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The US PulseNet and GenomeTrakr laboratory networks work together within the Genomics for Food Safety (Gen-FS) consortium to collect and analyze genomic data for foodborne pathogen surveillance (species include Salmonella enterica, Listeria monocytogenes, Escherichia coli (STECs), and Campylobactor). In 2017 these two laboratory networks started harmonizing their respective proficiency test exercises, agreeing on distributing a single strain-set and following the same standard operating procedure (SOP) for genomic data collection, running a jointly coordinated annual proficiency test exercise. In this data release we are publishing the reference genomes and raw data submissions for the 2017 and 2018 proficiency test exercises.

A total of 91 draft genome sequences were used to analyze isolates of Salmonella enterica serovar Enteritidis obtained from feral mice caught on poultry farms in Pennsylvania. One objective was to find mutations disrupting open reading frames (ORFs) and another was to determine if ORF-disruptive mutations were present in isolates obtained from other sources. A total of 83 mice were obtained between 1995-1998. Isolates separated into two genomic clades and 12 subgroups due to 742 mutations. Nineteen ORF-disruptive mutations were found, and in addition, bigA had exceptional heterogeneity requiring additional evaluation. The TRAMS algorithm detected only 6 ORF disruptions. The sefD mutation was the most frequently encountered mutation and it was prevalent in human, poultry, environmental and mouse isolates. These results confirm previous assessments of the mouse as a rich source of Salmonella enterica serovar Enteritidis that varies in genotype and phenotype., (Published by Elsevier Inc.)

Foodborne pathogen surveillance in the United States is transitioning from strain identification using restriction digest technology (pulsed-field gel electrophoresis [PFGE]) to shotgun sequencing of the entire genome (whole-genome sequencing [WGS]). WGS requires a new suite of analysis tools, some of which have long histories in academia but are new to the field of public health and regulatory decision making. Although the general workflow is fairly standard for collecting and analyzing WGS data for disease surveillance, there are a number of differences in how the data are collected and analyzed across public health agencies, both nationally and internationally. This impedes collaborative public health efforts, so national and international efforts are underway to enable direct comparison of these different analysis methods. Ultimately, the harmonization efforts will allow the (mutually trusted and understood) production and analysis of WGS data by labs and agencies worldwide, thus improving outbreak response capabilities globally. This review provides a historical perspective on the use of WGS for pathogen tracking and summarizes the efforts underway to ensure the major steps in phylogenomic pipelines used for pathogen disease surveillance can be readily validated. The tools for doing this will ensure that the results produced are sound, reproducible, and comparable across different analytic approaches.

Whole-genome sequence (WGS) analysis has revolutionized the food safety industry by enabling high-resolution typing of foodborne bacteria. Higher resolving power allows investigators to identify origins of contamination during illness outbreaks and regulatory activities quickly and accurately. Government agencies and industry stakeholders worldwide are now analyzing WGS data routinely. Although researchers have published many studies that assess the efficacy of WGS data analysis for source attribution, guidance for interpreting WGS analyses is lacking. Here, we provide the framework for interpreting WGS analyses used by the Food and Drug Administration's Center for Food Safety and Applied Nutrition (CFSAN). We based this framework on the experiences of CFSAN investigators, collaborations and interactions with government and industry partners, and evaluation of the published literature. A fundamental question for investigators is whether two or more bacteria arose from the same source of contamination. Analysts often count the numbers of nucleotide differences [single-nucleotide polymorphisms (SNPs)] between two or more genome sequences to measure genetic distances. However, using SNP thresholds alone to assess whether bacteria originated from the same source can be misleading. Bacteria that are isolated from food, environmental, or clinical samples are representatives of bacterial populations. These populations are subject to evolutionary forces that can change genome sequences. Therefore, interpreting WGS analyses of foodborne bacteria requires a more sophisticated approach. Here, we present a framework for interpreting WGS analyses that combines SNP counts with phylogenetic tree topologies and bootstrap support. We also clarify the roles of WGS, epidemiological, traceback, and other evidence in forming the conclusions of investigations. Finally, we present examples that illustrate the application of this framework to real-world situations.

