A Diagnosis for All Rare Genetic Diseases: The Horizon and the Next Frontiers.

The introduction of exome sequencing in the clinic has sparked tremendous optimism for the future of rare disease diagnosis, and there is exciting opportunity to further leverage these advances. To provide diagnostic clarity to all of these patients, however, there is a critical need for the field to develop and implement strategies to understand the mechanisms underlying all rare diseases and translate these to clinical care.

Bedside Back to Bench: Building Bridges between Basic and Clinical Genomic Research.

Genome sequencing has revolutionized the diagnosis of genetic diseases. Close collaborations between basic scientists and clinical genomicists are now needed to link genetic variants with disease causation. To facilitate such collaborations, we recommend prioritizing clinically relevant genes for functional studies, developing reference variant-phenotype databases, adopting phenotype description standards, and promoting data sharing.

Implementation of a dyadic nomenclature for monogenic diseases.

A core task when establishing the strength of evidence for a gene's role in a monogenic disorder is determining the appropriate disease entity to curate. Establishing this concept determines which evidence can be applied and quantified toward the final gene-disease validity, variant pathogenicity, or actionability classification. Genes with implications in more than one phenotype can necessitate a process of lumping and splitting, disease reorganization, and updates to disease nomenclature. Reappraisal of the names that are used as labels for disease entities is therefore a necessary and perpetual process. The Clinical Genome Resource (ClinGen), in collaboration with representatives from Monarch Disease Ontology (Mondo) and Online Inheritance in Man (OMIM), formed the Disease Naming Advisory Committee (DNAC) to develop guidance for groups faced with the need to establish the "curated disease entity" for gene-phenotype validity and variant pathogenicity and to update disease names for clinical use when necessary. The objective of this group was to harmonize guidance for disease naming across these nosologic entities and among ClinGen curation groups in collaboration with other disease-related professional groups. Here, we present the initial guidance developed by the DNAC with representative examples provided by the ClinGen expert panels and working groups that warranted nomenclature updates. We also discuss the broader implications of these efforts and their benefits for harmonization of gene-disease validity curation. Overall, this work sheds light on current inconsistencies and/or discrepancies and is designed to engage the broader community on how ClinGen defines monogenic disorders using a consistent approach for disease naming.

ClinGen guidance for use of the PP1/BS4 co-segregation and PP4 phenotype specificity criteria for sequence variant pathogenicity classification.

The 2015 American College of Medical Genetics and Genomics and the Association for Molecular Pathology variant classification publication established a standard employed internationally to guide laboratories in variant assessment. Those recommendations included both pathogenic (PP1) and benign (BS4) criteria for evaluating the inheritance patterns of variants, but details of how to apply those criteria at appropriate evidence levels were sparse. Several publications have since attempted to provide additional guidance, but anecdotally, this issue is still challenging. Additionally, it is not clear that those prior efforts fully distinguished disease-gene identification considerations from variant pathogenicity considerations nor did they address autosomal-recessive and X-linked inheritance. Here, we have taken a mixed inductive and deductive approach to this problem using real diseases as examples. We have developed a practical heuristic for genetic co-segregation evidence and have also determined that the specific phenotype criterion (PP4) is inseparably coupled to the co-segregation criterion. We have also determined that negative evidence at one locus constitutes positive evidence for other loci for disorders with locus heterogeneity. Finally, we provide a points-based system for evaluating phenotype and co-segregation as evidence types to support or refute a locus and show how that can be integrated into the Bayesian framework now used for variant classification and consistent with the 2015 guidelines.

Genotype first: Clinical genomics research through a reverse phenotyping approach.

