Minimum information and guidelines for reporting a multiplexed assay of variant effect

Multiplexed assays of variant effect (MAVEs) have emerged as a powerful approach for interrogating thousands of genetic variants in a single experiment. The flexibility and widespread adoption of these techniques across diverse disciplines have led to a heterogeneous mix of data formats and descriptions, which complicates the downstream use of the resulting datasets. To address these issues and promote reproducibility and reuse of MAVE data, we define a set of minimum information standards for MAVE data and metadata and outline a controlled vocabulary aligned with established biomedical ontologies for describing these experimental designs.

Genetically guided precision medicine clinical decision support tools: a systematic review

OBJECTIVES

Patient care using genetics presents complex challenges. Clinical decision support (CDS) tools are a potential solution because they provide patient-specific risk assessments and/or recommendations at the point of care. This systematic review evaluated the literature on CDS systems which have been implemented to support genetically guided precision medicine (GPM).

MATERIALS AND METHODS

A comprehensive search was conducted in MEDLINE and Embase, encompassing January 1, 2011-March 14, 2023. The review included primary English peer-reviewed research articles studying humans, focused on the use of computers to guide clinical decision-making and delivering genetically guided, patient-specific assessments, and/or recommendations to healthcare providers and/or patients.

RESULTS

The search yielded 3832 unique articles. After screening, 41 articles were identified that met the inclusion criteria. Alerts and reminders were the most common form of CDS used. About 27 systems were integrated with the electronic health record; 2 of those used standards-based approaches for genomic data transfer. Three studies used a framework to analyze the implementation strategy.

DISCUSSION

Findings include limited use of standards-based approaches for genomic data transfer, system evaluations that do not employ formal frameworks, and inconsistencies in the methodologies used to assess genetic CDS systems and their impact on patient outcomes.

CONCLUSION

We recommend that future research on CDS system implementation for genetically GPM should focus on implementing more CDS systems, utilization of standards-based approaches, user-centered design, exploration of alternative forms of CDS interventions, and use of formal frameworks to systematically evaluate genetic CDS systems and their effects on patient care.

Measuring, visualizing, and diagnosing reference bias with biastools

Many bioinformatics methods seek to reduce reference bias, but no methods exist to comprehensively measure it. Biastools analyzes and categorizes instances of reference bias. It works in various scenarios: when the donor's variants are known and reads are simulated; when donor variants are known and reads are real; and when variants are unknown and reads are real. Using biastools, we observe that more inclusive graph genomes result in fewer biased sites. We find that end-to-end alignment reduces bias at indels relative to local aligners. Finally, we use biastools to characterize how T2T references improve large-scale bias.

The Brain Gene Registry: a data snapshot

Monogenic disorders account for a large proportion of population-attributable risk for neurodevelopmental disabilities. However, the data necessary to infer a causal relationship between a given genetic variant and a particular neurodevelopmental disorder is often lacking. Recognizing this scientific roadblock, 13 Intellectual and Developmental Disabilities Research Centers (IDDRCs) formed a consortium to create the Brain Gene Registry (BGR), a repository pairing clinical genetic data with phenotypic data from participants with variants in putative brain genes. Phenotypic profiles are assembled from the electronic health record (EHR) and a battery of remotely administered standardized assessments collectively referred to as the Rapid Neurobehavioral Assessment Protocol (RNAP), which include cognitive, neurologic, and neuropsychiatric assessments, as well as assessments for attention deficit hyperactivity disorder (ADHD) and autism spectrum disorder (ASD). Co-enrollment of BGR participants in the Clinical Genome Resource's (ClinGen's) GenomeConnect enables display of variant information in ClinVar. The BGR currently contains data on 479 participants who are 55% male, 6% Asian, 6% Black or African American, 76% white, and 12% Hispanic/Latine. Over 200 genes are represented in the BGR, with 12 or more participants harboring variants in each of these genes: CACNA1A, DNMT3A, SLC6A1, SETD5, and MYT1L. More than 30% of variants are de novo and 43% are classified as variants of uncertain significance (VUSs). Mean standard scores on cognitive or developmental screens are below average for the BGR cohort. EHR data reveal developmental delay as the earliest and most common diagnosis in this sample, followed by speech and language disorders, ASD, and ADHD. BGR data has already been used to accelerate gene-disease validity curation of 36 genes evaluated by ClinGen's BGR Intellectual Disability (ID)-Autism (ASD) Gene Curation Expert Panel. In summary, the BGR is a resource for use by stakeholders interested in advancing translational research for brain genes and continues to recruit participants with clinically reported variants to establish a rich and well-characterized national resource to promote research on neurodevelopmental disorders.

DeLIVR: a deep learning approach to IV regression for testing nonlinear causal effects in transcriptome-wide association studies

Transcriptome-wide association studies (TWAS) have been increasingly applied to identify (putative) causal genes for complex traits and diseases. TWAS can be regarded as a two-sample two-stage least squares method for instrumental variable (IV) regression for causal inference. The standard TWAS (called TWAS-L) only considers a linear relationship between a gene's expression and a trait in stage 2, which may lose statistical power when not true. Recently, an extension of TWAS (called TWAS-LQ) considers both the linear and quadratic effects of a gene on a trait, which however is not flexible enough due to its parametric nature and may be low powered for nonquadratic nonlinear effects. On the other hand, a deep learning (DL) approach, called DeepIV, has been proposed to nonparametrically model a nonlinear effect in IV regression. However, it is both slow and unstable due to the ill-posed inverse problem of solving an integral equation with Monte Carlo approximations. Furthermore, in the original DeepIV approach, statistical inference, that is, hypothesis testing, was not studied. Here, we propose a novel DL approach, called DeLIVR, to overcome the major drawbacks of DeepIV, by estimating a related but different target function and including a hypothesis testing framework. We show through simulations that DeLIVR was both faster and more stable than DeepIV. We applied both parametric and DL approaches to the GTEx and UK Biobank data, showcasing that DeLIVR detected additional 8 and 7 genes nonlinearly associated with high-density lipoprotein (HDL) cholesterol and low-density lipoprotein (LDL) cholesterol, respectively, all of which would be missed by TWAS-L, TWAS-LQ, and DeepIV; these genes include BUD13 associated with HDL, SLC44A2 and GMIP with LDL, all supported by previous studies.

