteria. However, we report that treatment efficacy has a clear impact on the population dynamics: sufficient drug pressure bears a clear signature of purifying selection leading to apparent genetic stability. In contrast, M. tuberculosis, which is well adapted to the human host, purifying selection constrains the evolutionary trajectory to resistance in effectively treated individuals. Nonetheless, we also report a continuous turnover of minor variants, which could give rise to the emergence of drug resistance in cases of drug pressure weakening. Monitoring bacterial population dynamics could therefore provide an informative metric for assessing the efficacy of novel drug combinations.Our findings show that for a pathogen like The online version of this article (doi:10.1186/s13059-017-1196-0) contains supplementary material, which is available to authorized users. The public health and economic impact of drug resistance is steadily increasing , 2. EmpiDirectly observed treatment, short course (DOTS) is the current standard treatment for drug-susceptible TB. DOTS relies on a combination of four antibiotics—isoniazid, rifampicin, ethambutol, and pyrazinamide—administered with supervision for a total of 6 months. When implemented correctly, DOTS is very effective; it reduces the rate of disease relapse to less than 5%  and the However, the focus on treatment failure presents a biased view of the evolutionary processes involved: 95% of all TB patients infected with fully drug-susceptible strains are successfully treated . As a reMycobacterium tuberculosis complex (MTBC) populations by investigating their dynamics within the human host. We used an approach based on very deep population WGS (approximately 1000-fold read coverage per site) of serial sputum isolates from TB patients with high bacillary loads undergoing treatment. We report that MTBC populations in the human host are genetically more dynamic than previously thought. Furthermore, the presence and extent of drug pressure influences the observed changes. Our findings shed light on the genetic principles that underpin well-established clinical practices: combination therapy based on at least four effective drugs constrains the adaptive landscape of MTBC through purifying selection. Conversely, treatment with fewer than four effective drugs alleviates this constraint, allowing positive selection of resistance determinants.We explored the nature of selective forces that shape We collected sputum samples from 12 TB patients at entry, 2, 4, 6, and 8 weeks after commencement of treatment. Three sputum samples were obtained at each time point for each patient. The resistance profile of the initial MTBC isolates was determined with standard phenotypic drug susceptibility testing . The rate of load reduction was significantly lower in patients receiving fewer than four effective drugs , suggesting that observing the same v-SNP across multiple samples was not simply a function of the estimated v-SNP abundance. We then used data from later time points to determine what proportion of the v-SNPs identified in enrolment samples were repeatedly detected and classify them as recurrent. The majority of variants that were detected in more than one parallel sample were also recurrent  and glpK (Rv3696c) contained four and ten v-SNPs, respectively. The former is a known mediator of clofazimine and bedaquiline cross-resistance [mmpR v-SNPs accounted for a very small proportion of the overall population (1–5% of the population) but were nonetheless mostly stable over time—recurrent. glpK variants on the other hand were all unstable despite some of them being relatively abundant in some samples, accounting for 20–30% of the population. In fact, we did not observe any difference in variant frequency between recurrent and unstable v-SNPs in the parallel samples from patient 12 , again suggesting that the temporal stability of variants was not simply a function of abundance, but rather a reflection of an ongoing biological process. Furthermore, this also provides evidence of the co-existence of separate populations of MTBC within a host.Looking at the identity of the affected genes, we found that two contained multiple v-SNPs: sistance , while tsistance . Most ofQuantifying the relative abundance of individual alleles in sputa at different times during treatment therefore allows us to approximate the changes in the composition of bacterial populations within each patient. A naïve overview of temporal dynamics of recurrent v-SNPs revealed four types of allele trajectories: ascending, descending, constant, and sporadic Fig. , showingWe started with a general analysis of population heterogeneity within our isolates by combining all the detected v-SNPs into a folded site frequency spectrum  of v-SNPs in patients treated with four or more effective drugs and 29.0%  of v-SNPs in patients treated with fewer than four effective drugs were detected at successive time points. We proceeded to estimate the transition probabilities for synonymous and nonsynonymous mutations separately. Synonymous v-SNPs were more likely to be stable than nonsynonymous v-SNPs in patients receiving four or more drugs: 29.7%  and 12.0% , respectively. There was also a slight indication that synonymous mutations may be more likely to emerge within the population of patients receiving four or more effective drugs ugs Fig. . NeitherBy definition, we cannot apply the same temporal dynamics analysis to unstable v-SNPs. However, we can determine whether there are any discrepancies between the observed and expected number of detected synonymous and nonsynonymous mutations present in the population. A tool commonly used to measure this is dN/dS . In its p = 2.35 × 10−5; Fig. p = 1.10 × 10−5; Fig. p = 0.29, Fig. p = 0.44 for patients receiving four or more drugs and p = 0.09 for patients receiving fewer than four drugs).We calculated the pNS for each sample using the observed SNPs. As a first step, we wanted to interrogate the pNS of f-SNPs across all patients. We found that it was significantly lower than expected under a scenario of neutral evolution . While this clone eventually swept to fixation, it had to compete against a second clone carrying a substitution of aspartate to glycine at amino acid position 94 (D94G). Both of these mutations are known to lead to fluoroquinolone resistance [Extensive clinical evidence combined with multiple sequencing studies focusing on treatment failure provide ample examples of positive selection for antibiotic resistance traits from MTBC populations within a single patient , 55, 70.sistance . We wereWe were able to observe evidence of clonal expansion in 5 of our 12 patients , highlighting the existence of a drug pressure threshold that is necessary to suppress the emergence of resistance. If this threshold is not reached, as was the case in our patients receiving fewer than four drugs, the evolutionary constraint on the population is insufficient, allowing bacteria to acquire resistance.We considered three gene sets relating to known adaptation to drug stress. These contained either bona fide drug resistance genes , genes aWe combined very deep DNA sequencing with serial sputum sampling to gain a detailed view of MTBC population structure and dynamics within patients undergoing treatment. The first feature to emerge from our data was the wealth of genetic heterogeneity that is present in the population. Strikingly, the major contributors to genetic diversity were very rare variants that were detected only once across multiple sputum samples obtained from individual patients. There are several possible explanations for this result. The most trivial stems from the sequencing depth and the analytical challenge of distinguishing true minor variants from sequencing errors. While we did not systematically confirm individual v-SNP calls, an important limitation to our analyses, we deliberately chose a combination of stringent criteria to limit the impact of false positive v-SNP calls. Our approach mirrored previously published strategies , 56, andHaving grouped the patients based on the number of effective drugs they received, we were able to leverage the temporal dynamics of minor alleles to investigate the selective forces that shape MTBC diversity. While the fact that the pNS metric has not been extensively characterized calls for a cautious interpretation of our results, we