Although genomic research has predominantly relied on phenotypic ascertainment of individuals affected with heritable disease, the falling costs of sequencing allow consideration of genomic ascertainment and reverse phenotyping (the ascertainment of individuals with specific genomic variants and subsequent evaluation of physical characteristics). In this research modality, the scientific question is inverted: investigators gather individuals with a genomic variant and test the hypothesis that there is an associated phenotype via targeted phenotypic evaluations. Genomic ascertainment research is thus a model of predictive genomic medicine and genomic screening. Here, we provide our experience implementing this research method. We describe the infrastructure we developed to perform reverse phenotyping studies, including aggregating a super-cohort of sequenced individuals who consented to recontact for genomic ascertainment research. We assessed 13 studies completed at the National Institutes of Health (NIH) that piloted our reverse phenotyping approach. The studies can be broadly categorized as (1) facilitating novel genotype-disease associations, (2) expanding the phenotypic spectra, or (3) demonstrating ex vivo functional mechanisms of disease. We highlight three examples of reverse phenotyping studies in detail and describe how using a targeted reverse phenotyping approach (as opposed to phenotypic ascertainment or clinical informatics approaches) was crucial to the conclusions reached. Finally, we propose a framework and address challenges to building collaborative genomic ascertainment research programs at other institutions. Our goal is for more researchers to take advantage of this approach, which will expand our understanding of the predictive capability of genomic medicine and increase the opportunity to mitigate genomic disease.

Strategic vision for improving human health at The Forefront of Genomics.

Starting with the launch of the Human Genome Project three decades ago, and continuing after its completion in 2003, genomics has progressively come to have a central and catalytic role in basic and translational research. In addition, studies increasingly demonstrate how genomic information can be effectively used in clinical care. In the future, the anticipated advances in technology development, biological insights, and clinical applications (among others) will lead to more widespread integration of genomics into almost all areas of biomedical research, the adoption of genomics into mainstream medical and public-health practices, and an increasing relevance of genomics for everyday life. On behalf of the research community, the National Human Genome Research Institute recently completed a multi-year process of strategic engagement to identify future research priorities and opportunities in human genomics, with an emphasis on health applications. Here we describe the highest-priority elements envisioned for the cutting-edge of human genomics going forward-that is, at 'The Forefront of Genomics'.

Genome-wide identification of dominant polyadenylation hexamers for use in variant classification.

Polyadenylation is an essential process for the stabilization and export of mRNAs to the cytoplasm and the polyadenylation signal hexamer (herein referred to as hexamer) plays a key role in this process. Yet, only 14 Mendelian disorders have been associated with hexamer variants. This is likely an under-ascertainment as hexamers are not well defined and not routinely examined in molecular analysis. To facilitate the interrogation of putatively pathogenic hexamer variants, we set out to define functionally important hexamers genome-wide as a resource for research and clinical testing interrogation. We identified predominant polyA sites (herein referred to as pPAS) and putative predominant hexamers across protein coding genes (PAS usage >50% per gene). As a measure of the validity of these sites, the population constraint of 4532 predominant hexamers were measured. The predominant hexamers had fewer observed variants compared to non-predominant hexamers and trimer controls, and CADD scores for variants in these hexamers were significantly higher than controls. Exome data for 1477 individuals were interrogated for hexamer variants and transcriptome data were generated for 76 individuals with 65 variants in predominant hexamers. 3' RNA-seq data showed these variants resulted in alternate polyadenylation events (38%) and in elongated mRNA transcripts (12%). Our list of pPAS and predominant hexamers are available in the UCSC genome browser and on GitHub. We suggest this list of predominant hexamers can be used to interrogate exome and genome data. Variants in these predominant hexamers should be considered candidates for pathogenic variation in human disease, and to that end we suggest pathogenicity criteria for classifying hexamer variants.

Calibration of computational tools for missense variant pathogenicity classification and ClinGen recommendations for PP3/BP4 criteria.

Recommendations from the American College of Medical Genetics and Genomics and the Association for Molecular Pathology (ACMG/AMP) for interpreting sequence variants specify the use of computational predictors as "supporting" level of evidence for pathogenicity or benignity using criteria PP3 and BP4, respectively. However, score intervals defined by tool developers, and ACMG/AMP recommendations that require the consensus of multiple predictors, lack quantitative support. Previously, we described a probabilistic framework that quantified the strengths of evidence (supporting, moderate, strong, very strong) within ACMG/AMP recommendations. We have extended this framework to computational predictors and introduce a new standard that converts a tool's scores to PP3 and BP4 evidence strengths. Our approach is based on estimating the local positive predictive value and can calibrate any computational tool or other continuous-scale evidence on any variant type. We estimate thresholds (score intervals) corresponding to each strength of evidence for pathogenicity and benignity for thirteen missense variant interpretation tools, using carefully assembled independent data sets. Most tools achieved supporting evidence level for both pathogenic and benign classification using newly established thresholds. Multiple tools reached score thresholds justifying moderate and several reached strong evidence levels. One tool reached very strong evidence level for benign classification on some variants. Based on these findings, we provide recommendations for evidence-based revisions of the PP3 and BP4 ACMG/AMP criteria using individual tools and future assessment of computational methods for clinical interpretation.

