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Background The frequency of a variant in the general population is a key criterion used in the clinical interpretation of sequence variants. With certain exceptions, such as founder mutations, the rarity of a variant is a prerequisite for pathogenicity. However, defining the threshold at which a variant should be considered “too common” is challenging and therefore diagnostic laboratories have typically set conservative allele frequency thresholds. Methods Recent publications of large population sequencing data, such as the Exome Aggregation Consortium (ExAC) database, provide an opportunity to characterize with accuracy and precision the frequency distributions of very rare disease-causing alleles. Allele frequencies of pathogenic variants in ClinVar, as well as variants expected to be pathogenic through the nonsense-mediated decay (NMD) pathway, were analyzed to study the burden of pathogenic variants in 79 genes of clinical importance. Results Of 1364 BRCA1 and BRCA2 variants that are well characterized as pathogenic or that are expected to lead to NMD, 1350 variants had an allele frequency of less than 0.0025%. The remaining 14 variants were previously published founder mutations. Importantly, we observed no difference in the distributions of pathogenic variants expected to be lead to NMD compared to those that are not. Therefore, we expanded the analysis to examine the distributions of NMD expected variants in 77 additional genes. These 77 genes were selected to represent a broad set of clinical areas, modes of inheritance, and penetrance. Among these variants, most (97.3%) had an allele frequency of less than 0.01%. Furthermore, pathogenic variants with allele frequencies greater than 0.01% were well characterized in publications and included many founder mutations. Conclusions The observations made in this study suggest that, with certain caveats, a very low allele frequency threshold can be adopted to more accurately interpret sequence variants.

The transcriptome and proteome change dynamically as cells respond to environmental stress; however, prior proteomic studies reported poor correlation between mRNA and protein, rendering their relationships unclear. To address this, we combined high mass accuracy mass spectrometry with isobaric tagging to quantify dynamic changes in ∼2500 Saccharomyces cerevisiae proteins, in biological triplicate and with paired mRNA samples, as cells acclimated to high osmolarity. Surprisingly, while transcript induction correlated extremely well with protein increase, transcript reduction produced little to no change in the corresponding proteins. We constructed a mathematical model of dynamic protein changes and propose that the lack of protein reduction is explained by cell‐division arrest, while transcript reduction supports redistribution of translational machinery. Furthermore, the transient ‘burst’ of mRNA induction after stress serves to accelerate change in the corresponding protein levels. We identified several classes of post‐transcriptional regulation, but show that most of the variance in protein changes is explained by mRNA. Our results present a picture of the coordinated physiological responses at the levels of mRNA, protein, protein‐synthetic capacity, and cellular growth. By characterizing dynamic changes in yeast protein abundance following osmotic shock, this study shows that the correlation between protein and mRNA differs for transcripts that increase versus decrease in abundance, and reveals physiological reasons for these differences. Synopsis By characterizing dynamic changes in yeast protein abundance following osmotic shock, this study shows that the correlation between protein and mRNA differs for transcripts that increase versus decrease in abundance, and reveals physiological reasons for these differences. Natural microenvironments change rapidly, and living creatures must respond quickly and efficiently to thrive within this flux. At all cellular levels—signaling, transcription, translation, metabolism, cell growth, and division—the response is dynamic and coordinated. Some aspects of this response, such as dynamic changes of the transcriptome, are well understood. But other aspects, like the response of the proteome, have remained obscured primarily because of previous limitations in technology. Without coordinated time‐course data, it has remained impossible to correctly characterize the correlations and dependencies between these two essential levels of cell biology. This work presents an extended picture of the coordinated response of the transcriptome and proteome as cells respond to an abrupt environmental change. To assay proteomic dynamics, we developed a strategy for large‐scale, multiplexed quantitation using isobaric tags and high mass accuracy mass spectrometry. This sensitive yet efficient platform allows for the expedient collection of quantitative time‐course proteomic data at six time points, sufficiently reproducible to permit meaningful interpretation of variation across biological replicates. Time‐course transcriptome data were generated from paired biological samples, allowing us to examine the relationships between changes in mRNA and protein for each gene in terms of direction and intensity, as well as the characteristics of the temporal profiles for each gene. It was immediately obvious that a single measure of correlation across the entire data set was a meaningless metric. We therefore analyzed relationships between mRNA and protein for different subsets of data. In response to osmotic shock, hundreds of transcripts are highly induced, and their temporal pattern reveals a transient peak of maximal induction, which resolves into a new elevated level as cells acclimate (Figure 2 ). For this group of genes, there is extremely high correlation between peak mRNA change and protein change ( R 2 ∼0.8). But the dynamics of the molecules differ: while mRNA levels transiently overshoot their final levels, proteins gradually rise in abundance toward their new, elevated state. We observed, however, that a measure of efficiency connects the two profiles. The time it takes for a protein to acclimate to its new state correlates with the magnitude of the excess mRNA induction. Thus, the cell imparts an urgency to protein induction by transiently producing excess transcript. The most surprising result, however, involves transcripts that decrease in abundance. In response to osmotic shock, the cell transiently reduces over 600 transcripts, many of which are among the most highly expressed in unstressed cells. But protein levels for these genes remain, for the most part, almost completely unchanged. The stark absence of protein repression is independent of basal protein abundance, independent of reported protein half‐lives, reproducible across biological replicates, and validated by quantitative western blots. Furthermore, since we do detect a handful of proteins whose abundance is significantly reduced, our technology is capable of identifying protein loss. Thus, we conclude that transcript reduction serves another purpose besides reducing protein levels. To explore alternate interpretations of the consequence of transcriptional repression, we devised a mass‐action kinetic model, which describes protein changes based on mRNA dynamics in the context of transient changes in the rates of cell division. The model successfully recapitulated the observed data, allowing us to alter modeling parameters to test various hypotheses. In response to osmotic shock, overall rates of translation temporarily decrease and cell growth transiently arrests before resuming at a slower rate. We reasoned that mRNA reduction might lower the rate of new protein synthesis, but that retarded production is balanced by reduced cell division. We explored both aspects of this logic with our model. As expected, removing cell division from our model led to a calculated decrease of protein levels, indicating that reduced growth is necessary for maintaining protein levels. However, when we computationally held mRNA levels stable and calculated protein levels in the absence of mRNA repression, we did not find the expected increase in protein abundance. We then considered the possibility that one function of the regulated repression of these highly abundant transcripts was to liberate proteins essential for translation, such as ribosomes or translation initiation factors. To explore this, we examined a mutant lacking the Dot6p/Tod6p transcriptional repressors, which fails to properly repress ∼250 genes in response to osmotic shock. In the wild type, the mRNA for a Dot6p/Tod6p target ( ARX1 ) decreased seven‐fold, and the remaining transcript was generally unassociated with poly‐ribosomes. In the mutant, however, the mRNA levels were reduced only two‐fold, while the remaining transcript continued to bind ribosomes. Therefore, failure to reduce transcript levels led to a persistent association with poly‐ribosomes, thereby consuming translational machinery. Our hypothesis is, therefore, that widespread changes in the transcriptome promote efficient translation of new proteins. Transcript increase serves to increase abundance of the encoded proteins, while reduction of some of the most abundant and highly translated mRNAs supports this project by liberating translational capacity. While it is not clear what factors are the limiting elements, it is clear that a full picture of cellular biology requires exploring the dynamics of the cellular response. The correlation between protein and mRNA change is very high at transcripts that increase in abundance, but negligible at reduced transcripts following NaCl shock. Modeling and experimental data suggest that reducing levels of high‐abundance transcripts helps to direct translational machinery to newly made transcripts. The transient burst of transcript increase serves to accelerate changes in protein abundance. Post‐transcriptional regulation of protein abundance is pervasive, although most of the variance in protein change is explained by changes in mRNA abundance.

