While pathogenic variants can significantly increase disease risk, it is still challenging to estimate the clinical impact of rare missense variants more generally. Even in genes such as BRCA2 or ...PALB2, large cohort studies find no significant association between breast cancer and rare missense variants collectively. Here, we introduce REGatta, a method to estimate clinical risk from variants in smaller segments of individual genes. We first define these regions by using the density of pathogenic diagnostic reports and then calculate the relative risk in each region by using over 200,000 exome sequences in the UK Biobank. We apply this method in 13 genes with established roles across several monogenic disorders. In genes with no significant difference at the gene level, this approach significantly separates disease risk for individuals with rare missense variants at higher or lower risk (BRCA2 regional model OR = 1.46 1.12, 1.79, p = 0.0036 vs. BRCA2 gene model OR = 0.96 0.85, 1.07 p = 0.4171). We find high concordance between these regional risk estimates and high-throughput functional assays of variant impact. We compare our method with existing methods and the use of protein domains (Pfam) as regions and find REGatta better identifies individuals at elevated or reduced risk. These regions provide useful priors and are potentially useful for improving risk assessment for genes associated with monogenic diseases.
Methods to identify regions of genes that are most functionally impactful have relied on structural, evolutionary, and population data. We extend these approaches with REGatta, a method to estimate the clinical risk conferred by variants in regions of genes with established disease phenotypes using diagnostic and population cohort data.
Prime editing enables search-and-replace genome editing but is limited by low editing efficiency. We present a high-throughput approach, the Peptide Self-Editing sequencing assay (PepSEq), to measure ...how fusion of 12,000 85-amino acid peptides influences prime editing efficiency. We show that peptide fusion can enhance prime editing, prime-enhancing peptides combine productively, and a top dual peptide-prime editor increases prime editing significantly in multiple cell lines across dozens of target sites. Top prime-enhancing peptides function by increasing translation efficiency and serve as broadly useful tools to improve prime editing efficiency.
This study aimed to explore whether evidence of pathogenicity from prior variant classifications in ClinVar could be used to inform variant interpretation using the American College of Medical ...Genetics and Genomics/Association for Molecular Pathology clinical guidelines.
We identified distinct single-nucleotide variants (SNVs) that are either similar in location or in functional consequence to pathogenic variants in ClinVar and analyzed evidence in support of pathogenicity using 3 interpretation criteria.
Thousands of variants, including many in clinically actionable disease genes (American College of Medical Genetics and Genomics secondary findings v3.0), have evidence of pathogenicity from existing variant classifications, accounting for 2.5% of nonsynonymous SNVs within ClinVar. Notably, there are many variants with uncertain or conflicting classifications that cause the same amino acid substitution as other pathogenic variants (PS1, N = 323), variants that are predicted to cause different amino acid substitutions in the same codon as pathogenic variants (PM5, N = 7692), and loss-of-function variants that are present in genes in which many loss-of-function variants are classified as pathogenic (PVS1, N = 3635). Most of these variants have similar computational predictions of pathogenicity and splicing effect as their associated pathogenic variants.
Broadly, for >1.4 million SNVs exome wide, information from previously classified variants could be used to provide evidence of pathogenicity. We have developed a pipeline to identify variants meeting these criteria that may inform interpretation efforts.
Delirium is associated with worse outcomes in patients with stroke and neurocritical illness, but delirium detection in these patients can be challenging with existing screening tools. To address ...this gap, we aimed to develop and evaluate machine learning models that detect episodes of post-stroke delirium based on data from wearable activity monitors in conjunction with stroke-related clinical features.
Prospective observational cohort study.
Neurocritical Care and Stroke Units at an academic medical center.
We recruited 39 patients with moderate-to-severe acute intracerebral hemorrhage (ICH) and hemiparesis over a 1-year period mean (SD) age 71.3 (12.20), 54% male, median (IQR) initial NIH Stroke Scale 14.5 (6), median (IQR) ICH score 2 (1).
Each patient received daily assessments for delirium by an attending neurologist, while activity data were recorded throughout each patient's hospitalization using wrist-worn actigraph devices (on both paretic and non-paretic arms). We compared the predictive accuracy of Random Forest, SVM and XGBoost machine learning methods in classifying daily delirium status using clinical information alone and combined with actigraph data. Among our study cohort, 85% of patients (
= 33) had at least one delirium episode, while 71% of monitoring days (
= 209) were rated as days with delirium. Clinical information alone had a low accuracy in detecting delirium on a day-to-day basis accuracy mean (SD) 62% (18%), F1 score mean (SD) 50% (17%). Prediction performance improved significantly (
< 0.001) with the addition of actigraph data accuracy mean (SD) 74% (10%), F1 score 65% (10%). Among actigraphy features, night-time actigraph data were especially relevant for classification accuracy.
We found that actigraphy in conjunction with machine learning models improves clinical detection of delirium in patients with stroke, thus paving the way to make actigraph-assisted predictions clinically actionable.
Genetic variation contributes greatly to LDL cholesterol (LDL-C) levels and coronary artery disease risk. By combining analysis of rare coding variants from the UK Biobank and genome-scale ...CRISPR-Cas9 knockout and activation screening, we substantially improve the identification of genes whose disruption alters serum LDL-C levels. We identify 21 genes in which rare coding variants significantly alter LDL-C levels at least partially through altered LDL-C uptake. We use co-essentiality-based gene module analysis to show that dysfunction of the RAB10 vesicle transport pathway leads to hypercholesterolemia in humans and mice by impairing surface LDL receptor levels. Further, we demonstrate that loss of function of OTX2 leads to robust reduction in serum LDL-C levels in mice and humans by increasing cellular LDL-C uptake. Altogether, we present an integrated approach that improves our understanding of the genetic regulators of LDL-C levels and provides a roadmap for further efforts to dissect complex human disease genetics.
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•Genome-scale CRISPR screens identify 490 genes that alter liver cell LDL-C uptake•21 LDL-C uptake-altering genes enriched in rare coding variants in UK Biobank exomes•Dysfunction in the RAB10 exocytosis pathway raises LDL-C levels in mice and humans•OTX2 loss of function robustly reduces serum LDL-C in mice and humans
By combining analysis of rare coding variants from the UK Biobank and genome-scale CRISPR-Cas9 knockout and activation screening, Hamilton et al. improve the identification of genes whose disruption alters serum LDL-C levels. They show that RAB10 vesicle transport pathway dysfunction leads to hypercholesterolemia and that OTX2 disruption reduces serum LDL-C levels.
This volume in the Routledge Language Family Descriptions series describes, in depth, Celtic languages from historical, structural, & sociolinguistic perspectives with individual chapters on Irish, ...Scottish Gaelic, Manx, Welsh, Breton, & Cornish. The book consists of four parts. Part 1 discusses the origin & history of Celtic languages, their spread & retreat, present-day distribution, & typological profiles of the extant & recently extant languages. Parts 2 & 3 devote whole chapters to the description of the structural properties of each of the neo-Celtic languages, including their phonology, mutation, morphology, syntax, lexicon, & dialectology. Part 4 addresses sociolinguistic details identifying areal distribution, maintenance, & survival prospects. Z. Dubiel