Metal Fluorides Passivate II-VI and III-V Quantum Dots.

Quantum dots (QDs) with metal fluoride surface ligands were prepared via reaction with anhydrous oleylammonium fluoride. Carboxylate terminated II-VI QDs underwent carboxylate for fluoride exchange, while InP QDs underwent photochemical acidolysis yielding oleylamine, PH3, and InF3. The final photoluminescence quantum yield (PLQY) reached 83% for InP and near unity for core-shell QDs. Core-only CdS QDs showed dramatic improvements in PLQY, but only after exposure to air. Following etching, the InP QDs were bound by oleylamine ligands that were characterized by the frequency and breadth of the corresponding ν(N-H) bands in the infrared absorption spectrum. The fluoride content (1.6-9.2 nm-2) was measured by titration with chlorotrimethylsilane and compared with the oleylamine content (2.3-5.1 nm-2) supporting the formation of densely covered surfaces. The influence of metal fluoride adsorption on the air stability of QDs is discussed.

Disentangling Object Category Representations Driven by Dynamic and Static Visual Input.

Humans can label and categorize objects in a visual scene with high accuracy and speed, a capacity well characterized with studies using static images. However, motion is another cue that could be used by the visual system to classify objects. To determine how motion-defined object category information is processed by the brain in the absence of luminance-defined form information, we created a novel stimulus set of "object kinematograms" to isolate motion-defined signals from other sources of visual information. Object kinematograms were generated by extracting motion information from videos of 6 object categories and applying the motion to limited-lifetime random dot patterns. Using functional magnetic resonance imaging (fMRI) (n = 15, 40% women), we investigated whether category information from the object kinematograms could be decoded within the occipitotemporal and parietal cortex and evaluated whether the information overlapped with category responses to static images from the original videos. We decoded object category for both stimulus formats in all higher-order regions of interest (ROIs). More posterior occipitotemporal and ventral regions showed higher accuracy in the static condition, while more anterior occipitotemporal and dorsal regions showed higher accuracy in the dynamic condition. Further, decoding across the two stimulus formats was possible in all regions. These results demonstrate that motion cues can elicit widespread and robust category responses on par with those elicited by static luminance cues, even in ventral regions of visual cortex that have traditionally been associated with primarily image-defined form processing.SIGNIFICANCE STATEMENT Much research on visual object recognition has focused on recognizing objects in static images. However, motion is a rich source of information that humans might also use to categorize objects. Here, we present the first study to compare neural representations of several animate and inanimate objects when category information is presented in two formats: static cues or isolated dynamic motion cues. Our study shows that, while higher-order brain regions differentially process object categories depending on format, they also contain robust, abstract category representations that generalize across format. These results expand our previous understanding of motion-derived animate and inanimate object category processing and provide useful tools for future research on object category processing driven by multiple sources of visual information.

Updated variant curation expert panel criteria and pathogenicity classifications for 251 variants for RYR1-related malignant hyperthermia susceptibility.

The ClinGen malignant hyperthermia susceptibility (MHS) variant curation expert panel specified the American College of Medical Genetics and Genomics/Association of Molecular Pathologists (ACMG/AMP) criteria for RYR1-related MHS and a pilot analysis of 84 variants was published. We have now classified an additional 251 variants for RYR1-related MHS according to current ClinGen standards and updated the criteria where necessary. Criterion PS4 was modified such that individuals with multiple RYR1 variants classified as pathogenic (P), likely pathogenic (LP), or variant of uncertain significance (VUS) were not considered as providing evidence for pathogenicity. Criteria PS1 and PM5 were revised to consider LP variants at the same amino-acid residue as providing evidence for pathogenicity at reduced strength. Finally, PM1 was revised such that if PS1 or PM5 are used PM1, if applicable, should be downgraded to supporting. Of the 251 RYR1 variants, 42 were classified as P/LP, 16 as B/LB, and 193 as VUS. The primary driver of 175 VUS classifications was insufficient evidence supporting pathogenicity, rather than evidence against pathogenicity. Functional data supporting PS3/BS3 was identified for only 13 variants. Based on the posterior probabilities of pathogenicity and variant frequencies in gnomAD, we estimated the prevalence of individuals with RYR1-related MHS pathogenic variants to be between 1/300 and 1/1075, considerably higher than current estimates. We have updated ACMG/AMP criteria for RYR1-related MHS and classified 251 variants. We suggest that prioritization of functional studies is needed to resolve the large number of VUS classifications and allow for appropriate risk assessment. RYR1-related MHS pathogenic variants are likely to be more common than currently appreciated.