conclude that purifying selection is the main force shaping MTBC populations within patients receiving at least four effective drugs. The combined effect of multiple drugs seems to ensure that resistance to any individual drug poses too much of a fitness disadvantage to be retained within the population. An important consequence of the virtual absence of horizontal gene transfer in MTBC is that all loci are linked; as a result, variants providing only a slight advantage are more likely to be lost from the population since the benefit they provide is probably less than the cost of any linked deleterious mutation. This process is termed background selection , 76 and B. dolosa [Pseudomonas aeruginosa [Helicobacter pylori and Staphylococcus aureus both speak of an ongoing purifying selection, but with a component of diversifying selection, where the continued carriage of these bacteria within the human host is dependent upon their continuous immune escape [The main drivers of the global genetic diversity of MTBC are believed to be the recent demographic expansion of the human population combined with genetic drift and purifying selection , 66. The. dolosa , 78 or Pruginosa , 80. Oppe escape , 82. We e escape , 74, 83.Combination therapy in TB is effective at suppressing drug resistance by restricting the evolutionary options through purifying selection. However, unlike the accepted view that MTBC is genetically stable , we findThe importance of timely drug susceptibility testing and appropriate regimen composition is well established. We would like to put forward the suggestion that, in some cases, susceptibility testing should be done on multiple parallel sputum samples. Our observations regarding the heterogeneity of the composition of sputum samples clearly shows that even three samples obtained within 24 h from each other are not uniform and may result in different resistance profiles. Such information should lead to better treatment and lower probability of resistance expansion. Finally, our findings have clear implications for future clinical trials designed to test the efficacy of novel treatment regimens. We propose that monitoring population dynamics within patients during trials would provide an informative metric for assessing regimen efficacy. Specifically, effective regimens should carry the signature of purifying selection.The study to investigate the range of tuberculosis presentation and treatment  conducted in Henan Provincial Chest Hospital (HPCH) was approved by the HPCH and National Institute of Allergy and Infectious Diseases institutional review boards. The methods of this study were carried out in accordance with the approved guidelines and written informed consent was obtained from the subjects prior to the study. During this study, 52 smear-positive TB patients were enrolled. For these patients, time-serial isolates were collected at seven time points: at enrollment (before treatment) and 2, 4, 6, 8, 16, and 24 weeks after treatment. At each time point for each patient, three sputum samples were collected . As treatment progressed, some patients became both smear- and culture-negative. As a result the number of collected sputum samples varied. Thus, from the 52 patients, we selected 12 that presented more serial isolates and were more representative regarding differential drug susceptibilities/treatment efficacies. We focused specifically on the first 8 weeks of treatment. The isolates of the 12 patients are described in Fig. Each sputum sample was decontaminated and inoculated onto both solid Löwenstein–Jensen (L-J) and liquid mycobacterium growth indicator tube (MGIT) broth. This resulted in three L-J cultures and three MGIT cultures at each time point. Cultures from the early weeks of treatment often had hundreds of colonies on the L-J medium. We scraped the colonies from the slope surface and extracted DNA from the population using the CTAB method as described before  for deepM. tuberculosis CCDC5079 (GenBank CP001641) as a template. We simulated reads using our own read error model and quality profiles with parameters set according to our sequencing platform and strategy. A total of 500 synthetic paired-end sequencing files were simulated, with an average depth of 850.We aimed to evaluate the impact of PCR and sequencing errors as well as the emergence of minor variants during the in vitro expansion of bacterial colonies. We used a two-pronged approach. On the one hand, we picked two single colonies from L-J medium and expanded them in vitro. These colonies came from sputa that were not related to those used for the rest of the study. We extracted the DNA of each single colony as described above and prepared two DNA libraries for each single colony, giving us a total of four samples. Each library was sequenced on an Illumina HiSeq 2000 platform with the same strategy as above. On the other hand we used ART  to simulhttps://github.com/ucdavis-bioinformatics/scythe) and sickle (https://github.com/ucdavis-bioinformatics/sickle) were used for read trimming, bwa [M. tuberculosis H37Rv (GenBank AL123456) [The pipeline we used for fixed mutations and unfixed mutations obtained from all our sequencing runs and read simulations was published before . Brieflying, bwa  was useding, bwa  was usedL123456) , and theL123456) , a BeijiL123456)  and VarSL123456)  to call We performed all data manipulation and analyses using custom scripts written in Python, including specialized packages NumPy, SciPy , pandas https://github.com/swisstph/TBRU_serialTB).With the exception of sequencing data (see accession numbers below), we deposited all the data into a public repository (doi:10.5281/zenodo.322377) and made all the analysis scripts available through a public repository (doi:10.5281/zenodo.345135) and at GitHub  or absence (“Not Detected”) of alleles at a given time as two mutually exclusive and exhaustive states that define a Markov chain Fig. . We grou95%) by taking the 25th and 975th permille values of the sorted estimates. We performed the same analysis on SNPs separated by translational impact (nonsynonymous and synonymous). In addition we repeated the analysis for all SNPs and synonymous and nonsynonymous SNPs using only v-SNPs whose estimated frequency exceeded 1.5%.We used custom Python scripts to randomly re-sample with replacement (bootstrap) the v-SNPs and in each case count the number of occurrences of each type of transition. We then calculated what proportion of the possible outcomes each type represented and thus obtained an estimate of transition probabilities for each state. We performed 1000 iterations of the bootstrap, sorted the estimates and took the 50th percentile value as the estimate of the probability. We defined the 95% confidence interval  or custom Python scripts (f-SNPs). We omitted all SNPs that did not affect a coding sequence.We then generated a codon substitution matrix using a base substitution model that takes into account the proportion of guanine and cytosine in the genome  and the proportion of transitions that occurred at the final position of codons in synonymous f-SNPs  as described previously . BrieflyWe used the following formula to calculate the pNS for each sample:observed represents the number of observed nonsynonymous and SYNobserved represents the number of observed synonymous mutations in the sample. Pr(NSY)i represents the expected probability of a nonsynonymous mutation arising from the i-th codon, which is the same as the i-th codon mutated in the observed samples. n represents the number of all the mutated codons in the sample.where NSYIn order to be able to test the hypothesis that the observed pNS shows a deviation from the null expectation of genetic drift, we simulated the outcome of mutating the same codons under the assumption of random mutagenesis. To this effect we consider the mutation of each codon that was mutated within a sample as a Bernoulli trial with the probability of success given by the expected probability of nonsynonymous mutation calculated earlier. We also assumed that mutations occurred independently of each other. Performing a Bernoulli trial for each mutated codon in the sample generated a dataset that reflected a possible distribution of outcomes in the absence of selection. We used this outcome as the source of “observed” variables for the calculation of pNS in the formula above.We grouped the pNS calculations