Pathogen monitoring is becoming more precise as sequencing technologies become more affordable and accessible worldwide. This transition is especially apparent in the field of food safety, which has demonstrated how whole-genome sequencing (WGS) can be used on a global scale to protect public health. GenomeTrakr coordinates the WGS performed by public-health agencies and other partners by providing a public database with real-time cluster analysis for foodborne pathogen surveillance. Because WGS is being used to support enforcement decisions, it is essential to have confidence in the quality of the data being used and the downstream data analyses that guide these decisions. Routine proficiency tests, such as the one described here, have an important role in ensuring the validity of both data and procedures. In 2015, the GenomeTrakr proficiency test distributed eight isolates of common foodborne pathogens to participating laboratories, who were required to follow a specific protocol for performing WGS. Resulting sequence data were evaluated for several metrics, including proper labelling, sequence quality and new single nucleotide polymorphisms (SNPs). Illumina MiSeq sequence data collected for the same set of strains across 21 different laboratories exhibited high reproducibility, while revealing a narrow range of technical and biological variance. The numbers of SNPs reported for sequencing runs of the same isolates across multiple laboratories support the robustness of our cluster analysis pipeline in that each individual isolate cultured and resequenced multiple times in multiple places are all easily identifiable as originating from the same source.

The adoption of whole-genome sequencing within the public health realm for molecular characterization of bacterial pathogens has been followed by an increased emphasis on real-time detection of emerging outbreaks (e.g., food-borne Salmonellosis). In turn, large databases of whole-genome sequence data are being populated. These databases currently contain tens of thousands of samples and are expected to grow to hundreds of thousands within a few years. For these databases to be of optimal use one must be able to quickly interrogate them to accurately determine the genetic distances among a set of samples. Being able to do so is challenging due to both biological (evolutionary diverse samples) and computational (petabytes of sequence data) issues. We evaluated seven measures of genetic distance, which were estimated from either k-mer profiles (Jaccard, Euclidean, Manhattan, Mash Jaccard, and Mash distances) or nucleotide sites (NUCmer and an extended multi-locus sequence typing (MLST) scheme). When analyzing empirical data (whole-genome sequence data from 18,997 Salmonella isolates) there are features (e.g., genomic, assembly, and contamination) that cause distances inferred from k-mer profiles, which treat absent data as informative, to fail to accurately capture the distance between samples when compared to distances inferred from differences in nucleotide sites. Thus, site-based distances, like NUCmer and extended MLST, are superior in performance, but accessing the computing resources necessary to perform them may be challenging when analyzing large databases., Competing Interests: The authors have declared that no competing interests exist.

Ribavirin is a pharmaceutical antiviral used for the treatment of RNA virus infections including norovirus, hepatitis C virus, hepatitis E virus, Lassa virus, respiratory syncytial virus, and rhinovirus. Despite the drug's history and documented efficacy, the antiviral mechanism of Ribavirin remains unclear. Mechanisms proposed include depletion of the intracellular GTP pool, immunomodulatory effects, induction of error catastrophe, inhibition of viral polymerase activity, and/or inhibition of viral capping. In the present study, we leveraged deep sequencing data to demonstrate that Ribavirin increases murine norovirus (MNV-1) viral diversity. By serial passaging MNV-1 in RAW 264.7 cells for twenty generations in the presence of Ribavirin, we demonstrated statistically significant increases in both the number of unique haplotypes and the average pairwise difference (APD). Based on statistically significant differences in the probability of nucleotide mutations based on Roche 454 sequencing, we also demonstrated that single nucleotide substitutions are increased in the presence of Ribavirin. Finally, we demonstrated Ribavirin's impact on statistically significantly reducing the relative proportion of the dominant sequence within the quasispecies., (Copyright © 2015 Elsevier B.V. All rights reserved.)