Evaluating and improving the representation of bacterial contents in long-read metagenome assemblies

BACKGROUND

In the metagenomic assembly of a microbial community, abundant species are often thought to assemble well given their deeper sequencing coverage. This conjuncture is rarely tested or evaluated in practice. We often do not know how many abundant species are missing and do not have an approach to recover them.

RESULTS

Here, we propose k-mer based and 16S RNA based methods to measure the completeness of metagenome assembly. We show that even with PacBio high-fidelity (HiFi) reads, abundant species are often not assembled, as high strain diversity may lead to fragmented contigs. We develop a novel reference-free algorithm to recover abundant metagenome-assembled genomes (MAGs) by identifying circular assembly subgraphs. Complemented with a reference-free genome binning heuristics based on dimension reduction, the proposed method rescues many abundant species that would be missing with existing methods and produces competitive results compared to those state-of-the-art binners in terms of total number of near-complete genome bins.

CONCLUSIONS

Our work emphasizes the importance of metagenome completeness, which has often been overlooked. Our algorithm generates more circular MAGs and moves a step closer to the complete representation of microbial communities.

Ancestry-specific high-risk gene variant profiling unmasks diabetes-associated genes

How ancestry-associated genetic variance affects disparities in the risk of polygenic diseases and influences the identification of disease-associated genes warrants a deeper understanding. We hypothesized that the discovery of genes associated with polygenic diseases may be limited by the overreliance on single-nucleotide polymorphism (SNP)-based genomic investigation, as most significant variants identified in genome-wide SNP association studies map to introns and intergenic regions of the genome. To overcome such potential limitations, we developed a gene-constrained, function-based analytical method centered on high-risk variants (hrV) that encode frameshifts, stopgains or splice site disruption. We analyzed the total number of hrV per gene in populations of different ancestry, representing a total of 185 934 subjects. Using this analysis, we developed a quantitative index of hrV (hrVI) across 20 428 genes within each population. We then applied hrVI analysis to the discovery of genes associated with type 2 diabetes mellitus (T2DM), a polygenic disease with ancestry-related disparity. HrVI profiling and gene-to-gene comparisons of ancestry-specific hrV between the case (20 781 subjects) and control (24 440 subjects) populations in the T2DM national repository identified 57 genes associated with T2DM, 40 of which were discoverable only by ancestry-specific analysis. These results illustrate how a function-based, ancestry-specific analysis of genetic variations can accelerate the identification of genes associated with polygenic diseases. Besides T2DM, such analysis may facilitate our understanding of the genetic basis for other polygenic diseases that are also greatly influenced by environmental and behavioral factors, such as obesity, hypertension and Alzheimer's disease.

Survival-Associated Cellular Response Maintained in Pancreatic Ductal Adenocarcinoma (PDAC) Switched Between Soft and Stiff 3D Microgel Culture

Pancreatic ductal adenocarcinoma (PDAC) accounts for about 90% of all pancreatic cancer cases. Five-year survival rates have remained below 12% since the 1970s, in part due to the difficulty in detection prior to metastasis (migration and invasion into neighboring organs and glands). Mechanical memory is a concept that has emerged over the past decade that may provide a path toward understanding how invading PDAC cells "remember" the mechanical properties of their diseased ("stiff", elastic modulus, E ≈ 10 kPa) microenvironment even while invading a healthy ("soft", E ≈ 1 kPa) microenvironment. Here, we investigated the role of mechanical priming by culturing a dilute suspension of PDAC (FG) cells within a 3D, rheologically tunable microgel platform from hydrogels with tunable mechanical properties. We conducted a suite of acute (short-term) priming studies where we cultured PDAC cells in either a soft (E ≈ 1 kPa) or stiff (E ≈ 10 kPa) environment for 6 h, then removed and placed them into a new soft or stiff 3D environment for another 18 h. Following these steps, we conducted RNA-seq analyses to quantify gene expression. Initial priming in the 3D culture showed persistent gene expression for the duration of the study, regardless of the subsequent environments (stiff or soft). Stiff 3D culture was associated with the downregulation of tumor suppressors (LATS1, BCAR3, CDKN2C), as well as the upregulation of cancer-associated genes (RAC3). Immunofluorescence staining (BCAR3, RAC3) further supported the persistence of this cellular response, with BCAR3 upregulated in soft culture and RAC3 upregulated in stiff-primed culture. Stiff-primed genes were stratified against patient data found in The Cancer Genome Atlas (TCGA). Upregulated genes in stiff-primed 3D culture were associated with decreased survival in patient data, suggesting a link between patient survival and mechanical priming.

Post-transcriptional splicing can occur in a slow-moving zone around the gene

Splicing is the stepwise molecular process by which introns are removed from pre-mRNA and exons are joined together to form mature mRNA sequences. The ordering and spatial distribution of these steps remain controversial, with opposing models suggesting splicing occurs either during or after transcription. We used single-molecule RNA FISH, expansion microscopy, and live-cell imaging to reveal the spatiotemporal distribution of nascent transcripts in mammalian cells. At super-resolution levels, we found that pre-mRNA formed clouds around the transcription site. These clouds indicate the existence of a transcription-site-proximal zone through which RNA move more slowly than in the nucleoplasm. Full-length pre-mRNA undergo continuous splicing as they move through this zone following transcription, suggesting a model in which splicing can occur post-transcriptionally but still within the proximity of the transcription site, thus seeming co-transcriptional by most assays. These results may unify conflicting reports of co-transcriptional versus post-transcriptional splicing.