for our samples and their cognate simulations based on treatment efficacy and performed a Mann–Whitney U-test to determine whether the two sets of pNS results could come from the same population. Finally, we repeated the pNS analysis, simulation, and U-test considering only v-SNPs whose estimated frequency exceeded 1.5%.We focused the excess mutation accumulation analysis exclusively on v-SNPs accumulated after the onset of treatment, therefore ignoring all mutations that were present before treatment began. We considered a given gene set to be excessively mutated if it had accumulated more mutations than we would expect by chance given its nucleotide length. The null expectation is based on the binomial distribution where the probability of success is given as the proportion of the genome covered by the gene set of interest, the number of trials is the total number of mutations detected across the tested samples, and the number of successful trials is the number of mutations that affected the gene set of interest. We split our patients into two groups based on the number of effective drugs in their regimen and then performed a one-tailed binomial test for genes covering different gene sets ; the number of trials was defined as the total number of intragenic mutations within a gene set of interest; and the number of successful trials as the number of nonsynonymous mutations within the same set. We carried out these determinations for both groups of patients and then performed a one-tailed binomial test for genes covering different gene sets.Sequencing reads have been submitted to the EMBL-EBI European Nucleotide Archive (ENA) Sequence Read Archive (SRA) under the study accession number PRJEB13325 and PRJEB17864.Additional file 1:Table S1 Phenotypic drug susceptibility testing for first line drugs. Table S2 Treatment combinations given to patients. Table S3 Average sequencing depth of individual samples. Table S4 Variations in the number of detected f-SNPs in patients at each time point is consistent with v-SNP dynamics. Table S5 Transition probabilities for the detection of v-SNPs. Figure S1 Treatment efficacy in patient groups. Figure S2 Error profiles of simulated reads. Figure S3 Culture-induced minor variants in MTBC. Figure S4 Retained minor variants in our patient population. Figure S5 Demographic trends of MTBC populations within the host are consistent with bacterial killing. Figure S6 Sequencing depth does not bias v-SNP detection. Figure S7 The number of detected v-SNPs for each patient varies over time. Figure S8 Cumulative distributions of allele frequencies. Figure S9 Frequency trajectories of recurrent alleles vary, showing a dynamic and heterogeneous population. Figure S10 Sweeps of v-SNPs occur independently of the type of v-SNP affected the association of v-SNPs with drug resistance. Figure S11 Most patients are infected with strains of the Beijing family. (PDF 532 kb)Supplementary text, figures and tables. In this document we elaborate on the efficacy of treatment (Section 1), expand on the reasoning behind our choice of variant calling approach, describe the outcome of population simulations providing the basis for our demographic expectations, illustrate the phylogeny of our isolates, and finally describe the data contained in the other additional files. The display items in the document include the following tables and figures. Additional file 2:Patient-level data on bacterial populations, including the heterogeneity of the population and the burden of infection. (CSV 1 kb)Additional file 3:v-SNP data. All collected and calculated information for the detected v-SNPs. (CSV 36 kb)Additional file 4:f-SNP data. All collected and calculated information for the detected f-SNPs. (CSV 186 kb)Additional file 5:Published predefined gene sets. These gene sets were used for mutation enrichment analysis (Table


Background: Moose  colonized the North American continent from Asia less than 15,000 years ago, and spread across the boreal forest regions of Canada and the northern United States (US).  Contemporary populations have low genetic diversity, due either to low number of individuals in the original migration (founder effect), and/or subsequent population bottlenecks in North America.  Genetic tests based on informative single nucleotide polymorphism (SNP) markers are helpful in forensic and wildlife conservation activities, but have been difficult to develop for moose, due to the lack of a reference genome assembly and whole genome sequence (WGS) data.Methods:  WGS data were generated for four individual moose from the US states of Alaska, Idaho, Wyoming, and Vermont with minimum and average genome coverage depths of 14- and 19-fold, respectively.  Cattle and sheep reference genomes were used for aligning sequence reads and identifying moose SNPs.Results:  Approximately 11% and 9% of moose WGS reads aligned to cattle and sheep genomes, respectively.  The reads clustered at genomic segments, where sequence identity between these species was greater than 95%.  In these segments, average mapped read depth was approximately 19-fold.  Sets of 46,005 and 36,934 high-confidence SNPs were identified from cattle and sheep comparisons, respectively, with 773 and 552 of those having minor allele frequency of 0.5 and conserved flanking sequences in all three species.  Among the four moose, heterozygosity and allele sharing of SNP genotypes were consistent with decreasing levels of moose genetic diversity from west to east.  A minimum set of 317 SNPs, informative across all four moose, was selected as a resource for future SNP assay design.Conclusions:  All SNPs and associated information are available, without restriction, to support development of SNP-based tests for animal identification, parentage determination, and estimating relatedness in North American moose. Alces alces is the largest member of the Cervidae family, and ranges throughout the circumpolar boreal forests of Eurasia and North America2. The species diverged from the ancestors of domestic cattle and sheep approximately 27 million years ago3. Moose are important ecologically, as a large ungulate with strong ecosystem impacts; economically, due to their value for tourism and hunting; and culturally, as a prominent symbol in many regions4. Consequently, there is active management of moose populations by wildlife agencies throughout their range in North America. However, management is hampered by a lack of genetic tools for monitoring moose, assessing the genetic health of populations, and even detecting illegal harvesting. Moose populations appear to be declining in some regions, including parts of the Upper Midwest of the United States6, and effective management is often dependent on data that are logistically challenging and/or costly to collect.9. Identifying individuals can be as simple as observing their unique color patterns, for example in wild dogs (Lycaon pictus)10. However, this is not practical in species such as moose that have few features with obvious variation between individuals. Moreover, coat color patterns provide little information about genetic relatedness. Association of younger and older animals has been used to infer relationships, for example in swift foxes (Vulpes velox) where pups at a den are presumed to be offspring of the attending parents based on the monogamous behaviors they exhibit. However, detailed parentage studies have revealed multiple paternity within swift fox litters11 and other fox species12. Generally, genetic testing provides a more accurate assignment of parentage and supports unique identification of individuals in the vast majority of instances, as well as an estimation of intra- and inter-population genetic variability.Identifying individual animals and measuring relatedness among and within populations are important for effective wildlife management and conservation efforts16. This form of testing first gained popularity with the development of microsatellite short tandem repeat (STR), and mitochondrial genome markers, concurrent with the development of DNA amplification and sequencing technologies. Approximately 5 to 11 microsatellite markers from cattle, sheep, and caribou (Rangifer tarandus) have been adapted for moose studies22. These studies form the basis of our current understanding of North American moose population structure and genetic diversity. DNA technology developments