Complete Sequence, Analysis and Organization of the Orgyia leucostigma Nucleopolyhedrovirus Genome

The complete genome of the Orgyia leucostigma nucleopolyhedrovirus (OrleNPV) isolated from the whitemarked tussock moth (Orgyia leucostigma, Lymantridae: Lepidoptera) was sequenced, analyzed, and compared to other baculovirus genomes. The size of the OrleNPV genome was 156,179 base pairs (bp) and had a G+C content of 39%. The genome encoded 135 putative open reading frames (ORFs), which occupied 79% of the entire genome sequence. Three inhibitor of apoptosis (ORFs 16, 43 and 63), and five baculovirus repeated ORFs (bro-a through bro-e) were interspersed in the OrleNPV genome. In addition to six direct repeat (drs), a common feature shared among most baculoviruses, OrleNPV genome contained three homologous regions (hrs) that are located in the latter half of the genome. The presence of an F-protein homologue and the results from phylogenetic analyses placed OrleNPV in the genus Alphabaculovirus, group II. Overall, OrleNPV appears to be most closely related to group II alphabaculoviruses Ectropis obliqua (EcobNPV), Apocheima cinerarium (ApciNPV), Euproctis pseudoconspersa (EupsNPV), and Clanis bilineata (ClbiNPV)

Exome-wide association study to identify rare variants influencing COVID-19 outcomes : Results from the Host Genetics Initiative

Publisher Copyright: Copyright: © 2022 Butler-Laporte et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.Host genetics is a key determinant of COVID-19 outcomes. Previously, the COVID-19 Host Genetics Initiative genome-wide association study used common variants to identify multiple loci associated with COVID-19 outcomes. However, variants with the largest impact on COVID-19 outcomes are expected to be rare in the population. Hence, studying rare variants may provide additional insights into disease susceptibility and pathogenesis, thereby informing therapeutics development. Here, we combined whole-exome and whole-genome sequencing from 21 cohorts across 12 countries and performed rare variant exome-wide burden analyses for COVID-19 outcomes. In an analysis of 5,085 severe disease cases and 571,737 controls, we observed that carrying a rare deleterious variant in the SARS-CoV-2 sensor toll-like receptor TLR7 (on chromosome X) was associated with a 5.3-fold increase in severe disease (95% CI: 2.75–10.05, p = 5.41x10-7). This association was consistent across sexes. These results further support TLR7 as a genetic determinant of severe disease and suggest that larger studies on rare variants influencing COVID-19 outcomes could provide additional insights.Peer reviewe

Whole Exome Sequencing Reveals the Major Genetic Contributors to Nonsyndromic Tetralogy of Fallot

Rationale: Familial recurrence studies provide strong evidence for a genetic component to the predisposition to sporadic, nonsyndromic Tetralogy of Fallot (TOF), the most common cyanotic congenital heart disease phenotype. Rare genetic variants have been identified as important contributors to the risk of congenital heart disease, but relatively small numbers of TOF cases have been studied to date. Objective: We used whole exome sequencing to assess the prevalence of unique, deleterious variants in the largest cohort of nonsyndromic TOF patients reported to date. Methods and Results: Eight hundred twenty-nine TOF patients underwent whole exome sequencing. The presence of unique, deleterious variants was determined; defined by their absence in the Genome Aggregation Database and a scaled combined annotation-dependent depletion score of ≥20. The clustering of variants in 2 genes, NOTCH1 and FLT4, surpassed thresholds for genome-wide significance (assigned as P<5×10−8) after correction for multiple comparisons. NOTCH1 was most frequently found to harbor unique, deleterious variants. Thirty-one changes were observed in 37 probands (4.5%; 95% CI, 3.2%–6.1%) and included 7 loss-of-function variants 22 missense variants and 2 in-frame indels. Sanger sequencing of the unaffected parents of 7 cases identified 5 de novo variants. Three NOTCH1 variants (p.G200R, p.C607Y, and p.N1875S) were subjected to functional evaluation, and 2 showed a reduction in Jagged1-induced NOTCH signaling. FLT4 variants were found in 2.4% (95% CI, 1.6%–3.8%) of TOF patients, with 21 patients harboring 22 unique, deleterious variants. The variants identified were distinct to those that cause the congenital lymphoedema syndrome Milroy disease. In addition to NOTCH1, FLT4 and the well-established TOF gene, TBX1, we identified potential association with variants in several other candidates, including RYR1, ZFPM1, CAMTA2, DLX6, and PCM1. Conclusions: The NOTCH1 locus is the most frequent site of genetic variants predisposing to nonsyndromic TOF, followed by FLT4. Together, variants in these genes are found in almost 7% of TOF patients

Accuracy of Programs for the Determination of Human Leukocyte Antigen Alleles from Next-Generation Sequencing Data

