Oncolytic effect of SARS-CoV-2 in a patient with mycosis fungoides (MF): a case report

The most common variant of cutaneous T-cell lymphomas (CTCL) is mycosis fungoides (MF).The spontaneous regression (SR) of MF is rare. Here, we are reporting an interesting case of refractory MF after COVID-19. The SARS-CoV-2 could be an essential component in the improvement of clinical features related to MF.

A combined polygenic score of 21,293 rare and 22 common variants significantly improves diabetes diagnosis based on hemoglobin A1C levels

Polygenic scores (PS), constructed from the combined effects of many genetic variants, have been shown to predict risk or treatment strategies for certain common diseases. As most PS to date are based on common variants, the benefit of adding rare variation to PS remains largely unknown and methodically challenging. We developed and validated a novel method for constructing a rare variant PS and applied it to a previously identified clinical scenario, in which genetic variants modify the hemoglobin A1C (HbA1C) threshold recommended for type 2 diabetes (T2D) diagnosis. The resultant rare variant PS is highly polygenic (21,293 variants across 144 genes), depends on ultra-rare variants (72.7% of variants observed in <3 people), and identifies significantly more undiagnosed T2D cases than expected by chance (OR=2.71, p=1.51x10-6). A model combining the rare variant PS with a previously published common variant PS is expected to identify 4.9M misdiagnosed T2D cases in the USA, nearly 1.5-fold more than the common variant PS alone. These results provide a method for constructing complex phenotype PS from rare variants and suggest that rare variants will augment common variants in precision medicine approaches for common disease.

Adjusting for Common Variant Polygenic Scores Improves Yield in Rare Variant Association Analyses

With the emergence of large-scale sequencing data, methods for improving power in rare variant analyses (RVAT) are needed. Here, we show that adjusting for common variant polygenic scores improves the yield in gene-based RVAT across 65 quantitative traits in the UK Biobank (up to 20% increase at α=2.6x10-6), without a marked increase in false-positive rates or genomic inflation. Our results illustrate how adjusting for common variant effects can aid in rare variant association discovery.

Being Easy to Communicate Might Make Verdicts Based on Confessions More Legitimate

In many judicial systems, confessions are a requirement for criminal conviction. Even if confessions are intrinsically convincing, this might not entirely explain why they play such a paramount role. In addition, it has been suggested that confessions owe their importance to their legitimizing role, explaining why they could be required even when other evidence has convinced a judge. But why would confessions be particularly suited to justify verdicts? One possibility is that they can be more easily transmitted from one individual to the next, and thus spread in the population without losing their convincingness. 360 English-speaking participants were asked to evaluate the convincingness of one of three justifications for a verdict, grounded either in a confession, eyewitnesses, or circumstantial evidence, and to pass on that justification to another participant, who performed the same task. Then, 240 English-speaking participants evaluated the convincingness of some of the justifications produced by the first group of participants. Compared to the other justifications, justifications based on confessions lost less of their convincingness in the transmission process (small to medium effect sizes). Modeling pointed to the most common forms the justifications would take as they are transmitted, and results showed that the most common variant of the justification based on a confession was more convincing (small to medium effect sizes).

Using brain cell-type-specific protein interactomes to interpret genetic data in schizophrenia

Genetics have nominated many schizophrenia risk genes that lack functional interpretation. To empower such interpretation, we executed interaction proteomics for six risk genes in human induced neurons and found the resulting protein network to be enriched for common variant risk of schizophrenia in Europeans and East Asians. The network is down-regulated in layer 5/6 cortical neurons of patients and can complement fine-mapping and eQTL data to prioritize additional genes in GWAS loci. A sub-network centered on HCN1 is enriched for common variant risk and also contains proteins (HCN4 and AKAP11) enriched for rare protein-truncating mutations in patients with schizophrenia and bipolar disease. Our findings establish brain cell-type-specific interactomes as an organizing framework to facilitate interpretation of genetic and transcriptomic data in schizophrenia and psychiatric diseases.One Sentence SummaryNeuronal protein interactomes is an organizing framework for integrating genetic and transcriptomic data in schizophrenia.

Performance Comparison of Computational Prediction Methods for the Function and Pathogenicity of Non-coding Variants

Non-coding variants in the human genome greatly influence some traits and complex diseases by their own regulation and modification effects. Hence, an increasing number of computational methods are developed to predict the effects of variants in the human non-coding sequences. However, it is difficult for users with insufficient knowledge about the performances of computational methods to select appropriate computational methods from dozens of methods. In order to solve this problem, we assessed 12 performance measures of 24 methods on four independent non-coding variant benchmark datasets: (Ⅰ) rare germline variant from ClinVar, (Ⅱ) rare somatic variant from COSMIC, (Ⅲ) common regulatory variant dataset, and (Ⅳ) disease associated common variant dataset. All 24 tested methods performed differently under various conditions, indicating that these methods have varying strengths and weaknesses under different scenarios. Importantly, the performance of existing methods was acceptable in the rare germline variant from ClinVar with area under curves (AUCs) of 0.4481 - 0.8033 and poor in the rare somatic variant from COSMIC (AUCs: 0.4984 - 0.7131), common regulatory variant dataset (AUCs: 0.4837 - 0.6472), and disease associated common variant dataset (AUCs: 0.4766 -0.5188). We also compared the prediction performance among 24 methods for non-coding de novo mutations in autism spectrum disorder and found that the CADD and CDTS methods showed better performance. Summarily, we assessed the performances of 24 computational methods under diverse scenarios, providing preliminary advice for proper tool selection and new method development in interpreting non-coding variants.

Whole-genome association analyses of sleep-disordered breathing phenotypes in the NHLBI TOPMed program

Background Sleep-disordered breathing is a common disorder associated with significant morbidity. The genetic architecture of sleep-disordered breathing remains poorly understood. Through the NHLBI Trans-Omics for Precision Medicine (TOPMed) program, we performed the first whole-genome sequence analysis of sleep-disordered breathing. Methods The study sample was comprised of 7988 individuals of diverse ancestry. Common-variant and pathway analyses included an additional 13,257 individuals. We examined five complementary traits describing different aspects of sleep-disordered breathing: the apnea-hypopnea index, average oxyhemoglobin desaturation per event, average and minimum oxyhemoglobin saturation across the sleep episode, and the percentage of sleep with oxyhemoglobin saturation < 90%. We adjusted for age, sex, BMI, study, and family structure using MMSKAT and EMMAX mixed linear model approaches. Additional bioinformatics analyses were performed with MetaXcan, GIGSEA, and ReMap. Results We identified a multi-ethnic set-based rare-variant association (p = 3.48 × 10−8) on chromosome X with ARMCX3. Additional rare-variant associations include ARMCX3-AS1, MRPS33, and C16orf90. Novel common-variant loci were identified in the NRG1 and SLC45A2 regions, and previously associated loci in the IL18RAP and ATP2B4 regions were associated with novel phenotypes. Transcription factor binding site enrichment identified associations with genes implicated with respiratory and craniofacial traits. Additional analyses identified significantly associated pathways. Conclusions We have identified the first gene-based rare-variant associations with objectively measured sleep-disordered breathing traits. Our results increase the understanding of the genetic architecture of sleep-disordered breathing and highlight associations in genes that modulate lung development, inflammation, respiratory rhythmogenesis, and HIF1A-mediated hypoxic response.