Engagement and return of results preferences among a primarily African American genomic sequencing research cohort.

Genomics researchers are increasingly interested in what constitutes effective engagement of individuals from underrepresented groups. This is critical for longitudinal projects needed to inform the implementation of precision medicine. Return of results is one opportunity for engagement. The aims of this study were to determine participant perspectives on optimal engagement strategies and priorities for return of results and the extent to which focus groups were an effective modality for gathering input on these topics. We conducted six professionally moderated focus groups with 49 participants in a genomics research study. Transcripts from audio-recorded sessions were coded by two researchers and themes were discussed with the wider research team. All groups raised the issue of mistrust. Individuals participated nonetheless to contribute their perspectives and benefit their community. Many group members preferred engagement modalities that are offered to all participants and allow them to share the nuances of their perspectives over the use of participant representatives and surveys. All groups created a consensus ranking for result return priorities. Results for life-threatening conditions were the highest priority to return, followed by those related to treatable conditions that affect physical or mental health. We advocate for engagement strategies that reach as many participants as possible and allow them to share their perspectives in detail. Such strategies are valued by participants, can be effective for developing return of results policies, and may help institutions become more trustworthy.

A dyadic approach to the delineation of diagnostic entities in clinical genomics.

The delineation of disease entities is complex, yet recent advances in the molecular characterization of diseases provide opportunities to designate diseases in a biologically valid manner. Here, we have formalized an approach to the delineation of Mendelian genetic disorders that encompasses two distinct but inter-related concepts: (1) the gene that is mutated and (2) the phenotypic descriptor, preferably a recognizably distinct phenotype. We assert that only by a combinatorial or dyadic approach taking both of these attributes into account can a unitary, distinct genetic disorder be designated. We propose that all Mendelian disorders should be designated as "GENE-related phenotype descriptor" (e.g., "CFTR-related cystic fibrosis"). This approach to delineating and naming disorders reconciles the complexity of gene-to-phenotype relationships in a simple and clear manner yet communicates the complexity and nuance of these relationships.

Comparison of literature mining tools for variant classification: Through the lens of 50 RYR1 variants.

PURPOSE: The American College of Medical Genetics and Genomics and the Association for Molecular Pathology have outlined a schema that allows for systematic classification of variant pathogenicity. Although gnomAD is generally accepted as a reliable source of population frequency data and ClinGen has provided guidance on the utility of specific bioinformatic predictors, there is no consensus source for identifying publications relevant to a variant. Multiple tools are available to aid in the identification of relevant variant literature, including manually curated databases and literature search engines. We set out to determine the utility of 4 literature mining tools used for ascertainment to inform the discussion of the use of these tools. METHODS: Four literature mining tools including the Human Gene Mutation Database, Mastermind, ClinVar, and LitVar 2.0 were used to identify relevant variant literature for 50 RYR1 variants. Sensitivity and precision were determined for each tool. RESULTS: Sensitivity among the 4 tools ranged from 0.332 to 0.687. Precision ranged from 0.389 to 0.906. No single tool retrieved all relevant publications. CONCLUSION: At the current time, the use of multiple tools is necessary to completely identify the literature relevant to curate a variant.

Assessment of the evidence yield for the calibrated PP3/BP4 computational recommendations.