The human leukocyte antigen (HLA) genes code for proteins that play a central role in the function of the immune system by presenting peptide antigens to T cells. As HLA genes show extremely high genetic polymorphism, HLA typing at the allele level is demanding and is based on DNA sequencing. Determination of HLA alleles is warranted as HLA alleles are major genetic risk factors in autoimmune diseases and are matched in transplantation. Here, we compared the accuracy of several published HLA-typing algorithms that are based on next-generation sequencing (NGS) data. As genome sequencing is becoming increasingly common in research, we wanted to test how well HLA alleles can be deduced from genome data produced in studies with objectives other than HLA typing and in platforms not especially designed for HLA typing. The accuracies were assessed using datasets consisting of NGS data produced using an in-house sequencing platform, including the full 4 Mbp HLA segment, from 94 stem cell transplantation patients and exome sequences from 63 samples of the 1000 Genomes collection. In the patient dataset, none of the software gave perfect results for all the samples and genes when programs were used with the default settings. However, we found that ensemble prediction of the results or modifications of the settings could be used to improve accuracy. For the exome-only data, most of the algorithms did not perform very well. The results indicate that the use of these algorithms for accurate HLA allele determination is not straightforward when based on NGS data not especially targeted to the HLA typing and their accurate use requires HLA expertise

Illness Perceptions Predict Cognitive Performance Validity

© 2018 The International Neuropsychological Society. Objectives: The aim of this study was to investigate the relationship of psychological variables to cognitive performance validity test (PVT) results in mixed forensic and nonforensic clinical samples. Methods: Participants included 183 adults who underwent comprehensive neuropsychological examination. Criterion groups were formed, that is, Credible Group or Noncredible Group, based upon their performance on the Word Memory Test and other stand-alone and embedded PVT measures. Results: Multivariate logistic regression analysis identified three significant predictors of cognitive performance validity. These included two psychological constructs, for example, Cogniphobia (perception that cognitive effort will exacerbate neurological symptoms), and Symptom Identity (perception that current symptoms are the result of illness or injury), and one contextual factor (forensic). While there was no interaction between these factors, elevated scores were most often observed in the forensic sample, suggesting that these independently contributing intrinsic psychological factors are more likely to occur in a forensic environment. Conclusions: Illness perceptions were significant predictors of cognitive performance validity particularly when they reached very elevated levels. Extreme elevations were more common among participants in the forensic sample, and potential reasons for this pattern are explored

Mouse ENU Mutagenesis to Understand Immunity to Infection: Methods, Selected Examples, and Perspectives

International audienc

The Copper CHARM Set: A New Set of Certified Reference Materials for the Standardization of Quantitative X-Ray Fluorescence Analysis of Heritage Copper Alloys

International audienceThis paper introduces a new set of certified reference materials designed to aid scientists and conservators working in cultural heritage fields with quantitative X-ray fluorescence analysis of historical and prehistoric copper alloys. This set has been designated as the Copper CHARM Set (Cultural Heritage Alloy Reference Material Set). The Copper CHARM Set is designed to be used by a wide range of museum-, art- and archaeology-oriented scientists and conservators to help improve the accuracy and range of their calibrations for quantitative ED–XRF spectrometry of copper alloys, and also increase the number of elements that can routinely be quantified. In addition, the common use of a single core set of the reference materials is designed to significantly improve inter-laboratory reproducibility, allowing greater data sharing between researchers and thus furthering possibilities for collaborative study

WEGS: a cost-effective sequencing method for genetic studies combining high-depth whole exome and low-depth whole genome

Whole genome sequencing (WGS) at high-depth (30X) allows the accurate discovery of variants in the coding and non-coding DNA regions and helps elucidate the genetic underpinnings of human health and diseases. Yet, due to the prohibitive cost of high-depth WGS, most large-scale genetic association studies use genotyping arrays or high-depth whole exome sequencing (WES). Here we propose a novel, cost-effective method, which we call “Whole Exome Genome Sequencing” (WEGS), that combines low-depth WGS and high-depth WES with up to 8 samples pooled and sequenced simultaneously (multiplexed). We experimentally assess the performance of WEGS with four different depth of coverage and sample multiplexing configurations. We show that the optimal WEGS configurations are 1.7-2.0 times cheaper than standard WES (no-plexing), 1.8-2.1 times cheaper than high-depth WGS, reach similar recall and precision rates in detecting coding variants as WES, and capture more population-specific variants in the rest of the genome that are difficult to recover when using genotype imputation methods. We apply WEGS to 862 patients with peripheral artery disease and show that it directly assesses more known disease-associated variants than a typical genotyping array and thousands of non-imputable variants per disease-associated locus

A cost-effective sequencing method for genetic studies combining high-depth whole exome and low-depth whole genome.

Whole genome sequencing (WGS) at high-depth (30X) allows the accurate discovery of variants in the coding and non-coding DNA regions and helps elucidate the genetic underpinnings of human health and diseases. Yet, due to the prohibitive cost of high-depth WGS, most large-scale genetic association studies use genotyping arrays or high-depth whole exome sequencing (WES). Here we propose a cost-effective method which we call "Whole Exome Genome Sequencing" (WEGS), that combines low-depth WGS and high-depth WES with up to 8 samples pooled and sequenced simultaneously (multiplexed). We experimentally assess the performance of WEGS with four different depth of coverage and sample multiplexing configurations. We show that the optimal WEGS configurations are 1.7-2.0 times cheaper than standard WES (no-plexing), 1.8-2.1 times cheaper than high-depth WGS, reach similar recall and precision rates in detecting coding variants as WES, and capture more population-specific variants in the rest of the genome that are difficult to recover when using genotype imputation methods. We apply WEGS to 862 patients with peripheral artery disease and show that it directly assesses more known disease-associated variants than a typical genotyping array and thousands of non-imputable variants per disease-associated locus