Effects of parental age and polymer composition on short tandem repeat de novo mutation rates

Short tandem repeats (STRs) are hotspots of genomic variability in the human germline because of their high mutation rates, which have long been attributed largely to polymerase slippage during DNA replication. This model suggests that STR mutation rates should scale linearly with a father's age, as progenitor cells continually divide after puberty. In contrast, it suggests that STR mutation rates should not scale with a mother's age at her child's conception, since oocytes spend a mother's reproductive years arrested in meiosis II and undergo a fixed number of cell divisions that are independent of the age at ovulation. Yet, mirroring recent findings, we find that STR mutation rates covary with paternal and maternal age, implying that some STR mutations are caused by DNA damage in quiescent cells rather than polymerase slippage in replicating progenitor cells. These results echo the recent finding that DNA damage in oocytes is a significant source of de novo single nucleotide variants and corroborate evidence of STR expansion in postmitotic cells. However, we find that the maternal age effect is not confined to known hotspots of oocyte mutagenesis, nor are postzygotic mutations likely to contribute significantly. STR nucleotide composition demonstrates divergent effects on de novo mutation (DNM) rates between sexes. Unlike the paternal lineage, maternally derived DNMs at A/T STRs display a significantly greater association with maternal age than DNMs at G/C-containing STRs. These observations may suggest the mechanism and developmental timing of certain STR mutations and contradict prior attribution of replication slippage as the primary mechanism of STR mutagenesis.

Conserved Noncoding Elements Evolve Around the Same Genes Throughout Metazoan Evolution

Conserved noncoding elements (CNEs) are DNA sequences located outside of protein-coding genes that can remain under purifying selection for up to hundreds of millions of years. Studies in vertebrate genomes have revealed that most CNEs carry out regulatory functions. Notably, many of them are enhancers that control the expression of homeodomain transcription factors and other genes that play crucial roles in embryonic development. To further our knowledge of CNEs in other parts of the animal tree, we conducted a large-scale characterization of CNEs in more than 50 genomes from three of the main branches of the metazoan tree: Cnidaria, Mollusca, and Arthropoda. We identified hundreds of thousands of CNEs and reconstructed the temporal dynamics of their appearance in each lineage, as well as determining their spatial distribution across genomes. We show that CNEs evolve repeatedly around the same genes across the Metazoa, including around homeodomain genes and other transcription factors; they also evolve repeatedly around genes involved in neural development. We also show that transposons are a major source of CNEs, confirming previous observations from vertebrates and suggesting that they have played a major role in wiring developmental gene regulatory mechanisms since the dawn of animal evolution.

SAMHD1 compound heterozygous rare variants associated with moyamoya and mitral valve disease in the absence of other features of Aicardi-Goutières syndrome

Aicardi-Goutières syndrome (AGS) is an autosomal recessive inflammatory syndrome that manifests as an early-onset encephalopathy with both neurologic and extraneurologic clinical findings. AGS has been associated with pathogenic variants in nine genes: TREX1, RNASEH2B, RNASEH2C, RNASEH2A, SAMHD1, ADAR, IFIH1, LSM11, and RNU7-1. Diagnosis is established by clinical findings (encephalopathy and acquired microcephaly, intellectual and physical impairments, dystonia, hepatosplenomegaly, sterile pyrexia, and/or chilblains), characteristic abnormalities on cranial CT (calcification of the basal ganglia and white matter) and MRI (leukodystrophic changes), or the identification of pathogenic/likely pathogenic variants in the known genes. One of the genes associated with AGS, SAMHD1, has also been associated with a spectrum of cerebrovascular diseases, including moyamoya disease (MMD). In this report, we describe a 31-year-old male referred to genetics for MMD since childhood who lacked the hallmark features of AGS patients but was found to have compound heterozygous SAMHD1 variants. He later developed mitral valve insufficiency due to recurrent chordal rupture and ultimately underwent a heart transplant at 37 years of age. Thus, these data suggest that SAMHD1 pathogenic variants can cause MMD without typical AGS symptoms and support that SAMHD1 should be assessed in MMD patients even in the absence of AGS features.

Pacybara: accurate long-read sequencing for barcoded mutagenized allelic libraries

MOTIVATION

Long-read sequencing technologies, an attractive solution for many applications, often suffer from higher error rates. Alignment of multiple reads can improve base-calling accuracy, but some applications, e.g. sequencing mutagenized libraries where multiple distinct clones differ by one or few variants, require the use of barcodes or unique molecular identifiers. Unfortunately, sequencing errors can interfere with correct barcode identification, and a given barcode sequence may be linked to multiple independent clones within a given library.

RESULTS

Here we focus on the target application of sequencing mutagenized libraries in the context of multiplexed assays of variant effects (MAVEs). MAVEs are increasingly used to create comprehensive genotype-phenotype maps that can aid clinical variant interpretation. Many MAVE methods use long-read sequencing of barcoded mutant libraries for accurate association of barcode with genotype. Existing long-read sequencing pipelines do not account for inaccurate sequencing or nonunique barcodes. Here, we describe Pacybara, which handles these issues by clustering long reads based on the similarities of (error-prone) barcodes while also detecting barcodes that have been associated with multiple genotypes. Pacybara also detects recombinant (chimeric) clones and reduces false positive indel calls. In three example applications, we show that Pacybara identifies and correctly resolves these issues.

AVAILABILITY AND IMPLEMENTATION

Pacybara, freely available at https://github.com/rothlab/pacybara, is implemented using R, Python, and bash for Linux. It runs on GNU/Linux HPC clusters via Slurm, PBS, or GridEngine schedulers. A single-machine simplex version is also available.

Depletion of lamins B1 and B2 promotes chromatin mobility and induces differential gene expression by a mesoscale-motion-dependent mechanism

BACKGROUND

B-type lamins are critical nuclear envelope proteins that interact with the three-dimensional genomic architecture. However, identifying the direct roles of B-lamins on dynamic genome organization has been challenging as their joint depletion severely impacts cell viability. To overcome this, we engineered mammalian cells to rapidly and completely degrade endogenous B-type lamins using Auxin-inducible degron technology.

