Jenkinson, Garrett

Abstract Clonal hematopoiesis (CH) of indeterminate potential (CHIP), driven by somatic mutations in leukemia-associated genes, confers increased risk of hematologic malignancies, cardiovascular disease, and all-cause mortality. In blood of healthy individuals, small CH clones can expand over time to reach 2% variant allele frequency (VAF), the current threshold for CHIP. Nevertheless, reliable detection of low-VAF CHIP mutations is challenging, often relying on deep targeted sequencing. Here, we present UNISOM, a streamlined workflow for enhancing CHIP detection from whole-genome and whole-exome sequencing data that are underpowered, especially for low VAFs. UNISOM utilizes a meta-caller for variant detection, in couple with machine learning models which classify variants into CHIP, germline, and artifact. In whole-exome sequencing data, UNISOM recovered nearly 80% of the CHIP mutations identified via deep targeted sequencing in the same cohort. Applied to whole-genome sequencing data from Mayo Clinic Biobank, it recapitulated the patterns previously established in much larger cohorts, including the most frequently mutated CHIP genes and predominant mutation types and signatures, as well as strong associations of CHIP with age and smoking status. Notably, 30% of the identified CHIP mutations had < 5% VAFs, demonstrating its high sensitivity toward small mutant clones. This workflow is applicable to CHIP screening in population genomic studies. The UNISOM pipeline is freely available at https://github.com/shulanmayo/UNISOM and https://ngdc.cncb.ac.cn/biocode/tool/7816.

Abstract Background Large β-globin gene cluster deletions (hereditary persistence of fetal hemoglobin [Hb] or β-, δβ-, γδβ-, and ϵγδβ-thalassemia), are associated with widely disparate phenotypes, including variable degrees of microcytic anemia and Hb F levels. When present, increased Hb A2 is used as a surrogate marker for β-thalassemia. Notably, ϵγδβ-thalassemias lack the essential regulatory locus control region (LCR) and cause severe transient perinatal anemia but normal newborn screen (NBS) results and Hb A2 levels. Herein, we report a novel deletion of the ϵ, Aγ, Gγ, and ψβ loci with intact LCR, δ-, and β-regions in 2 women and newborn twins. Methods Capillary electrophoresis (CE), high-performance liquid chromatography (HPLC), DNA sequencing, multiplex ligation-dependent probe amplification (MLPA), gap-polymerase chain reaction (gap-PCR), and long-read sequencing (LRS) were performed. Results NBS showed an Hb A > Hb F pattern for both twins. At 20 months, Hb A2 was increased similarly to that in the mother and an unrelated woman. Unexplained microcytosis was absent and the twins lacked severe neonatal anemia. MLPA, LRS, and gap-PCR confirmed a 32 599 base pair deletion of ϵ (HBE1) through ψβ (HBBP1) loci. Conclusions This deletion represents a hemoglobinopathy category with a distinct phenotype that has not been previously described, an ϵγ-thalassemia. Both the NBS Hb A > F pattern and the subsequent increased Hb A2 without microcytosis are unusual. A similar deletion should be considered when this pattern is encountered and appropriate test methods selected for detection. Knowledge of the clinical impact of this new category will improve genetic counselling, with distinction from the severe transient anemia associated with ϵγδβ-thalassemia.

Context.— Small case series have evaluated severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) detection in formalin-fixed, paraffin-embedded tissue using reverse transcription–polymerase chain reaction, immunohistochemistry (IHC), and/or RNA in situ hybridization (RNAish). Objective.— To compare droplet digital polymerase chain reaction, IHC, and RNAish to detect SARS-CoV-2 in formalin-fixed, paraffin-embedded tissue in a large series of lung specimens from coronavirus disease 2019 (COVID-19) patients. Design.— Droplet digital polymerase chain reaction and RNAish used commercially available probes; IHC used clone 1A9. Twenty-six autopsies of COVID-19 patients with formalin-fixed, paraffin-embedded tissue blocks of 62 lung specimens, 22 heart specimens, 2 brain specimens, and 1 liver, and 1 umbilical cord were included. Control cases included 9 autopsy lungs from patients with other infections/inflammation and virus-infected tissue or cell lines. Results.— Droplet digital polymerase chain reaction had the highest sensitivity for SARS-CoV-2 (96%) when compared with IHC (31%) and RNAish (36%). All 3 tests had a specificity of 100%. Agreement between droplet digital polymerase chain reaction and IHC or RNAish was fair (κ = 0.23 and κ = 0.35, respectively). Agreement between IHC and in situ hybridization was substantial (κ = 0.75). Interobserver reliability was almost perfect for IHC (κ = 0.91) and fair to moderate for RNAish (κ = 0.38–0.59). Lung tissues from patients who died earlier after onset of symptoms revealed higher copy numbers by droplet digital polymerase chain reaction (P = .03, Pearson correlation = −0.65) and were more likely to be positive by RNAish (P = .02) than lungs from patients who died later. We identified SARS-CoV-2 in hyaline membranes, in pneumocytes, and rarely in respiratory epithelium. Droplet digital polymerase chain reaction showed low copy numbers in 7 autopsy hearts from ProteoGenex Inc. All other extrapulmonary tissues were negative. Conclusions.— Droplet digital polymerase chain reaction was the most sensitive and highly specific test to identify SARS-CoV-2 in lung specimens from COVID-19 patients.

Abstract Motivation Next-generation sequencing is rapidly improving diagnostic rates in rare Mendelian diseases, but even with whole genome or whole exome sequencing, the majority of cases remain unsolved. Increasingly, RNA sequencing is being used to solve many cases that evade diagnosis through sequencing alone. Specifically, the detection of aberrant splicing in many rare disease patients suggests that identifying RNA splicing outliers is particularly useful for determining causal Mendelian disease genes. However, there is as yet a paucity of statistical methodologies to detect splicing outliers. Results We developed LeafCutterMD, a new statistical framework that significantly improves the previously published LeafCutter in the context of detecting outlier splicing events. Through simulations and analysis of real patient data, we demonstrate that LeafCutterMD has better power than the state-of-the-art methodology while controlling false-positive rates. When applied to a cohort of disease-affected probands from the Mayo Clinic Center for Individualized Medicine, LeafCutterMD recovered all aberrantly spliced genes that had previously been identified by manual curation efforts. Availability and implementation The source code for this method is available under the opensource Apache 2.0 license in the latest release of the LeafCutter software package available online at http://davidaknowles.github.io/leafcutter. Supplementary information Supplementary data are available at Bioinformatics online.