in the past decade have led to the replacement of microsatellite and mitochondrial genome markers with SNP markers because SNPs are more abundant, have greater stability over generations, are more accurately genotyped, and are amenable to automating the genotyping processes23. Moreover, panels of SNPs broaden the use of genotyping for management and conservation efforts, because they can provide not only identification of individuals and parentage, but also estimation of inbreeding and relatedness, and detection of admixture between populations of wildlife. For example, an SNP-based approach has been used for conservation efforts in endangered species such as the Iberian lynx (Lynx pardinus)24 and Tasmanian devil (Sarcophilus harrisii)25, as well as more common but wide-ranging species like the brown bear (Ursus arctos)26. The application of SNP-based approaches, however, requires first the identification of polymorphisms segregating in the populations being studied, and developing assays that support accurate genotyping. Accordingly, a SNP panel spanning the genome of North American moose would be useful for addressing fundamental questions about population genetics in this species.Genetic testing using DNA markers has been applied to human, livestock, and wildlife studies for many years18, making development of SNP panels challenging. The low diversity has been attributed in the prevailing theory, to a relatively recent  colonization from Asia and subsequent founder effect induced by extended range expansion from an original small group of animals27. However, no definitive evidence has been presented that refutes an alternative hypothesis, that North American moose experienced a severe population bottleneck at some time in the past20, as occurred for North American bison (Bison bison) populations28. Whether a founder effect, bottleneck, or both, the small effective population size simultaneously increases the need for developing genetic tools for management and the challenge of creating SNP marker panels.Low genetic diversity among North American moose populations has been previously reported29, and shown to effectively identify high-confidence SNPs likely to be segregating within the target species. Here we report the whole genome sequencing of four moose genomes, each obtained from distant geographic regions of North America, and the use of the cattle and sheep reference genomes to align the sequence data and identify SNPs likely to be segregating among moose populations. A set of criteria was developed to select the potentially most useful set of moose SNPs, and to identify 317 autosomal variants meeting these criteria. The associated sequence information was made freely available, and represents a resource for developing genotyping assays to support moose genetic research.Discovery of SNPs in a species has generally been preceded by development of its reference genome assembly. Using this assembly, whole genome sequence (WGS) reads from individual animals can be aligned, and differences between segregating alleles identified. However, creation of a reference assembly still represents a significant barrier for most research communities interested in wildlife species. Fortunately, an alternative approach that uses the reference genomes of related species has been developedThis article contains no studies performed with animal subjects, and thus, no additional institutional ethical permits were required. Samples for DNA extraction were donated by private individuals not associated with this research. These were hunters that had legally harvested moose during the firearm hunting season in their state. No additional approvals were needed, since all hunters obtained valid hunting licenses for the harvesting of moose.A. alces based on their location in North America:A. gigas,A. shirasi, andA. americana30. These animals were harvested at four distinct geographic locations . These fragments were used to make an indexed, 500 bp paired-end library according to the manufacturer’s instructions . After construction, indexed libraries were pooled with other indexed samples in groups of four to eight, and sequenced with a massively parallel sequencing machine and high-output kits . After sequencing, the raw reads were filtered to remove adaptor sequences, contaminating dimer sequences, and low-quality reads. Pooled libraries with compatible indexes were repeatedly sequenced until a minimum of 40 Gb of sequence with greater than Q20 quality was collected for each animal. Previous results showed that this level of coverage provided genotype scoring rates and accuracies that exceeded 99%29.DNA was extracted from muscle with a typical phenol:chloroform method and stored at 4°C in 10 mM TrisCl, 1 mM EDTA (pH 8.0) as previously described29. Briefly, FASTQ files corresponding to a minimum of 40 Gb of Q20 sequence were aggregated for each animal. The reference assemblies for bothUMD3.132 andOar_v3.1 were downloaded from the NCBI genomes download site and indexed for use with the Burrows Wheeler aligner (BWA) version 0.7.1233. The fastq files corresponding to R1 and R2 runs for the paired end libraries of each respective animal were aligned individually using the BWA aln algorithm and bovine reference assembly UMD3.1. The R1 and R2 datasets were then merged and collated using BWA sampe. The process was repeated for the mapping of the reads to the ovine Oar_v3.1 reference assembly. The resulting sequence alignment map (SAM) files were converted to binary alignment map (BAM) files, and subsequently sorted using Samtools (version 0.1.18)34. PCR duplicates were marked in the BAM files using the Genome Analysis Toolkit 35. Regions in the mapped dataset that would benefit from realignment due to small insertions and deletions were identified using the GATK module RealignerTargetCreator, and realigned using the module IndelRealigner. The BAM file produced at each of these steps was indexed using Samtools. The resulting indexed BAM files were made available via the Intrepid Bioinformatics genome browserhttp://www.intrepidbio.com/, with groups of animals linked at the USMARC WGS browser . The raw reads were deposited at NCBI BioProject AccessionPRJNA325061. Some SNP variants were identified manually by inspecting the target sequence with Integrative Genomics Viewer (IGV) software version 2.1.2837, as described in previously38. In these cases, read depth, allele count, allele position in the read, and quality score were taken into account when the manual genotype determination was made.The DNA sequence alignment process was similar to that previously reportedThe above mapping efforts produced BAM files for the alignments to both UMD3.1, and Oar_v3.1. The BAM files for all four animals were analyzed simultaneously for variation against both the UMD3.1 and the Oar_v3.1 genomes. The GATK UnifiedGenotyper was used with the genotype mode (-gt_mode) flag set to DISCOVERY, and the likelihood model (-glm) flag was set to BOTH in order to identify both single nucleotide variants, and small insertions and deletions. The maximum number of alternate alleles  flag was set to allow only three. Other than those mentioned, default parameters were used. Samtools was used to generate a pileup file containing the measured allele and depth of coverage at each position for all four animals. Variant sites in the four moose were filtered for having a minimal read depth of ten, and a minimum genotype quality score of 30. The SNPs were filtered for having a minor allele frequency (MAF) of 0.5, with both homozygous genotypes present among four animals. Fifty bases of flanking DNA sequence on either side of the targeted moose SNP were analyzed for nucleotide alleles that were homozygous in all four moose yet different from the cattle or sheep reference sequences. These nucleotide sites were flagged as potential moose “species-specific” alleles and the 101 bp of context sequence was edited to create a moose consensus reference sequence. The 101 bp of moose consensus sequence derived from the alignment of one reference genome was then tested for alignment to the other reference genome. Moose SNPs with MAFs of 0.5, and having been derived independently from alignment to both reference genomes, were manually assigned genome-wide bins based on their chromosome and proximity as inferred by alignment with the cattle genome. The goal of assigning markers to