Background The All of Us Research Program (AoURP, “the program”) is an initiative, sponsored by the National Institutes of Health (NIH), that aims to enroll one million people (or more) across the USA. Through repeated engagement of participants, a research resource is being created to enable a variety of future observational and interventional studies. The program has also committed to genomic data generation and returning important health-related information to participants. Methods Whole-genome sequencing (WGS), variant calling processes, data interpretation, and return-of-results procedures had to be created and receive an Investigational Device Exemption (IDE) from the United States Food and Drug Administration (FDA). The performance of the entire workflow was assessed through the largest known cross-center, WGS-based, validation activity that was refined iteratively through interactions with the FDA over many months. Results The accuracy and precision of the WGS process as a device for the return of certain health-related genomic results was determined to be sufficient, and an IDE was granted. Conclusions We present here both the process of navigating the IDE application process with the FDA and the results of the validation study as a guide to future projects which may need to follow a similar path. Changes to the program in the future will be covered in supplementary submissions to the IDE and will support additional variant classes, sample types, and any expansion to the reportable regions.

Comprehensively mapping the genetic basis of human disease across diverse individuals is a long-standing goal for the field of human genetics 1 – 4 . The All of Us Research Program is a longitudinal cohort study aiming to enrol a diverse group of at least one million individuals across the USA to accelerate biomedical research and improve human health 5 , 6 . Here we describe the programme’s genomics data release of 245,388 clinical-grade genome sequences. This resource is unique in its diversity as 77% of participants are from communities that are historically under-represented in biomedical research and 46% are individuals from under-represented racial and ethnic minorities. All of Us identified more than 1 billion genetic variants, including more than 275 million previously unreported genetic variants, more than 3.9 million of which had coding consequences. Leveraging linkage between genomic data and the longitudinal electronic health record, we evaluated 3,724 genetic variants associated with 117 diseases and found high replication rates across both participants of European ancestry and participants of African ancestry. Summary-level data are publicly available, and individual-level data can be accessed by researchers through the All of Us Researcher Workbench using a unique data passport model with a median time from initial researcher registration to data access of 29 hours. We anticipate that this diverse dataset will advance the promise of genomic medicine for all. A study describes the release of clinical-grade whole-genome sequence data for 245,388 diverse participants by the All of Us Research Program and characterizes the properties of the dataset.