RESULTS

Using live-cell Dual Partial Wave Spectroscopic (Dual-PWS) microscopy, Stochastic Optical Reconstruction Microscopy (STORM), in situ Hi-C, CRISPR-Sirius, and fluorescence in situ hybridization (FISH), we demonstrate that lamin B1 and lamin B2 are critical structural components of the nuclear periphery that create a repressive compartment for peripheral-associated genes. Lamin B1 and lamin B2 depletion minimally alters higher-order chromatin folding but disrupts cell morphology, significantly increases chromatin mobility, redistributes both constitutive and facultative heterochromatin, and induces differential gene expression both within and near lamin-associated domain (LAD) boundaries. Critically, we demonstrate that chromatin territories expand as upregulated genes within LADs radially shift inwards. Our results indicate that the mechanism of action of B-type lamins comes from their role in constraining chromatin motion and spatial positioning of gene-specific loci, heterochromatin, and chromatin domains.

CONCLUSIONS

Our findings suggest that, while B-type lamin degradation does not significantly change genome topology, it has major implications for three-dimensional chromatin conformation at the single-cell level both at the lamina-associated periphery and the non-LAD-associated nuclear interior with concomitant genome-wide transcriptional changes. This raises intriguing questions about the individual and overlapping roles of lamin B1 and lamin B2 in cellular function and disease.

SUMMIT-FA: a new resource for improved transcriptome imputation using functional annotations

Transcriptome-wide association studies (TWAS) integrate gene expression prediction models and genome-wide association studies (GWAS) to identify gene-trait associations. The power of TWAS is determined by the sample size of GWAS and the accuracy of the expression prediction model. Here, we present a new method, the Summary-level Unified Method for Modeling Integrated Transcriptome using Functional Annotations (SUMMIT-FA), which improves gene expression prediction accuracy by leveraging functional annotation resources and a large expression quantitative trait loci (eQTL) summary-level dataset. We build gene expression prediction models in whole blood using SUMMIT-FA with the comprehensive functional database MACIE and eQTL summary-level data from the eQTLGen consortium. We apply these models to GWAS for 24 complex traits and show that SUMMIT-FA identifies significantly more gene-trait associations and improves predictive power for identifying "silver standard" genes compared to several benchmark methods. We further conduct a simulation study to demonstrate the effectiveness of SUMMIT-FA.

Natural language processing to identify lupus nephritis phenotype in electronic health records

BACKGROUND

Systemic lupus erythematosus (SLE) is a rare autoimmune disorder characterized by an unpredictable course of flares and remission with diverse manifestations. Lupus nephritis, one of the major disease manifestations of SLE for organ damage and mortality, is a key component of lupus classification criteria. Accurately identifying lupus nephritis in electronic health records (EHRs) would therefore benefit large cohort observational studies and clinical trials where characterization of the patient population is critical for recruitment, study design, and analysis. Lupus nephritis can be recognized through procedure codes and structured data, such as laboratory tests. However, other critical information documenting lupus nephritis, such as histologic reports from kidney biopsies and prior medical history narratives, require sophisticated text processing to mine information from pathology reports and clinical notes. In this study, we developed algorithms to identify lupus nephritis with and without natural language processing (NLP) using EHR data from the Northwestern Medicine Enterprise Data Warehouse (NMEDW).

METHODS

We developed five algorithms: a rule-based algorithm using only structured data (baseline algorithm) and four algorithms using different NLP models. The first NLP model applied simple regular expression for keywords search combined with structured data. The other three NLP models were based on regularized logistic regression and used different sets of features including positive mention of concept unique identifiers (CUIs), number of appearances of CUIs, and a mixture of three components (i.e. a curated list of CUIs, regular expression concepts, structured data) respectively. The baseline algorithm and the best performing NLP algorithm were externally validated on a dataset from Vanderbilt University Medical Center (VUMC).

RESULTS

Our best performing NLP model incorporated features from both structured data, regular expression concepts, and mapped concept unique identifiers (CUIs) and showed improved F measure in both the NMEDW (0.41 vs 0.79) and VUMC (0.52 vs 0.93) datasets compared to the baseline lupus nephritis algorithm.

CONCLUSION

Our NLP MetaMap mixed model improved the F-measure greatly compared to the structured data only algorithm in both internal and external validation datasets. The NLP algorithms can serve as powerful tools to accurately identify lupus nephritis phenotype in EHR for clinical research and better targeted therapies.

Pretesting Discrete-Choice Experiments: A Guide for Researchers

Discrete-choice experiments (DCEs) are a frequently used method to explore the preferences of patients and other decision-makers in health. Pretesting is an essential stage in the design of a high-quality choice experiment and involves engaging with representatives of the target population to improve the readability, presentation, and structure of the preference instrument. The goal of pretesting in DCEs is to improve the validity, reliability, and relevance of the survey, while decreasing sources of bias, burden, and error associated with preference elicitation, data collection, and interpretation of the data. Despite its value to inform DCE design, pretesting lacks documented good practices or clearly reported applied examples. The purpose of this paper is: (1) to define pretesting and describe the pretesting process specifically in the context of a DCE, (2) to present a practical guide and pretesting interview discussion template for researchers looking to conduct a rigorous pretest of a DCE, and (3) to provide an illustrative example of how these resources were operationalized to inform the design of a complex DCE aimed at eliciting tradeoffs between personal privacy and societal benefit in the context of a police method known as investigative genetic genealogy (IGG).

Computational prediction of protein interactions in single cells by proximity sequencing

Proximity sequencing (Prox-seq) simultaneously measures gene expression, protein expression and protein complexes on single cells. Using information from dual-antibody binding events, Prox-seq infers surface protein dimers at the single-cell level. Prox-seq provides multi-dimensional phenotyping of single cells in high throughput, and was recently used to track the formation of receptor complexes during cell signaling and discovered a novel interaction between CD9 and CD8 in naïve T cells. The distribution of protein abundance can affect identification of protein complexes in a complicated manner in dual-binding assays like Prox-seq. These effects are difficult to explore with experiments, yet important for accurate quantification of protein complexes. Here, we introduce a physical model of Prox-seq and computationally evaluate several different methods for reducing background noise when quantifying protein complexes. Furthermore, we developed an improved method for analysis of Prox-seq data, which resulted in more accurate and robust quantification of protein complexes. Finally, our Prox-seq model offers a simple way to investigate the behavior of Prox-seq data under various biological conditions and guide users toward selecting the best analysis method for their data.