bins was to minimize linkage while allowing automated SNP assay design software the opportunity to select the best candidate marker for each distinct genomic region. All of these conservative filters were intended to maximize marker informativity in North American moose populations, and minimize potential technical difficulties with SNP assay designs that rely on oligonucleotide hybridization for genotype detection.An average of 63.5 Gb total genome sequence was collected for four moose. Based on similar estimates in cattle and sheep, this would correspond to an average read depth of 19-fold coverage if aligned to a moose reference genome of similar quality . HoweverAlignment of moose reads to the cattle and sheep genomes identified approximately 48.3 million and 39.7 million sites that differed from the reference assemblies, respectively. These included SNPs, insertions and deletions, and sites where moose-associated nucleotide differences occurred. The latter sites were defined as having homozygous genotypes in the four moose, with alleles differing from those in cattle or sheep . After sCandidate moose SNPs were further excluded when the flanking sequences in one reference genome were not uniquely identified in the other. This left 773 and 552 highly informative moose SNPs identified in conserved regions of the cattle and sheep genomes, respectively . Of thesThe alignment coordinates of the 1,325 highly informative SNPs were analyzed for genome-wide distribution patterns that may indicate ascertainment biases caused by the variant selection. Overall, the distribution of SNP sites in the sets with 773, 552, and the 317 intersecting markers, appeared to be widespread in the cattle and sheep genomes and generally appropriate for genome-wide estimates . HoweverGenotype analysis for the 773 and 552 moose SNPs derived from the cattle and sheep alignments, respectively, showed that each moose had approximately the same proportion of opposing homozygous genotypes . HoweverPRNP) affects susceptibility to spongiform encephalopathies such as chronic wasting disease in cervids. By manually viewing thePRNP coding sequence with IGV software, a coding SNP with 0.5 MAF and both homozygotes present was identified  and sheep (552 SNPs) reference genome assemblies. The 1,008 moose SNPs were refined to a minimal subset of 317 moose SNPs found in the most highly conserved genome regions. All of these markers are publicly available and ready for validation on a variety of SNP genotyping technology platforms. An important first step in evaluating these SNPs will be characterizing their MAFs in wild populations of North American moose. The online whole genome moose sequences, together with reference genotypes 41 and the Tasmanian devil (S. harrisii)25. A caveat with our results is the overall heterozygosity of each moose may be underestimated due to ascertainment bias for highly informative SNPs in highly conserved genomic regions. In other words, variation in conserved moose genome regions may occur at a lower rate than that in non-conserved regions. In spite of this potential ascertainment bias, the results suggest that combinations of these markers may be useful in detecting population structure.Genotype information from the 1,008 moose SNPs was useful for measuring genome-wide differences in DNA sequence diversity among the four individuals. Measurements of heterozygosity and allele sharing showed that the Alaskan moose was the most diverse, the Vermont moose was the least, with the moose from Idaho and Wyoming being intermediate. This is consistent with a species that crossed the Bering Land Bridge into Alaska and radiated outward from west to east across North America. SNPs have been previously used to estimate genome diversity in other species with low genetic diversity like the European bison , andhttp://www.ars.usda.gov/Research/docs.htm?docid=25712 .In addition, access to the aligned sequences is available via the USDA:http://server1.intrepidbio.com/FeatureBrowser/customlist/record?listid=7919250313 , andhttp://server1.intrepidbio.com/FeatureBrowser/customlist/record?listid=7919250315 .Download access to the BAM files is available at the Intrepid Bioinformatics sites: This study presents SNP discovery and variation of moose in North America, which are very valuable data. First, moose are an understudied species and their genetics can tell us a lot about the phylogeography of species after the LGM. As the authors state, moose in North America have generally low neutral nuclear diversity, and are less diverse than their European counterparts . They are also less diverse than many northern species and many species of cervids, such as caribou, white-tailed deer, and elk. Hypotheses have been proposed for these observations and have been tested with mtDNA, but only recently have nuclear markers been used to study phylogeography in European moose. No such tests have been conducted in North America. This study provides new tools to spur moose research, although I agree with Reviewer 2 that microsatellites and mtDNA still have their uses. The inclusion of 4 individuals in this study is good, as is the broad geographic range of the samples across North America. The methods are appropriate, although as the first reviewer points out, RADSeq could have been used in SNP discovery and has some advantages over the methods used, but I believe that this report on moose is secondary to the authors’ overall goals and we are fortunate that Kalbfleish et al. decided to pursue this and share their findings. The comparison to reference genomes, although from bovids not very closely related to moose, yielded some advantages, including identifying SNPs in important functional genes, such as the PRNP locus. Nonetheless, a white-tailed deer genome was made public in summer 2017 and although these authors may not be willing to start over by comparison of their data to a very similar genome (same subfamily and same number of chromosomes) the prospect exists and should be pursued. I can see the point of Reviewer 2 concerning Fig. 4 and assumed synteny but I believe I got the intended message from the figure as is. But I think mentioning somewhere that North American moose have 34 pairs of autosomes when discussing the success of mapping SNPs to the reference genomes would be appropriate.   I find the last sentence interesting in that the authors believe the SNP variants they found existed prior to moose entering North America. Considering that event likely happened within the last 15,000 years that is a very reasonable statement, and may cause some to think that SNPs may contain little information about geographic variation in moose and all that goes with it. Given the morphological and behavioral differences between, say, Alaskan moose and those in the eastern continent, however, it is obvious that moose have evolved rapidly in that time, despite limited genetic diversity due to Pleistocene bottlenecks and founder effects. How that translates to current SNP diversity and what the latter may be able to tell us about moose are exciting questions to be asked, for which this manuscript sets the stage. Minor comments:nd paragraph of Introduction: should be “among individuals” not “between individuals.” 