Background: Mosaic somatic alterations are present in all multi-cellular organisms, but the physiological effects of low-level mosaicism are largely unknown. Most mosaic alterations remain undetectable with current analytical approaches, although the presence of such alterations is increasingly implicated as causative for disease., Results: Here, we present the Parent-of-Origin-based Detection (POD) method for chromosomal abnormality detection in trio-based SNP microarray data. Our software implementation, triPOD, was benchmarked using a simulated dataset, outperformed comparable software for sensitivity of abnormality detection, and displayed substantial improvement in the detection of low-level mosaicism while maintaining comparable specificity. Examples of low-level mosaic abnormalities from a large autism dataset demonstrate the benefits of the increased sensitivity provided by triPOD. The triPOD analyses showed robustness across multiple types of Illumina microarray chips. Two large, clinically-relevant datasets were characterized and compared., Conclusions: Our method and software provide a significant advancement in the ability to detect low-level mosaic abnormalities, thereby opening new avenues for research into the implications of mosaicism in pathogenic and non-pathogenic processes.

Background: The Sturge-Weber syndrome is a sporadic congenital neurocutaneous disorder characterized by a port-wine stain affecting the skin in the distribution of the ophthalmic branch of the trigeminal nerve, abnormal capillary venous vessels in the leptomeninges of the brain and choroid, glaucoma, seizures, stroke, and intellectual disability. It has been hypothesized that somatic mosaic mutations disrupting vascular development cause both the Sturge-Weber syndrome and port-wine stains, and the severity and extent of presentation are determined by the developmental time point at which the mutations occurred. To date, no such mutation has been identified., Methods: We performed whole-genome sequencing of DNA from paired samples of visibly affected and normal tissue from 3 persons with the Sturge-Weber syndrome. We tested for the presence of a somatic mosaic mutation in 97 samples from 50 persons with the Sturge-Weber syndrome, a port-wine stain, or neither (controls), using amplicon sequencing and SNaPshot assays, and investigated the effects of the mutation on downstream signaling, using phosphorylation-specific antibodies for relevant effectors and a luciferase reporter assay., Results: We identified a nonsynonymous single-nucleotide variant (c.548G→A, p.Arg183Gln) in GNAQ in samples of affected tissue from 88% of the participants (23 of 26) with the Sturge-Weber syndrome and from 92% of the participants (12 of 13) with apparently nonsyndromic port-wine stains, but not in any of the samples of affected tissue from 4 participants with an unrelated cerebrovascular malformation or in any of the samples from the 6 controls. The prevalence of the mutant allele in affected tissues ranged from 1.0 to 18.1%. Extracellular signal-regulated kinase activity was modestly increased during transgenic expression of mutant Gαq., Conclusions: The Sturge-Weber syndrome and port-wine stains are caused by a somatic activating mutation in GNAQ. This finding confirms a long-standing hypothesis. (Funded by the National Institutes of Health and Hunter's Dream for a Cure Foundation.).

Tens of thousands of lymphoblastoid cell lines (LCLs) have been established by the research community, providing nearly unlimited source material from samples of interest. LCLs are used to address questions in population genomics, mechanisms of disease, and pharmacogenomics. Thus, it is of fundamental importance to define the extent of chromosomal variation in LCLs. We measured variation in genotype and copy number in multiple LCLs derived from peripheral blood mononuclear cells (PBMCs) of single individuals as well as two comparison groups: (1) three types of differentiated cell lines (DCLs) and (2) triplicate HapMap samples. We then validated and extended our findings using data from a large study consisting of samples from blood or LCLs. We observed high concordances between genotypes and copy number estimates within all sample groups. While the genotypes of LCLs tended to faithfully reflect the genotypes of PBMCs, 13.7% (4 of 29) of immortalized cell lines harbored mosaic regions greater than 20 megabases, which were not present in PBMCs, DCLs, or HapMap replicate samples. We created a list of putative LCL-specific changes (affecting regions such as immunoglobulin loci) that is available as a community resource., (© 2012 Wiley Periodicals, Inc.)

