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Abstract 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.

Self-testing for COVID-19 may be a preferable strategy for identifying SARS-CoV-2 infection among populations in low- and middle-income settings. To determine South Africans’ values related to COVID-19 self-testing should it become widely available, a cross-sectional survey was administered in Durban, KwaZulu-Natal Province and the King Sabata Dalindyebo sub-district of the Eastern Cape.A 35-question survey was administered to 531 participants (268 female) in one urban and one rural setting of South Africa. Survey participants were randomly selected by household in the rural setting, while in the urban setting participants were approached in randomly selected public places. The survey assessed participants’ likelihood of using and willingness to pay for a COVID-19 self-test and actions they would take following a COVID-19 self-test. The results were analysed using descriptive statistics and bivariate and multivariate regression.Overall, 93.03% of participants supported COVID-19 self-testing, 61.62% of participants were willing to pay for self-testing, and 90.15% indicated they would communicate their results if they tested positive. Rural participants were more positively associated with each of these outcomes compared with urban-based participants. Should they test positive, most participants said they would: go in-person to a health facility for counselling (76.45%), self-isolate (95.85%), notify close contacts (97.74%), and inform their employer (95.14%).COVID-19 self-testing was a preferable option for most participants, although this varied with setting and demographic characteristics. Self-testing may overcome barriers to care for South Africans, but to achieve this, policies for self-testing and delivery methods must not exacerbate individuals’ underlying economic vulnerabilities.

Synthesis inhibition is the basis for the treatment of type 1 Gaucher disease by the glucosylceramide synthase (GCS) inhibitor eliglustat tartrate. However, the extended use of eliglustat and related compounds for the treatment of glycosphingolipid storage diseases with CNS manifestations is limited by the lack of brain penetration of this drug. Property modeling around the D-threo-1-phenyl-2-decanoylamino-3-morpholino-propanol (PDMP) pharmacophore was employed in a search for compounds of comparable activity against the GCS but lacking P-glycoprotein (MDR1) recognition. Modifications of the carboxamide N-acyl group were made to lower total polar surface area and rotatable bond number. Compounds were screened for inhibition of GCS in crude enzyme and whole cell assays and for MDR1 substrate recognition. One analog, 2-(2,3-dihydro-1H-inden-2-yl)-N-((1R,2R)-1-(2,3-dihydrobenzo[b][1,4]dioxin-6-yl)-1-hydroxy-3-(pyrrolidin-1-yl)propan-2-yl)acetamide (CCG-203586), was identified that inhibited GCS at low nanomolar concentrations with little to no apparent recognition by MDR1. Intraperitoneal administration of this compound to mice for 3 days resulted in a significant dose dependent decrease in brain glucosylceramide content, an effect not seen in mice dosed in parallel with eliglustat tartrate.