Critical assessment of variant prioritization methods for rare disease diagnosis within the rare genomes project.

BACKGROUND: A major obstacle faced by families with rare diseases is obtaining a genetic diagnosis. The average "diagnostic odyssey" lasts over five years and causal variants are identified in under 50%, even when capturing variants genome-wide. To aid in the interpretation and prioritization of the vast number of variants detected, computational methods are proliferating. Knowing which tools are most effective remains unclear. To evaluate the performance of computational methods, and to encourage innovation in method development, we designed a Critical Assessment of Genome Interpretation (CAGI) community challenge to place variant prioritization models head-to-head in a real-life clinical diagnostic setting. METHODS: We utilized genome sequencing (GS) data from families sequenced in the Rare Genomes Project (RGP), a direct-to-participant research study on the utility of GS for rare disease diagnosis and gene discovery. Challenge predictors were provided with a dataset of variant calls and phenotype terms from 175 RGP individuals (65 families), including 35 solved training set families with causal variants specified, and 30 unlabeled test set families (14 solved, 16 unsolved). We tasked teams to identify causal variants in as many families as possible. Predictors submitted variant predictions with estimated probability of causal relationship (EPCR) values. Model performance was determined by two metrics, a weighted score based on the rank position of causal variants, and the maximum F-measure, based on precision and recall of causal variants across all EPCR values. RESULTS: Sixteen teams submitted predictions from 52 models, some with manual review incorporated. Top performers recalled causal variants in up to 13 of 14 solved families within the top 5 ranked variants. Newly discovered diagnostic variants were returned to two previously unsolved families following confirmatory RNA sequencing, and two novel disease gene candidates were entered into Matchmaker Exchange. In one example, RNA sequencing demonstrated aberrant splicing due to a deep intronic indel in ASNS, identified in trans with a frameshift variant in an unsolved proband with phenotypes consistent with asparagine synthetase deficiency. CONCLUSIONS: Model methodology and performance was highly variable. Models weighing call quality, allele frequency, predicted deleteriousness, segregation, and phenotype were effective in identifying causal variants, and models open to phenotype expansion and non-coding variants were able to capture more difficult diagnoses and discover new diagnoses. Overall, computational models can significantly aid variant prioritization. For use in diagnostics, detailed review and conservative assessment of prioritized variants against established criteria is needed.

Molecular Characterization of Juxtaglomerular Cell Tumors: Evidence of Alterations in MAPK-RAS Pathway.

Juxtaglomerular cell tumor (JGCT) is a rare neoplasm, part of the family of mesenchymal tumors of the kidney. Although the pathophysiological and clinical correlates of JGCT are well known, as these tumors are an important cause of early-onset arterial hypertension refractory to medical treatment, their molecular background is unknown, with only few small studies investigating their karyotype. Herein we describe a multi-institutional cohort of JGCTs diagnosed by experienced genitourinary pathologists, evaluating clinical presentation and outcome, morphologic diversity, and, importantly, the molecular features. Ten JGCTs were collected from 9 institutions, studied by immunohistochemistry, and submitted to whole exome sequencing. Our findings highlight the morphologic heterogeneity of JGCT, which can mimic several kidney tumor entities. Three cases showed concerning histologic features, but the patient course was unremarkable, which suggests that morphologic evaluation alone cannot reliably predict the clinical behavior. Gain-of-function variants in RAS GTPases were detected in JGCTs, with no evidence of additional recurrent genomic alterations. In conclusion, we present the largest series of JGCT characterized by whole exome sequencing, highlighting the putative role of the MAPK-RAS pathway.

Critical assessment of variant prioritization methods for rare disease diagnosis within the Rare Genomes Project.

Background: A major obstacle faced by rare disease families is obtaining a genetic diagnosis. The average "diagnostic odyssey" lasts over five years, and causal variants are identified in under 50%. The Rare Genomes Project (RGP) is a direct-to-participant research study on the utility of genome sequencing (GS) for diagnosis and gene discovery. Families are consented for sharing of sequence and phenotype data with researchers, allowing development of a Critical Assessment of Genome Interpretation (CAGI) community challenge, placing variant prioritization models head-to-head in a real-life clinical diagnostic setting. Methods: Predictors were provided a dataset of phenotype terms and variant calls from GS of 175 RGP individuals (65 families), including 35 solved training set families, with causal variants specified, and 30 test set families (14 solved, 16 unsolved). The challenge tasked teams with identifying the causal variants in as many test set families as possible. Ranked variant predictions were submitted with estimated probability of causal relationship (EPCR) values. Model performance was determined by two metrics, a weighted score based on rank position of true positive causal variants and maximum F-measure, based on precision and recall of causal variants across EPCR thresholds. Results: Sixteen teams submitted predictions from 52 models, some with manual review incorporated. Top performing teams recalled the causal variants in up to 13 of 14 solved families by prioritizing high quality variant calls that were rare, predicted deleterious, segregating correctly, and consistent with reported phenotype. In unsolved families, newly discovered diagnostic variants were returned to two families following confirmatory RNA sequencing, and two prioritized novel disease gene candidates were entered into Matchmaker Exchange. In one example, RNA sequencing demonstrated aberrant splicing due to a deep intronic indel in ASNS, identified in trans with a frameshift variant, in an unsolved proband with phenotype overlap with asparagine synthetase deficiency. Conclusions: By objective assessment of variant predictions, we provide insights into current state-of-the-art algorithms and platforms for genome sequencing analysis for rare disease diagnosis and explore areas for future optimization. Identification of diagnostic variants in unsolved families promotes synergy between researchers with clinical and computational expertise as a means of advancing the field of clinical genome interpretation.