Genetic inhibition of angiopoietin-like protein-3, lipids, and cardiometabolic risk

BACKGROUND AND AIMS

RNA-based, antibody-based, and genome editing-based therapies are currently under investigation to determine if the inhibition of angiopoietin-like protein-3 (ANGPTL3) could reduce lipoprotein-lipid levels and atherosclerotic cardiovascular disease (ASCVD) risk. Mendelian randomisation (MR) was used to determine whether genetic variations influencing ANGPTL3 liver gene expression, blood levels, and protein structure could causally influence triglyceride and apolipoprotein B (apoB) levels as well as coronary artery disease (CAD), ischaemic stroke (IS), and other cardiometabolic diseases.

METHODS

RNA sequencing of 246 explanted liver samples and genome-wide genotyping was performed to identify single-nucleotide polymorphisms (SNPs) associated with liver expression of ANGPTL3. Genome-wide summary statistics of plasma protein levels of ANGPTL3 from the deCODE study (n = 35 359) were used. A total of 647 carriers of ANGPTL3 protein-truncating variants (PTVs) associated with lower plasma triglyceride levels were identified in the UK Biobank. Two-sample MR using SNPs that influence ANGPTL3 liver expression or ANGPTL3 plasma protein levels as exposure and cardiometabolic diseases as outcomes was performed (CAD, IS, heart failure, non-alcoholic fatty liver disease, acute pancreatitis, and type 2 diabetes). The impact of rare PTVs influencing plasma triglyceride levels on apoB levels and CAD was also investigated in the UK Biobank.

RESULTS

In two-sample MR studies, common genetic variants influencing ANGPTL3 hepatic or blood expression levels of ANGPTL3 had a very strong effect on plasma triglyceride levels, a more modest effect on low-density lipoprotein cholesterol, a weaker effect on apoB levels, and no effect on CAD or other cardiometabolic diseases. In the UK Biobank, the carriers of rare ANGPTL3 PTVs providing lifelong reductions in median plasma triglyceride levels [-0.37 (interquartile range 0.41) mmol/L] had slightly lower apoB levels (-0.06 ± 0.32 g/L) and similar CAD event rates compared with non-carriers (10.2% vs. 10.9% in carriers vs. non-carriers, P = .60).

CONCLUSIONS

PTVs influencing ANGPTL3 protein structure as well as common genetic variants influencing ANGPTL3 hepatic expression and/or blood protein levels exhibit a strong effect on circulating plasma triglyceride levels, a weak effect on circulating apoB levels, and no effect on ASCVD. Near-complete inhibition of ANGPTL3 function in patients with very elevated apoB levels may be required to reduce ASCVD risk.

Enrichment on steps, not genes, improves inference of differentially expressed pathways

Enrichment analysis is frequently used in combination with differential expression data to investigate potential commonalities amongst lists of genes and generate hypotheses for further experiments. However, current enrichment analysis approaches on pathways ignore the functional relationships between genes in a pathway, particularly OR logic that occurs when a set of proteins can each individually perform the same step in a pathway. As a result, these approaches miss pathways with large or multiple sets because of an inflation of pathway size (when measured as the total gene count) relative to the number of steps. We address this problem by enriching on step-enabling entities in pathways. We treat sets of protein-coding genes as single entities, and we also weight sets to account for the number of genes in them using the multivariate Fisher's noncentral hypergeometric distribution. We then show three examples of pathways that are recovered with this method and find that the results have significant proportions of pathways not found in gene list enrichment analysis.

Predicting Functional Consequences of Recent Natural Selection in Britain

Ancient DNA can directly reveal the contribution of natural selection to human genomic variation. However, while the analysis of ancient DNA has been successful at identifying genomic signals of selection, inferring the phenotypic consequences of that selection has been more difficult. Most trait-associated variants are noncoding, so we expect that a large proportion of the phenotypic effects of selection will also act through noncoding variation. Since we cannot measure gene expression directly in ancient individuals, we used an approach (Joint-Tissue Imputation [JTI]) developed to predict gene expression from genotype data. We tested for changes in the predicted expression of 17,384 protein coding genes over a time transect of 4,500 years using 91 present-day and 616 ancient individuals from Britain. We identified 28 genes at seven genomic loci with significant (false discovery rate [FDR] < 0.05) changes in predicted expression levels in this time period. We compared the results from our transcriptome-wide scan to a genome-wide scan based on estimating per-single nucleotide polymorphism (SNP) selection coefficients from time series data. At five previously identified loci, our approach allowed us to highlight small numbers of genes with evidence for significant shifts in expression from peaks that in some cases span tens of genes. At two novel loci (SLC44A5 and NUP85), we identify selection on gene expression not captured by scans based on genomic signatures of selection. Finally, we show how classical selection statistics (iHS and SDS) can be combined with JTI models to incorporate functional information into scans that use present-day data alone. These results demonstrate the potential of this type of information to explore both the causes and consequences of natural selection.

The T2T-CHM13 reference assembly uncovers essential WASH1 and GPRIN2 paralogues

Summary

The recently published T2T-CHM13 reference assembly completed the annotation of the final 8% of the human genome. It introduced 1956 genes, close to 100 of which are predicted to be coding because they have a protein coding parent gene. Here, we confirm the coding status and functional relevance of two of these genes, paralogues of WASHC1 and GPRIN2. We find that LOC124908094, one of four novel subtelomeric WASH1 genes uncovered in the new assembly, produces the WASH1 protein that forms part of the vital actin-regulatory WASH complex. Its coding status is supported by abundant proteomics, conservation, and cDNA evidence. It was previously assumed that gene WASHC1 produced the functional WASH1 protein, but new evidence shows that WASHC1 is a human-derived duplication and likely to be one of 12 WASH1 pseudogenes in the human gene set. We also find that the T2T-CHM13 assembly has added a functionally important copy of GPRIN2 to the human gene set. We demonstrate that uniquely mapping peptides from proteomics databases support the novel LOC124900631 rather than the GRCh38 assembly GPRIN2 gene. These new additions to the set of human coding genes underlines the importance of the new T2T-CHM13 assembly.