Purpose ClinVar is increasingly used as a resource for both genetic variant interpretation and clinical practice. However, controversies exist regarding the consistency of classifications in ClinVar, and questions remain about how best to use these data. Our study systematically examined ClinVar to identify common sources of discordance and thus inform ongoing practices. Methods We analyzed variants that had multiple classifications in ClinVar, excluding benign polymorphisms. Classifications were categorized by potential actionability and pathogenicity. Consensus interpretations were calculated for each variant, and the properties of the discordant outlier classifications were summarized. Results Our study included 74,065 classifications of 27,224 unique variants in 1,713 genes. We found that (i) concordance rates differed among clinical areas and variant types; (ii) clinical testing methods had much higher concordance than basic literature curation and research efforts; (iii) older classifications had greater discordance than newer ones; and (iv) low-penetrance variants had particularly high discordance. Conclusion Recent variant classifications from clinical testing laboratories have high overall concordance in many (but not all) clinical areas. ClinVar can be a reliable resource supporting variant interpretation, quality assessment, and clinical practice when factors uncovered in this study are taken into account. Ongoing improvements to ClinVar may make it easier to use, particularly for nonexpert users.

Background The American College of Medical Genetics and Genomics (ACMG)/Association for Molecular Pathology (AMP) clinical variant interpretation guidelines established criteria for different types of evidence. This includes the strong evidence codes PS3 and BS3 for “well-established” functional assays demonstrating a variant has abnormal or normal gene/protein function, respectively. However, they did not provide detailed guidance on how functional evidence should be evaluated, and differences in the application of the PS3/BS3 codes are a contributor to variant interpretation discordance between laboratories. This recommendation seeks to provide a more structured approach to the assessment of functional assays for variant interpretation and guidance on the use of various levels of strength based on assay validation. Methods The Clinical Genome Resource (ClinGen) Sequence Variant Interpretation (SVI) Working Group used curated functional evidence from ClinGen Variant Curation Expert Panel-developed rule specifications and expert opinions to refine the PS3/BS3 criteria over multiple in-person and virtual meetings. We estimated the odds of pathogenicity for assays using various numbers of variant controls to determine the minimum controls required to reach moderate level evidence. Feedback from the ClinGen Steering Committee and outside experts were incorporated into the recommendations at multiple stages of development. Results The SVI Working Group developed recommendations for evaluators regarding the assessment of the clinical validity of functional data and a four-step provisional framework to determine the appropriate strength of evidence that can be applied in clinical variant interpretation. These steps are as follows: (1) define the disease mechanism, (2) evaluate the applicability of general classes of assays used in the field, (3) evaluate the validity of specific instances of assays, and (4) apply evidence to individual variant interpretation. We found that a minimum of 11 total pathogenic and benign variant controls are required to reach moderate-level evidence in the absence of rigorous statistical analysis. Conclusions The recommendations and approach to functional evidence evaluation described here should help clarify the clinical variant interpretation process for functional assays. Further, we hope that these recommendations will help develop productive partnerships with basic scientists who have developed functional assays that are useful for interrogating the function of a variety of genes.

Disparities in data underlying clinical genomic interpretation is an acknowledged problem, but there is a paucity of data demonstrating it. The All of Us Research Program is collecting data including whole-genome sequences, health records, and surveys for at least a million participants with diverse ancestry and access to healthcare, representing one of the largest biomedical research repositories of its kind. Here, we examine pathogenic and likely pathogenic variants that were identified in the All of Us cohort. The European ancestry subgroup showed the highest overall rate of pathogenic variation, with 2.26% of participants having a pathogenic variant. Other ancestry groups had lower rates of pathogenic variation, including 1.62% for the African ancestry group and 1.32% in the Latino/Admixed American ancestry group. Pathogenic variants were most frequently observed in genes related to Breast/Ovarian Cancer or Hypercholesterolemia. Variant frequencies in many genes were consistent with the data from the public gnomAD database, with some notable exceptions resolved using gnomAD subsets. Differences in pathogenic variant frequency observed between ancestral groups generally indicate biases of ascertainment of knowledge about those variants, but some deviations may be indicative of differences in disease prevalence. This work will allow targeted precision medicine efforts at revealed disparities. A comparison of the frequency of pathogenic mutations in 73 genes in the All of Us cohort highlights the differences in pathogenic variation attributed to ancestry.

This corrects the article DOI: 10.1038/gim.2017.60.

Correction to: Genet Med (2017) 19, 1118–1126; 10.1038/gim.2017.60 The originally published version of this article contained the statement that “The first two authors contributed equally to this paper.” This is incorrect. The authors regret the error.