Ten simple rules for using public biological data for your research.

With an increasing amount of biological data available publicly, there is a need for a guide on how to successfully download and use this data. The 10 simple rules for using public biological data are: (1) use public data purposefully in your research; (2) evaluate data for your use case; (3) check data reuse requirements and embargoes; (4) be aware of ethics for data reuse; (5) plan for data storage and compute requirements; (6) know what you are downloading; (7) download programmatically and verify integrity; (8) properly cite data; (9) make reprocessed data and models Findable, Accessible, Interoperable, and Reusable (FAIR) and share; and (10) make pipelines and code FAIR and share. These rules are intended as a guide for researchers wanting to make use of available data and to increase data reuse and reproducibility.

Genetic factors help explain the variable responses of young children with cystic fibrosis to vitamin D supplements.

BACKGROUND & AIMS: Children with cystic fibrosis (CF) are susceptible to fat-soluble vitamin deficiencies unless supplemented, but even large doses of vitamin D may not prevent low 25-hydroxyvitamin D (25OHD) concentrations. The explanation for these vitamin D non-responders has been elusive. We utilized data from whole genome sequencing (WGS) to test the hypothesis that genetic variations predict responsiveness to vitamin D supplementation in a prospective cohort study of children with CF in the first 3 years of life. METHODS: One hundred and one infants born during 2012-2017 and diagnosed with CF through newborn screening were studied. Serum 25OHD concentrations and vitamin D supplement doses were assessed during early infancy and annually thereafter. WGS was performed, the resultant variant calling files processed, and the summary statistics from a recent genome-wide association study were utilized to construct a polygenic risk score (PRS) for each subject. RESULTS: Overall, the prevalence of vitamin D insufficiency (<30 ng/mL) was 21% in the first 3 years of life. Among the 70 subjects who always adhered to vitamin D supplement doses recommended by the US CF Foundation guidelines, 89% were responders (achieved vitamin D sufficiency) by 3 years of age, while 11% were transient or non-responders. Multiple regression analysis revealed that PRS was a significant predictor of 25OHD concentrations (p < 0.001) and the likelihood of being an earlier responder in the first 3 years of life (p < 0.01). A limited SNP analysis revealed variants in four important genes (GC, LIPC, CYP24A1, and PDE3B) that were shown to be associated with 25OHD concentrations and vitamin D responder status. Other determinants included vitamin D supplement dose, season at 25OHD measurement, and pancreatic functional status. CONCLUSIONS: Applying WGS in conjunction with utilizing a PRS approach revealed genetic variations that partially explain the unresponsiveness of some children with CF to vitamin D supplementation. Our findings suggest that a nutrigenomics strategy could help promote personalized treatment in CF.

VarSight: prioritizing clinically reported variants with binary classification algorithms.

BACKGROUND: When applying genomic medicine to a rare disease patient, the primary goal is to identify one or more genomic variants that may explain the patient's phenotypes. Typically, this is done through annotation, filtering, and then prioritization of variants for manual curation. However, prioritization of variants in rare disease patients remains a challenging task due to the high degree of variability in phenotype presentation and molecular source of disease. Thus, methods that can identify and/or prioritize variants to be clinically reported in the presence of such variability are of critical importance. METHODS: We tested the application of classification algorithms that ingest variant annotations along with phenotype information for predicting whether a variant will ultimately be clinically reported and returned to a patient. To test the classifiers, we performed a retrospective study on variants that were clinically reported to 237 patients in the Undiagnosed Diseases Network. RESULTS: We treated the classifiers as variant prioritization systems and compared them to four variant prioritization algorithms and two single-measure controls. We showed that the trained classifiers outperformed all other tested methods with the best classifiers ranking 72% of all reported variants and 94% of reported pathogenic variants in the top 20. CONCLUSIONS: We demonstrated how freely available binary classification algorithms can be used to prioritize variants even in the presence of real-world variability. Furthermore, these classifiers outperformed all other tested methods, suggesting that they may be well suited for working with real rare disease patient datasets.

Dynamic behavior of oligomeric inorganic pyrophosphatase explored by quasielastic neutron scattering.

The purpose of this investigation is to determine whether a large oligomeric protein, inorganic pyrophosphatase (IPPase) from Thermococcus thioreducens with quaternary structural complexity, would have distinguishable dynamic characteristics compared to those of the small simple monomeric model protein, lysozyme. In this study, the ß-relaxational dynamics of the two proteins, IPPase and lysozyme, are compared in the 10 ps to 0.5 ns time interval using quasi-elastic neutron scattering (QENS). Both of the protein dynamics show a characteristic logarithmic-like decay in the intermediate scattering function (ISF) of the hydrogen atoms. Distinguishable dynamical behavior found between two proteins reveals local flexibility and conformational substates unique to oligomeric structures. Moreover, the temperature dependence of the mean square displacement (MSD) of the hydrogen atoms in protein molecules, which is a traditional way to determine the "softness" of the protein molecule, is measured and shows no difference for the two proteins.