Availability and implementation

None.

Aptamer-Functionalized Interface Nanopores Enable Amino Acid-Specific Peptide Detection

Single-molecule proteomics based on nanopore technology has made significant advances in recent years. However, to achieve nanopore sensing with single amino acid resolution, several bottlenecks must be tackled: controlling nanopore sizes with nanoscale precision and slowing molecular translocation events. Herein, we address these challenges by integrating amino acid-specific DNA aptamers into interface nanopores with dynamically tunable pore sizes. A phenylalanine aptamer was used as a proof-of-concept: aptamer recognition of phenylalanine moieties led to the retention of specific peptides, slowing translocation speeds. Importantly, while phenylalanine aptamers were isolated against the free amino acid, the aptamers were determined to recognize the combination of the benzyl or phenyl and the carbonyl group in the peptide backbone, enabling binding to specific phenylalanine-containing peptides. We decoupled specific binding between aptamers and phenylalanine-containing peptides from nonspecific interactions (e.g., electrostatics and hydrophobic interactions) using optical waveguide lightmode spectroscopy. Aptamer-modified interface nanopores differentiated peptides containing phenylalanine vs. control peptides with structurally similar amino acids (i.e., tyrosine and tryptophan). When the duration of aptamer-target interactions inside the nanopore were prolonged by lowering the applied voltage, discrete ionic current levels with repetitive motifs were observed. Such reoccurring signatures in the measured signal suggest that the proposed method has the possibility to resolve amino acid-specific aptamer recognition, a step toward single-molecule proteomics.

Molecular Determinants of Current Blockade Produced by Peptide Transport Through a Nanopore

The nanopore sensing method holds the promise of delivering a single molecule technology for identification of biological proteins, direct detection of post-translational modifications, and perhaps de novo determination of a protein's amino acid sequence. The key quantity measured in such nanopore sensing experiments is the magnitude of the ionic current passing through a nanopore blocked by a polypeptide chain. Establishing a relationship between the amino acid sequence of a peptide fragment confined within a nanopore and the blockade current flowing through the nanopore remains a major challenge for realizing the nanopore protein sequencing. Using the results of all-atom molecular dynamics simulations, here we compare nanopore sequencing of DNA with nanopore sequencing of proteins. We then delineate the factors affecting the blockade current modulation by the peptide sequence, showing that the current can be determined by (i) the steric footprint of an amino acid, (ii) its interactions with the pore wall, (iii) the local stretching of a polypeptide chain, and (iv) the local enhancement of the ion concentration at the nanopore constriction. We conclude with a brief discussion of the prospects for purely computational prediction of the blockade currents.

Epistasis between mutator alleles contributes to germline mutation spectrum variability in laboratory mice

Maintaining germline genome integrity is essential and enormously complex. Although many proteins are involved in DNA replication, proofreading, and repair, mutator alleles have largely eluded detection in mammals. DNA replication and repair proteins often recognize sequence motifs or excise lesions at specific nucleotides. Thus, we might expect that the spectrum of de novo mutations - the frequencies of C>T, A>G, etc. - will differ between genomes that harbor either a mutator or wild-type allele. Previously, we used quantitative trait locus mapping to discover candidate mutator alleles in the DNA repair gene Mutyh that increased the C>A germline mutation rate in a family of inbred mice known as the BXDs (Sasani et al., 2022, Ashbrook et al., 2021). In this study we developed a new method to detect alleles associated with mutation spectrum variation and applied it to mutation data from the BXDs. We discovered an additional C>A mutator locus on chromosome 6 that overlaps Ogg1, a DNA glycosylase involved in the same base-excision repair network as Mutyh (David et al., 2007). Its effect depends on the presence of a mutator allele near Mutyh, and BXDs with mutator alleles at both loci have greater numbers of C>A mutations than those with mutator alleles at either locus alone. Our new methods for analyzing mutation spectra reveal evidence of epistasis between germline mutator alleles and may be applicable to mutation data from humans and other model organisms.

Recent advances in polygenic scores: translation, equitability, methods and FAIR tools

Polygenic scores (PGS) can be used for risk stratification by quantifying individuals' genetic predisposition to disease, and many potentially clinically useful applications have been proposed. Here, we review the latest potential benefits of PGS in the clinic and challenges to implementation. PGS could augment risk stratification through combined use with traditional risk factors (demographics, disease-specific risk factors, family history, etc.), to support diagnostic pathways, to predict groups with therapeutic benefits, and to increase the efficiency of clinical trials. However, there exist challenges to maximizing the clinical utility of PGS, including FAIR (Findable, Accessible, Interoperable, and Reusable) use and standardized sharing of the genomic data needed to develop and recalculate PGS, the equitable performance of PGS across populations and ancestries, the generation of robust and reproducible PGS calculations, and the responsible communication and interpretation of results. We outline how these challenges may be overcome analytically and with more diverse data as well as highlight sustained community efforts to achieve equitable, impactful, and responsible use of PGS in healthcare.

Cell-type-specific cis-regulatory divergence in gene expression and chromatin accessibility revealed by human-chimpanzee hybrid cells

Although gene expression divergence has long been postulated to be the primary driver of human evolution, identifying the genes and genetic variants underlying uniquely human traits has proven to be quite challenging. Theory suggests that cell-type-specific cis-regulatory variants may fuel evolutionary adaptation due to the specificity of their effects. These variants can precisely tune the expression of a single gene in a single cell-type, avoiding the potentially deleterious consequences of trans-acting changes and non-cell type-specific changes that can impact many genes and cell types, respectively. It has recently become possible to quantify human-specific cis-acting regulatory divergence by measuring allele-specific expression in human-chimpanzee hybrid cells-the product of fusing induced pluripotent stem (iPS) cells of each species in vitro. However, these cis-regulatory changes have only been explored in a limited number of cell types. Here, we quantify human-chimpanzee cis-regulatory divergence in gene expression and chromatin accessibility across six cell types, enabling the identification of highly cell-type-specific cis-regulatory changes. We find that cell-type-specific genes and regulatory elements evolve faster than those shared across cell types, suggesting an important role for genes with cell-type-specific expression in human evolution. Furthermore, we identify several instances of lineage-specific natural selection that may have played key roles in specific cell types, such as coordinated changes in the cis-regulation of dozens of genes involved in neuronal firing in motor neurons. Finally, using novel metrics and a machine learning model, we identify genetic variants that likely alter chromatin accessibility and transcription factor binding, leading to neuron-specific changes in the expression of the neurodevelopmentally important genes FABP7 and GAD1. Overall, our results demonstrate that integrative analysis of cis-regulatory divergence in chromatin accessibility and gene expression across cell types is a promising approach to identify the specific genes and genetic variants that make us human.