2 Third paragraph of Introduction: should be “a SNP-based approach” not “an SNP-based approach.” Last paragraph of Introduction, last sentence: should it be “has been made freely available” rather than “was made freely available?”nd-to-last sentence: Should be “previously.” not “in previously.” Last paragraph of WGS Production …, last words of 2 Discussion, last sentence: should this be “SNPs” instead of SNP?”I have read this submission. I believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. This well-written manuscript describes the discovery and characterization of single nucleotide polymorphisms (SNPs) in moose. These SNPs will be valuable for future research and conservation efforts, as they can be used for animal identification, assigning parentage, and estimating intra- and inter-population genetic variability. The SNP discovery and genotyping are done using well-established and clearly described approaches, and care has been taken to avoid false positive SNPs (requiring that both homozygous genotypes be observed for example). The raw sequencing data is available through the SRA database, while the aligned reads (BAM) files are available via the USDA and Intrepid Bioinformatics sites. The final filtered variants are provided with flanking sequence in the supplementary materials. Larger, less-filtered collections of variants are provided in the form of VCF files, again in the supplementary materials. The clear, detailed manuscript and the raw data and progressively filtered results will make is easy for others to reproduce or make use of the results of this work. Minor comments 1. In the last paragraph of the introduction the authors note the challenge of creating a whole genome assembly for use in SNP discovery, and that the use of an existing reference genome from a related species can be an effective alternative. In the discussion section the authors mention that the cross-species mapping approach, however, has the drawback of targeting conserved regions of the genome. I am curious as to why the authors chose not to employ a technique like RADseq for SNP discovery, as it does not depend on the availability of a reference genome and would allow them to better assess minor allele frequency, through the inclusion of more individuals. Also, RADseq would be equally effective at targeting conserved and non-conserved regions. The reasons for not using RADseq  could be addressed in the introduction. 2. The figure legends for Figure 2 and Figure 3 could be expanded slightly to explain to readers not familiar with IGV which elements represent reads, coverage, reference sequence. Also, in Figure 3 it isn't clear to me why the sequence of one read is shown . 3. In the Methods section "fastq" is written as "FASTQ" and "fastq".I have read this submission. I believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. et al. develop a novel set of SNP loci for moose. To do so the authors generated genomic sequence data from 4 moose across their range in the United States, aligned the reads to both cow and sheep reference genomes, and then applied a series of filters to select a subset of loci in highly conserved regions. The resulting set of loci showed a gradient of diversity decreasing from west to east, consistent with hypothesized colonization. The authors state that these loci will serve as a resource for future management and conservation applications. I think that this study has laudable goals and adds a valuable resource for moose conservation. However, there are some issues the analyses and presentation that need to be clarified.In this work Kalbfleischde novo assembly of the moose sequence?de novo assembly and then apply cross-species alignment for scaffolding, e.g. https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-017-1911-6Along these lines there are several recent papers that detail a hybrid procedure for genome construction that begins withThe alignment and filtering procedures seem overly restrictive. Authors note that only ~10% of their reads aligned to the cow and sheep reference genomes (not surprising given the levels of divergence among the species) meaning that 90% of the data was essentially thrown away. Why not start with aIf part of the goals is to eventually use these loci for “population genetic applications”  it strikes me that selecting loci that are in such heavily conserved regions may result in biased estimates. Can the authors comment on this? Doesn’t this procedure result in a lot of “moose specific” variants being missed?I can understand that it is not the objective of this paper to create a draft genome sequence for the moose , but then more justification as to why this was not attempted needs to be given.Did the authors check and make sure that the “highly conserved genomic regions” are not in repetitive elements? A blast search of these regions should do the trick.   Overall commentsIn the sentence in the introduction starting with “DNA technology developments…” saying that SNPs have “replaced” microsatellites and mitochondrial DNA is a gross overstatement. There are many studies still using these markers and there are many applications where these markers may be preferable. Would be better to say something along the lines that SNPs have gained in use.As currently written, the sentence in the introduction starting with “Moreover, panels pf SNPs broaden…” is incorrect. Microsatellites and mitochondrial DNA also provide means of measuring inbreeding and relatedness among individuals. This should be rephrased to state that SNPs may be better at these estimates, likely due to the factors listed in the previous sentence I would couch the statement that previous estimates of low genetic diversity in moose will make it hard to discover SNPs. This may be true, but it has not been tested and estimates of diversity from 5-11 microsatellites likely will not reflect genome-wide patterns.Remove “in the prevailing theory,”et al. (2012)1.  This should be addressed.I find it striking that in the final paragraph of the introduction there is no mention of the plethora of genotype-by-sequencing approaches that are used for SNP discovery and population genetic analyses but do not require a reference genome, e.g Peterson When you state the sub-species names in the Methods refer readers to Table 1 so that they know which occurs where relative to your samples.Which program or programs were used to filter the raw reads?State what “UMD3.1” and “Oar_v3.1” are when you first introduce them in the methodsIs binning relative to the cow genome actually informative for moose? Are the karyotypes comparable? How many chromosomes does the moose have?Along this same line, I think having the variants locations for both UMD3.1 and Oar_v3.1 plotted on top of each other in Figure 4 is misleading. It suggests that the locations are syntenic, which is not true. For instance, the plot implies that no variants were discovered on chromosomes 27, 28, and 29 in the sheep genome, when really this is impossible as sheep only have 27 autosomes. I would suggest re-doing this figure with separate panels for each figure, though see the caveat above.In Figure 5A why are there two bars for each of the “Hom” counts but only one for the “Het”? Specific commentsI have read this submission. I believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above.


Yield improvement is an ever-important objective of wheat breeding. Studying and understanding the phenotypes and genotypes of yield-related traits has potential for genetic improvement of crops.R2) ranging from 2.03 to 12.76%. In comparing allelic variation between founder parents and their derivatives (106) and other cultivars (98) using the 76 associated SNPs, we found that the region 116.0–133.2 cM on chromosome 5A in founder parents and derivatives carried alleles positively influencing kernel weight per spike (KWPS), rarely found in other cultivars.The genotypes of 215 wheat cultivars including 11 founder parents and 106 derivatives were analyzed by the 9 K wheat SNP iSelect assay. A total of 4138 polymorphic single nucleotide polymorphism (SNP) loci were detected on 21 chromosomes, of which 3792 were mapped to single chromosome locations. All genotypes were phenotyped for six yield-related traits including plant height (PH), spike length (SL), spikelet number per spike (SNPS), kernel number per spike (KNPS), kernel weight per spike (KWPS), and thousand kernel weight (TKW) in six irrigated environments. Genome-wide association analysis detected 117 significant associations of 76 SNPs on 15 chromosomes with phenotypic explanation rates  contains supplementary material, which is available to authorized users. Wheat, the most widely grown grain crop providing the food requirements of about 35% of the global population, generates the largest total harvest and is the most traded grain commodity –3. StudySingle nucleotide polymorphisms (SNP), as third-generation molecular markers, are superior in automated genotyping –6. ThereGS3) and weight (GW5) associated with grain quality in rice by genome-wide SNP scanning and high-density genetic maps. Cormier et al. [In addition to genetic mapping SNP markers have unique advantages for genome-wide association studies (GWAS) of yield-related traits in cereal crops, including rice , barley r et al.  investigr et al.  scanned Analysis of the breeding history of many crop species revealed the presence and roles of founder parents. Molecular markers were used to analyze the contributions of the genetic bases of founder parents in improvement of barley , sugarcaIn this study we genotyped 215 wheat cultivars using the iSelect 9 K SNP array, including 11 founder parents and 106 derivatives. Based on multi-environmental trial data we used GWAS to identify favorable alleles of yield-related traits through sequential generations of breeding. Favorable alleles identified in derivatives could be used to detect important chromosome regions inherited from the founder parents. This information might be used for marker-assisted selection (MAS) in wheat breeding.P < 0.01; Additional file The average coefficients of variation for phenotypic traits in each environment ranged from 6.29 to 26.35%, indicating considerable phenotypic variation Table . There wThe founder parents Funo, Bima 4, and Nanda 2419 and their derivatives over following generations were compared in terms of yield-related traits, including plant height (PH), spike length (SL), spikelet number per spike (SNPS), kernel number per spike (KNPS), kernel weight per spike (KWPS), and thousand kernel weight (TKW). PH gradually declined and TKW increased with advancing generations, while SL, SNPS, KNPS, and KWPS showed no significant changes. This indicated continuing selective pressure on PH and TKW during breeding  was plotted against the ΔK calculated from the Structure, and the peak of the broken line graph was observed at K = 2 Fig. . The neiP < 3.06 × 10− 4) among the 76 associated SNP loci, including 20, 35, 6, 23, 24, and 9 signals associated with PH, SL, SNPS, KNPS, KWPS, and TKW, respectively  ranged from 2.03 to 12.76%. The associated loci were located on 15 chromosomes (Table wsnp_Ex_c49211_53875575-5A (SL) was significantly associated in all six environments, whereas others were significant in two to five environments , wsnp_Ex_c1630_3105100-5BAA (1.34 cm in 10YL), wsnp_Ex_c7713_13153321-6BCC (1.48 cm in 09YL); wsnp_Ex_c13953_21831752-4ACC (increases in KNPS by 4.27 in 09YZ and 3.45 in 10YL); wsnp_Ex_c19467_28423197-6BAA (increases in TKW by 0.26 g in 09YL); and wsnp_Ku_rep_c69511_68887456-3ATT , respectively. The frequencies of these alleles at associated loci ranged from 6.05 to 97.21%, and exceeded 50% for 64 alleles, indicating strong selection on those alleles in breeding.The phenotypic effects of alleles were further analyzed Table . FavorabAll 76 alleles with a positive effect on yield-related traits identified in the association analysis were used to analyze the transmission rates of alleles from founder parents to progenies, as well as the frequencies of favorable alleles in later generations. Transmission rates from the first generation of Funo to the fifth generation were between 81.88 and 65.48%, and the frequencies of favorable alleles in different generations changed from 71.99 to 78.21%. Transmission rates from the first generation of Bima 4 to the fourth generation were between 79.94 and 64.38%, and frequencies of favorable alleles increased from 74.79 to 79.49%. Likewise, transmission rates for first to fifth generation derivatives of Nanda 2419 were between 64.25 and 50.72%, while the corresponding frequencies of favorable alleles increased from 68.91 to 78.21%  including Funo, Nanda 2419, and their derived offspring, while the second  included Bima 4 and many of its derived offspring. Pedigree analysis showed that the first generation derivatives of Funo Sumai 2 and Sumai 3, as well as the second generation Ning’ai 8628 and Wu 7815–4-1, clustered together. Moreover, the first generation of Funo derivatives obtained from a cross with Neixiang 5 , and the second generation obtained from crosses involving Zhengzhou 17  were also in the same cluster. Thus, different cultivars from the same original cross had high similarity, indicating little genetic differences in the traits analyzed. Moreover, among different clusters, genetically related lines mostly grouped in the same cluster, indicating the results were consistent with the genealogy  in two growing seasons (2008–2009 and 2009–2010). Field management followed local practices. The six irrigated environments were designated 09TA, 10TA, 09YL, 10YL, 09YZ and 10YZ. Field experiments were grown in randomized block designs with three replications. Each line was planted in 2 m, 5 row plots at 40 kernels per row, with a row spacing of 20 cm. The agronomic traits PH and TKW were measured at maturity. Thirty spikes of each line were randomly collected from the middle row and used for measurements of SL, SNPS, KNPS, and KWPS.http://www.brothersoft.com/ibm-spss-statistics-469577.html). The best linear unbiased prediction (BLUP) method was used to calculate the mean values of each trait [Genomic DNA extraction was carried out using the CTAB method . Descripch trait –37.http://www.maizegenetics.net), GWAS was performed using the yield-related traits and SNP marker data. After exclusion of SNP loci with frequencies < 0.05, a uniform suggestive genome-wide significance threshold  was given.SNP genotyping was performed on the BeadStation and iScan instruments and conducted at the Genome Center of the University of California at Davis according to the manufacturer’s protocols  . Data coThe 215 wheat cultivars were grouped by the neighbor-joining method in MEGA 5.0 . The traAdditional file 1: Table S1.Pearson’s correlation coefficients between phenotypic traits in different environments. (XLSX 10 kb)Additional file 2: Table S2.Descriptive statistics (Means ± SD) of six phenotypic traits in three founder parents and their derivatives in different environments. (XLSX 20 kb)Additional file 3: Table S3.Allelic number, MAF and PIC of 3792 SNP markers detected in this study. (XLSX 304 kb)Additional file 4: Table S4.Information for 215 wheat accessions used in this study. (XLSX 24 kb)Additional file 5: Table S5.Significant MTAs identified in current and previous study. (XLSX 13 kb)Additional file 6: Figure S1.The pedigree sketch of wheat varieties cultivated in large scale and their founder genotypes. (TIFF 6380 kb)


Fragaria and Prunus genomes revealed clusters of associated markers indicating five genomic regions associated with the total anthocyanin content and two large clusters associated with the carotenoid content. Among the marker clusters associated with the phenotypes, we found several candidate genes with known functions in either the anthocyanin or the carotenoid biosynthesis pathways. Among others, we identified a glutathione-S-transferase, 4CL, an auxin response factor and F3'H as candidate genes affecting anthocyanin concentration, and CCD4 and Zeaxanthine epoxidase as candidates affecting the concentration of carotenoids. These markers are starting points for future validation experiments in independent populations as well as for functional genomic studies to identify the causal factors for the observed color phenotypes. Furthermore, validated markers may be interesting tools for marker-assisted selection in commercial breeding programmes in that they provide the tools to identify superior parental combinations that combine several associated markers in higher dosages.Petal color is one of the key characteristics determining the attractiveness and therefore the commercial value of an ornamental crop. Here, we present the first genome-wide association study for the important ornamental crop rose, focusing on the anthocyanin and carotenoid contents in petals of 96 diverse tetraploid garden rose genotypes. Cultivated roses display a vast phenotypic and genetic diversity and are therefore ideal targets for association genetics. For marker analysis, we used a recently designed Axiom SNP chip comprising 68,000 SNPs with additionally 281 SSRs, 400 AFLPs and 246 markers from candidate genes. An analysis of the structure of the rose population revealed three subpopulations with most of the genetic variation between individual genotypes rather than between clusters and with a high average proportion of heterozygous loci. The mapping of markers significantly associated with anthocyanin and carotenoid content to the related  Rosa comprises a vast amount of genetic resources represented by more than 100 wild species as well as more than 30,000 mostly tetraploid varieties bred for ornamental purposes . Few studies cover polyploids, and very few GWAS have been performed on highly heterozygous polyploids, such as potato  in the spring of 2012. A second collection of cultivars was maintained under semi-controlled conditions as potted plants in three