PURPOSE: To investigate the number of rare missense variants observed in human genome sequences by ACMG/AMP PP3/BP4 evidence strength, following the ClinGen-calibrated PP3/BP4 computational recommendations. METHODS: Missense variants from the genome sequences of 300 probands from the Rare Genomes Project with suspected rare disease were analyzed using computational prediction tools that were able to reach PP3_Strong and BP4_Moderate evidence strengths (BayesDel, MutPred2, REVEL, and VEST4). The numbers of variants at each evidence strength were analyzed across disease-associated genes and genome-wide. RESULTS: From a median of 75.5 rare (≤1% allele frequency) missense variants in disease-associated genes per proband, a median of one reached PP3_Strong, 3-5 PP3_Moderate, and 3-5 PP3_Supporting. Most were allocated BP4 evidence (median 41-49 per proband) or were indeterminate (median 17.5-19 per proband). Extending the analysis to all protein-coding genes genome-wide, the number of variants reaching PP3_Strong score thresholds increased approximately 2.6-fold compared with disease-associated genes, with a median per proband of 1-3 PP3_Strong, 8-16 PP3_Moderate, and 10-17 PP3_Supporting. CONCLUSION: A small number of variants per proband reached PP3_Strong and PP3_Moderate in 3424 disease-associated genes. Although not the intended use of the recommendations, this was also observed genome-wide. Use of PP3/BP4 evidence as recommended from calibrated computational prediction tools in the clinical diagnostic laboratory is unlikely to inappropriately contribute to the classification of an excessive number of variants as pathogenic or likely pathogenic by ACMG/AMP rules.

Accrual Quality Improvement Program for clinical trials.

BACKGROUND: The Early Phase Cancer Prevention Clinical Trials Program (Consortia), led by the Division of Cancer Prevention, National Cancer Institute, supports and conducts trials assessing safety, tolerability, and cancer preventive potential of a variety of interventions. Accrual to cancer prevention trials includes the recruitment of unaffected populations, posing unique challenges related to minimizing participant burden and risk, given the less evident or measurable benefits to individual participants. The Accrual Quality Improvement Program was developed to address these challenges and better understand the multiple determinants of accrual activity throughout the life of the trial. Through continuous monitoring of accrual data, Accrual Quality Improvement Program identifies positive and negative factors in real-time to optimize enrollment rates for ongoing and future trials. METHODS: The Accrual Quality Improvement Program provides a web-based centralized infrastructure for collecting, analyzing, visualizing, and storing qualitative and quantitative participant-, site-, and study-level data. The Accrual Quality Improvement Program approaches cancer prevention clinical trial accrual as multi-factorial, recognizing protocol design, potential participants' characteristics, and individual site as well as study-wide implementation issues. RESULTS: The Accrual Quality Improvement Program was used across 39 Consortia trials from 2014 to 2022 to collect comprehensive trial information. The Accrual Quality Improvement Program captures data at the participant level, including number of charts reviewed, potential participants contacted and reasons why participants were not eligible for contact or did not consent to the trial or start intervention. The Accrual Quality Improvement Program also captures site-level (e.g. staffing issues) and study-level (e.g. when protocol amendments are made) data at each step of the recruitment/enrollment process, from potential participant identification to contact, consent, intervention, and study completion using a Recruitment Journal. Accrual Quality Improvement Program's functionality also includes tracking and visualization of a trial's cumulative accrual rate compared to the projected accrual rate, including a zone-based performance rating with corresponding quality improvement intervention recommendations. CONCLUSION: The challenges associated with recruitment and timely completion of early phase cancer prevention clinical trials necessitate a data collection program capable of continuous collection and quality improvement. The Accrual Quality Improvement Program collects cumulative data across National Cancer Institute, Division of Cancer Prevention early phase clinical trials, providing the opportunity for real-time review of participant-, site-, and study-level data and thereby enables responsive recruitment strategy and protocol modifications for improved recruitment rates to ongoing trials. Of note, Accrual Quality Improvement Program data collected from ongoing trials will inform future trials to optimize protocol design and maximize accrual efficiency.

Equivalent processing of facial expression and identity by macaque visual system and task-optimized neural network.

Both the primate visual system and artificial deep neural network (DNN) models show an extraordinary ability to simultaneously classify facial expression and identity. However, the neural computations underlying the two systems are unclear. Here, we developed a multi-task DNN model that optimally classified both monkey facial expressions and identities. By comparing the fMRI neural representations of the macaque visual cortex with the best-performing DNN model, we found that both systems: (1) share initial stages for processing low-level face features which segregate into separate branches at later stages for processing facial expression and identity respectively, and (2) gain more specificity for the processing of either facial expression or identity as one progresses along each branch towards higher stages. Correspondence analysis between the DNN and monkey visual areas revealed that the amygdala and anterior fundus face patch (AF) matched well with later layers of the DNN's facial expression branch, while the anterior medial face patch (AM) matched well with later layers of the DNN's facial identity branch. Our results highlight the anatomical and functional similarities between macaque visual system and DNN model, suggesting a common mechanism between the two systems.