Infiltration of CD3+ and CD8+ lymphocytes in association with inflammation and survival in pancreatic cancer

BACKGROUND

Pancreatic ductal adenocarcinomas (PDAC) have heterogeneous tumor microenvironments relatively devoid of infiltrating immune cells. We aimed to quantitatively assess infiltrating CD3+ and CD8+ lymphocytes in a treatment-naïve patient cohort and assess associations with overall survival and microenvironment inflammatory proteins.

METHODS

Tissue microarrays were immunohistochemically stained for CD3+ and CD8+ lymphocytes and quantitatively assessed using QuPath. Levels of inflammation-associated proteins were quantified by multiplexed, enzyme-linked immunosorbent assay panels on matching tumor and tissue samples.

RESULTS

Our findings revealed a significant increase in both CD3+ and CD8+ lymphocytes populations in PDAC compared with non-PDAC tissue, except when comparing CD8+ percentages in PDAC versus intraductal papillary mucinous neoplasms (IPMN) (p = 0.5012). Patients with quantitatively assessed CD3+ low tumors (lower 50%) had shorter survival (median 273 days) compared to CD3+ high tumors (upper 50%) with a median overall survival of 642.5 days (p = 0.2184). Patients with quantitatively assessed CD8+ low tumors had significantly shorter survival (median 240 days) compared to CD8+ high tumors with a median overall survival of 1059 days (p = 0.0003). Of 41 proteins assessed in the inflammation assay, higher levels of IL-1B and IL-2 were significantly associated with decreased CD3+ infiltration (r = -0.3704, p = 0.0187, and r = -0.4275, p = 0.0074, respectively). Higher levels of IL-1B were also significantly associated with decreased CD8+ infiltration (r = -0.4299, p = 0.0045), but not IL-2 (r = -0.0078, p = 0.9616). Principal component analysis of the inflammatory analytes showed diverse inflammatory responses in PDAC.

CONCLUSION

In this work, we found a marked heterogeneity in infiltrating CD3+ and CD8+ lymphocytes and individual inflammatory responses in PDAC. Future mechanistic studies should explore personalized therapeutic strategies to target the immune and inflammatory components of the tumor microenvironment.

Quantification of Proteus syndrome-associated lung disease

BACKGROUND

Proteus syndrome is an ultra-rare mosaic overgrowth disorder. Individuals with Proteus syndrome can develop emphysematous and cystic changes of the lung that may lead to progressive respiratory symptoms and require surgical intervention. This retrospective study seeks to quantify the radiographic features of Proteus syndrome-associated lung disease using computed tomography (CT) of the chest. The first method derives a Cystic Lung Score (CLS) by using a computer-aided diagnostic tool to quantify the fraction of cystic involvement of the lung. The second method yields a Clinician Visual Score (CVS), an observer reported scale of severity based on multiple radiographic features. The aim of this study was to determine if these measurements are associated with clinical symptoms, pulmonary function test (PFT) measurements, and if they may be used to assess progression of pulmonary disease.

RESULTS

One hundred and thirteen imaging studies from 44 individuals with Proteus syndrome were included. Dyspnea and oxygen use were each associated with higher CLS (p = 0.001 and < 0.001, respectively) and higher CVS (p < 0.001 and < 0.001). Decreases in percent predicted FVC, FEV1, and DLCO each correlated with increased CLS and CVS. The annual increase of CLS in children, 5.6, was significantly greater than in adults, 1.6. (p = 0.03). The annual increase in CVS in children, 0.4, was similar to adults, 0.2 (p = 0.36).

CONCLUSIONS

Proteus syndrome-associated lung disease is progressive. The rate of cystic progression is increased in children. Increased scores in CLS and CVS were associated with clinical symptoms and decreased pulmonary function. Both methods were able to detect change over time and were associated with clinically meaningful outcomes which may enable their use in interventional studies.

Provider-reported experiences, barriers, and perspectives on genetic testing as part of autism diagnosis

Several professional organizations recommend conducting genetic testing as part of the autism diagnosis process, as it can provide additional information and benefits for autistic people and their families. However, there is disagreement among autism communities about whether genetic testing reflects autistic people's best interests. In practice, rates of clinical genetic testing for autism are much lower than diagnoses, creating a large gap between clinical guidelines and real clinical encounters. To investigate one potential source of this gap, we interviewed 14 healthcare providers about the autism diagnostic process and their actions related to autism genetic testing. We recruited a sample of primarily Ph.D. level-psychologists and analyzed our qualitative data using a five-step framework analysis method. Participants generally had positive or mixed views of genetic testing in autism. They described their current experiences of implementation of genetic testing, including that they did not often find it changed their clinical practice. Only some providers recommended it to everyone receiving an autism diagnosis. They also listed factors which discourage families from getting testing, including high costs, families feeling overwhelmed, other support needs taking priority, and ethical implications. Notably, providers highlighted a trend of referring patients to research genetic testing rather than clinical testing, which may provide a cheaper and easier alternative but is not likely to return results to participants. Finally, participants felt they needed more training in genetics and listed specific topics of uncertainty. Our research highlights a need to further educate clinicians in the uses and limitations of genetic testing for autism and suggests content areas of focus for genetics educators.