randomized blocks in a greenhouse . The plants were initially cultivated in 3-l pots and then transferred to 7-l pots with the fertilized substrate Einheitserde T  under natural light with a day and night temperature of 22° ± 5°C.An association panel of 96 rose cultivars with code numbers from 1 to 141  was used for the present study. Most of the cultivars were commercially available or provided by German rose-breeding companies Table . Based o525nm > 1.0), the anthocyanin extracts were diluted with the extraction solvent. Each clone was measured three times, and the overall mean was calculated for each cultivar. The anthocyanin content was recorded and evaluated in two environments: (i) in the field at Herrenhausen and (ii) in the greenhouse at the Federal Plant Variety Office, Hannover. The absorbance values were not used to calculate the levels of compounds, as this measurement involves variable mixtures of anthocyanidins.Flowers were always sampled from 8 to 12 a.m. Opened buds at flower development stage 3   was performed using DARwin 5.0.158 . An unweighted neighbor-joining  in TASSEL 3.0 . The data that were not normally distributed were transformed using log- and Box-Cox transformation Wessa, . The staK = 1 to K = 10. The optimum number of K can be identified according to the maximum value of LnP(D)  and the number of AFLP, microsatellite and candidate gene markers (575 and 927) for 5 Figure . Using t5 Figure  in two i3 Figure . The resK = 3 is visualized in Figure The structure of the population at The kinship estimates based on SNPs Figure  indicateThe heterozygosity was determined based on the SNP data without considering the dosage of the markers . When defined this way, the percentage of heterozygous loci is identical to the percentage polymorphic loci. On average, varieties displayed 55.2% of heterozygous loci ranging from 27% for variety No. 105 and 66.9% for variety No. 2. No correlation between heterozygosity and the age of the variety was observed Figure .On the other hand, there were significant differences between ground cover, climber, bedding roses, Hybrid teas and shrub roses, both in the average level of heterozygosity and in the variation in heterozygosity within groups Figure . Groundc525nm = 0.35 to E525nm = 33.22 in the greenhouse and from E525nm = 0.33 to E525nm = 38.39 in the field. The distribution was skewed to the left . In roses classified as orange, a balanced occurrence of anthocyanins and carotenoids was always measured. However, there was no overall correlation between the anthocyanin and carotenoid content in the rose petals .Carotenoids in the rose petal extracts were measured by their characteristic absorbance at 442 nm Figure  from allMarker-trait associations were calculated in Tassel 3.0 with 39,831 SNPs based on their diploid allelic state . The tetraploid dosage of the SNP was not used for calculation because TASSEL 3.0 can only handle haploid and diploid genotypic data .525nm) and was significantly higher (p = 5.03E-8) than 1.93 (E525nm) for the homozygous B:B genotypes. For the 3-ß-OH-steroid-dehydrogenase SNP, the difference between the mean of the two groups  was also significant (p = 8.56E-7).The anthocyanin content in greenhouse-grown roses was significantly associated with 17 SNP markers, five of which were also associated with the anthocyanin content from field-grown roses Table . These SRosa sp. is not complete, the contigs of 133 SNPs with hits just below the Bonferroni threshold plus the 17 significant SNPs were blasted against the closely related genomes of Fragaria vesca and Prunus persica and mapped on these genomes . A blast of the contigs located three of the SNP markers in the coding region of anthocyanin biosynthesis genes, 4-coumarate-ligase (4_CL), flavonoid 3′-hydroxylase (F3′H) and glutathione-S-transferase (GST). The positions of these genes of the anthocyanin biosynthesis pathway and of further transcription factors are shown in the genome plots as green dots . The putative transcription factors that are associated with anthocyanin accumulation include a ubiquitin-like protein SMT3 (SUMO1), WRKY transcription factor 17 and UTP4/Cirhin, a WD40 repeat protein . Most of the significant SNPs formed two large clusters in linkage group 5 of F. vesca and P. persica with more than 250 SNPs . They may be located on a part of the chromosome with low recombination. Two of the SNPs were located on contigs encoding genes of the MEP (methylerythritol 4-phosphate) pathway, CMS (2-c-methyl-d-erythritol-cyclodiphosphatase) and DXR (1-deoxy-d-xylulose 5-phosphate reducto-isomerase). A third enzyme, more upstream in the carotenoid biosynthesis, is Zeaxanthin epoxidase . It was located in linkage groups Fvb1 and Pp07. ZEP is a part of the branch of the ß-carotenoid biosynthesis that catalyzes the step from zeaxanthin to violaxanthin. Additionally, several significant SNPs were mapped to linkage group four of F. vesca and to linkage group one of P. persica. The positions of these SNPs were close to the assumed position of a carotenoid cleavage dioxygenase gene, CCD4. Other significant SNPs were located in two cytochrome P450 monooxygenases (CP450): cytochrome P450_71A24  and cytochrome CP450_CYP749A22 .Because as many as 351 SNPs were significantly associated with the accumulation of carotenoids in rose petals and surpassed the Bonferroni threshold of α = 2.62e-6 Table , the eff442nm), whereas the mean for the A:B genotypes was 0.325 (E442nm). The effect of the SNP for CMS, the subsequent gene after DXR in the carotenoid pathway, was 0.206 (E442nm), and the mean was also obviously higher in the A:B group .The effects of SNPs on the carotenoid biosynthesis genes CMS and DXR are shown as box plots in Figure Floral traits in ornamental roses are determined by a number of quantitative traits, e.g., petal number, flower size and a large number of secondary metabolites that constitute flower color and flower fragrance. Here, we applied GWAS based on the rose WagRhSNP array to analyse factors influencing the amount of anthocyanins and carotenoids in petals. In this study, we tried for the first time to utilize association genetics to exploit the vast phenotypic variation in mainly tetraploid cultivated roses for an analysis of quantitative traits.The large number of markers used to genotype the association panel revealed a high average heterozygosity of 55.2% in the population, which is in agreement with previous studies on marker diversity in cultivated roses , and UTP4/Cirhin, a WD40 repeat protein , also known as “Production of Anthocyanin Pigment 2” (PAP2), was identified on Fragaria chromosome 6 and is a member of the MBW-complex  and dimethylallyl diphosphate (DMAPP) via the MEP pathway. Enzymes of the MEP pathway significantly influence carotenoid production Lois, , 2006. WFragaria chromosome 4 and Prunus chromosome 1, close to a CCD4 gene  to 351 (carotenoids) trait-marker associations. Some of these had large effect sizes: these QTLs may be useful in breeding for intense flower colors in that parental breeding lines with combinations of several markers with high SNP dosages (duplex to quadruplex) might now be selected using the validated SNPs.This is the first association mapping study in rose. We focussed on the anthocyanin and carotenoid contents, which largely determine petal color. The phenotype data were collected in the field and in the greenhouse, and the overall levels of these compounds were not influenced by the differences in environment. To analyse the GWAS-associated SNPs in the absence of a rose genome sequence, we mapped the underlying rose contigs to the genome sequence of the related species DS conducted the experiments, did the statistical analysis and wrote most parts of the manuscript. RS conducted parts of the experiments. RV conducted part of the data analysis and contributed in writing the manuscript. MS contributed some of the data and contributed in writing the manuscript. ML contributed to the experimental setup, contributed data to some of the experiments and contributed in writing the manuscript. TD was involved in planning the experiments, in statistical analysis, and wrote parts of the manuscript.The research was in part funded within the program “Zentrales Innovationsprgramm Mittelstand (ZIM) of the German Bundesministerium für Wirtschaft und Energie (BMWi).”The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.