Management of Secondary Genomic Findings.

Secondary genomic findings are increasingly being returned to individuals as opportunistic screening results. A secondary finding offers the chance to identify and mitigate disease that may otherwise be unrecognized in an individual. As a form of screening, secondary findings must be considered differently from sequencing results in a diagnostic setting. For these reasons, clinicians should employ an evaluation and long-term management strategy that accounts for both the increased disease risk associated with a secondary finding and the lower positive predictive value of a screening result compared to an indication-based testing result. Here we describe an approach to the clinical evaluation and management of an individual who presents with a secondary finding. This approach enumerates five domains of evaluation-(1) medical history, (2) physical exam, (3) family history, (4) diagnostic phenotypic testing, and (5) variant correlation-through which a clinician can distinguish a molecular finding from a clinicomolecular diagnosis of genomic disease. With this framework, both geneticists and non-geneticist clinicians can optimize their ability to detect and mitigate genomic disease while avoiding the pitfalls of overdiagnosis. Our goal with this approach is to help clinicians translate secondary findings into meaningful recognition, treatment, and prevention of disease.

Variant Classification Concordance using the ACMG-AMP Variant Interpretation Guidelines across Nine Genomic Implementation Research Studies.

Harmonization of variant pathogenicity classification across laboratories is important for advancing clinical genomics. The two CLIA-accredited Electronic Medical Record and Genomics Network sequencing centers and the six CLIA-accredited laboratories and one research laboratory performing genome or exome sequencing in the Clinical Sequencing Evidence-Generating Research Consortium collaborated to explore current sources of discordance in classification. Eight laboratories each submitted 20 classified variants in the ACMG secondary finding v.2.0 genes. After removing duplicates, each of the 158 variants was annotated and independently classified by two additional laboratories using the ACMG-AMP guidelines. Overall concordance across three laboratories was assessed and discordant variants were reviewed via teleconference and email. The submitted variant set included 28 P/LP variants, 96 VUS, and 34 LB/B variants, mostly in cancer (40%) and cardiac (27%) risk genes. Eighty-six (54%) variants reached complete five-category (i.e., P, LP, VUS, LB, B) concordance, and 17 (11%) had a discordance that could affect clinical recommendations (P/LP versus VUS/LB/B). 21% and 63% of variants submitted as P and LP, respectively, were discordant with VUS. Of the 54 originally discordant variants that underwent further review, 32 reached agreement, for a post-review concordance rate of 84% (118/140 variants). This project provides an updated estimate of variant concordance, identifies considerations for LP classified variants, and highlights ongoing sources of discordance. Continued and increased sharing of variant classifications and evidence across laboratories, and the ongoing work of ClinGen to provide general as well as gene- and disease-specific guidance, will lead to continued increases in concordance.

Large-scale exploration of nitrogen utilization efficiency in Asia region for rice crop: Variation patterns and determinants.

Improving rice nitrogen utilization efficiency (NUtE) is imperative to maximizing future food productivity while minimizing environmental threats, yet knowledge of its variation and the underlying regulatory factors is still lacking. Here, we integrated a dataset with 21,571 data compiled by available data from peer-reviewed literature and a large-scale field survey to address this knowledge gap. The overall results revealed great variations in rice NUtE, which were mainly associated with human activities, climate conditions, and rice variety. Specifically, N supply rate, temperature, and precipitation were the foremost determinants of rice NUtE, and NUtE responses to climatic change differed among rice varieties. Further prediction highlighted the improved rice NUtE with the increasing latitude or longitude. The indica and hybrid rice exhibited higher NUtE in low latitude regions compared to japonica and inbred rice, respectively. Collectively, our results evaluated the primary drivers of rice NUtE variations and predicted the geographic responses of NUtE in different varieties. Linking the global variations in rice NUtE with environmental factors and geographic adaptability provides valuable agronomic and ecological insights into the regulation of rice NUtE.