A set of Centre d'Étude du Polymorphisme Humain (CEPH) cell lines serves as a large reference collection that has been widely used as a benchmark for allele frequencies in the analysis of genetic variants, to create linkage maps of the human genome, to study the genetics of gene expression, to provide samples to the HapMap and 1000 Genomes projects, and for a variety of other applications. An explicit feature of the CEPH collection is that these multigenerational families represent reference panels of known relatedness, consisting mostly of three-generation pedigrees with large sibships, two parents, and grandparents. We applied identity-by-state (IBS) and identity-by-descent (IBD) methods to high-density genotype data from 186 CEPH individuals in 13 families. We identified unexpected relatedness between nominally unrelated grandparents both within and between pedigrees. For one pair, the estimated Cotterman coefficient of relatedness k1 exceeded 0.2, consistent with one-eighth sharing (eg, first-cousins). Unexpectedly, significant IBD2 values were discovered in both second-degree and parent-child relationships. These were accompanied by regions of homozygosity in the offspring, which corresponded to blocks lacking IBS0 in purportedly unrelated parents, consistent with inbreeding. Our findings support and extend a 1999 report, based on the use of short tandem-repeat polymorphisms, that several CEPH families had regions of homozygosity consistent with autozygosity. We benchmarked our IBD approach (called kcoeff) against both RELPAIR and PREST software packages. Our findings may affect the interpretation of previous studies and the design of future studies that rely on the CEPH resource.

Correct annotation of the genetic relationships between samples is essential for population genomic studies, which could be biased by errors or omissions. To this end, we used identity-by-state (IBS) and identity-by-descent (IBD) methods to assess genetic relatedness of individuals within HapMap phase III data. We analyzed data from 1,397 individuals across 11 ethnic populations. Our results support previous studies (Pemberton et al., 2010; Kyriazopoulou-Panagiotopoulou et al., 2011) assessing unknown relatedness present within this population. Additionally, we present evidence for 1,657 novel pairwise relationships across 9 populations. Surprisingly, significant Cotterman's coefficients of relatedness K1 (IBD1) values were detected between pairs of known parents. Furthermore, significant K2 (IBD2) values were detected in 32 previously annotated parent-child relationships. Consistent with a hypothesis of inbreeding, regions of homozygosity (ROH) were identified in the offspring of related parents, of which a subset overlapped those reported in previous studies (Gibson et al. 2010; Johnson et al. 2011). In total, we inferred 28 inbred individuals with ROH that overlapped areas of relatedness between the parents and/or IBD2 sharing at a different genomic locus between a child and a parent. Finally, 8 previously annotated parent-child relationships had unexpected K0 (IBD0) values (resulting from a chromosomal abnormality or genotype error), and 10 previously annotated second-degree relationships along with 38 other novel pairwise relationships had unexpected IBD2 (indicating two separate paths of recent ancestry). These newly described types of relatedness may impact the outcome of previous studies and should inform the design of future studies relying on the HapMap Phase III resource.

It is an assumption of large, population-based datasets that samples are annotated accurately whether they correspond to known relationships or unrelated individuals. These annotations are key for a broad range of genetics applications. While many methods are available to assess relatedness that involve estimates of identity-by-descent (IBD) and/or identity-by-state (IBS) allele-sharing proportions, we developed a novel approach that estimates IBD0, 1, and 2 based on observed IBS within windows. When combined with genome-wide IBS information, it provides an intuitive and practical graphical approach with the capacity to analyze datasets with thousands of samples without prior information about relatedness between individuals or haplotypes. We applied the method to a commonly used Human Variation Panel consisting of 400 nominally unrelated individuals. Surprisingly, we identified identical, parent-child, and full-sibling relationships and reconstructed pedigrees. In two instances non-sibling pairs of individuals in these pedigrees had unexpected IBD2 levels, as well as multiple regions of homozygosity, implying inbreeding. This combined method allowed us to distinguish related individuals from those having atypical heterozygosity rates and determine which individuals were outliers with respect to their designated population. Additionally, it becomes increasingly difficult to identify distant relatedness using genome-wide IBS methods alone. However, our IBD method further identified distant relatedness between individuals within populations, supported by the presence of megabase-scale regions lacking IBS0 across individual chromosomes. We benchmarked our approach against the hidden Markov model of a leading software package (PLINK), showing improved calling of distantly related individuals, and we validated it using a known pedigree from a clinical study. The application of this approach could improve genome-wide association, linkage, heterozygosity, and other population genomics studies that rely on SNP genotype data., Competing Interests: TJD is an employee and stockholder of Partek. GH is an employee of Partek.