Plasma Neuronal Growth Regulator 1 May Link Physical Activity to Reduced Risk of Type 2 Diabetes: A Proteome-Wide Study of ARIC Participants

Habitual physical activity (PA) impacts the plasma proteome and reduces the risk of developing type 2 diabetes (T2D). Using a large-scale proteome-wide approach in Atherosclerosis Risk in Communities study participants, we aimed to identify plasma proteins associated with PA and determine which of these may be causally related to lower T2D risk. PA was associated with 92 plasma proteins in discovery (P < 1.01 × 10-5), and 40 remained significant in replication (P < 5.43 × 10-4). Eighteen of these proteins were independently associated with incident T2D (P < 1.25 × 10-3), including neuronal growth regulator 1 (NeGR1; hazard ratio per SD 0.85; P = 7.5 × 10-11). Two-sample Mendelian randomization (MR) inverse variance weighted analysis indicated that higher NeGR1 reduces T2D risk (odds ratio [OR] per SD 0.92; P = 0.03) and was consistent with MR-Egger, weighted median, and weighted mode sensitivity analyses. A stronger association was observed for the single cis-acting NeGR1 genetic variant (OR per SD 0.80; P = 6.3 × 10-5). Coupled with previous evidence that low circulating NeGR1 levels promote adiposity, its association with PA and potential causal role in T2D shown here suggest that NeGR1 may link PA exposure with metabolic outcomes. Further research is warranted to confirm our findings and examine the interplay of PA, NeGR1, adiposity, and metabolic health.

ARTICLE HIGHLIGHTS

Comparison of clinical geneticist and computer visual attention in assessing genetic conditions

Artificial intelligence (AI) for facial diagnostics is increasingly used in the genetics clinic to evaluate patients with potential genetic conditions. Current approaches focus on one type of AI called Deep Learning (DL). While DL- based facial diagnostic platforms have a high accuracy rate for many conditions, less is understood about how this technology assesses and classifies (categorizes) images, and how this compares to humans. To compare human and computer attention, we performed eye-tracking analyses of geneticist clinicians (n = 22) and non-clinicians (n = 22) who viewed images of people with 10 different genetic conditions, as well as images of unaffected individuals. We calculated the Intersection-over-Union (IoU) and Kullback-Leibler divergence (KL) to compare the visual attentions of the two participant groups, and then the clinician group against the saliency maps of our deep learning classifier. We found that human visual attention differs greatly from DL model's saliency results. Averaging over all the test images, IoU and KL metric for the successful (accurate) clinician visual attentions versus the saliency maps were 0.15 and 11.15, respectively. Individuals also tend to have a specific pattern of image inspection, and clinicians demonstrate different visual attention patterns than non-clinicians (IoU and KL of clinicians versus non-clinicians were 0.47 and 2.73, respectively). This study shows that humans (at different levels of expertise) and a computer vision model examine images differently. Understanding these differences can improve the design and use of AI tools, and lead to more meaningful interactions between clinicians and AI technologies.

Autonomy and bioethics in fan responses to Orphan Black

Viewers' responses to Orphan Black (2013-2017), a popular, genetics-themed sci-fi television series, reveal much about public understanding of the ethical challenges associated with genetic science. In this article, we assess how fans of Orphan Black process the bioethical themes that are prominent in the show through an analysis of 182 viewer-created blog posts. Using a mixed methods approach, our findings reveal that Orphan Black's fans distill the essence of the show down to its characters' fight for autonomy. Furthermore, fan blogs reveal two notable pathways through which this bioethical principle is explored: gender and reproduction. Viewers draw striking connections between the moral problems they observe on screen in Orphan Black and those they see in the real world-both today and in a possible future-particularly as those problems affect women. While existing scholarship acknowledges these themes in the show itself, our approach demonstrates science fiction fans' active participation in meaning-making and bioethical reasoning and offers a novel approach to studying fan-generated content for public understanding of science research.

Caught between pity, explicit bias, and discrimination: a qualitative study on the impact of stigma on the quality of life of persons living with sickle cell disease in three African countries

PURPOSE

Sickle cell disease (SCD) is an inherited blood disorder characterized by unpredictable episodes of acute pain and numerous health complications. Individuals with SCD often face stigma from the public, including perceptions that they are lazy or weak tending to exaggerate their pain crisis, which can profoundly impact their quality of life (QoL).

METHODS

In a qualitative phenomenological study conducted in Cameroon, Ghana, and Tanzania, we explored stakeholders' perceptions of SCD-related stigma using three analytical frameworks: Bronfenbrenner's Ecological Systems Theory; The Health Stigma and Discriminatory Framework; and A Public Health Framework for Reducing Stigma.

RESULTS

The study reveals that SCD-related stigma is marked by prejudice, negative labelling and social discrimination, with derogatory terms such as sickler, ogbanje (one who comes and goes), sika besa (money will finish), ene mewu (I can die today, I can die tomorrow), vampire (one who consumes human blood), and Efiewura (landlord-of the hospital), commonly used to refer to individuals living with SCD. Drivers of stigma include frequent crises and hospitalizations, distinct physical features of individuals living with SCD, cultural misconceptions about SCD and its association with early mortality. Proposed strategies for mitigating stigma include public health education campaigns about SCD, integrating SCD into school curricula, healthcare worker training and community engagement.

CONCLUSION

The results highlight the importance of challenging stigmatizing narratives on SCD and recognizing that stigmatization represents a social injustice that significantly diminishes the QoL of individuals living with SCD.

MicroRNAs and Cardiovascular Disease Risk

PURPOSE OF REVIEW

MicroRNAs (miRNAs)-short, non-coding RNAs-play important roles in almost all aspects of cardiovascular biology, and changes in intracellular miRNA expression are indicative of cardiovascular disease development and progression. Extracellular miRNAs, which are easily measured in blood and can be reflective of changes in intracellular miRNA levels, have emerged as potential non-invasive biomarkers for disease. This review summarizes current knowledge regarding miRNAs as biomarkers for assessing cardiovascular disease risk and prognosis.

RECENT FINDINGS

Numerous studies over the last 10-15 years have identified associations between extracellular miRNA profiles and cardiovascular disease, supporting the potential use of extracellular miRNAs as biomarkers for risk stratification. However, clinical application of extracellular miRNA profiles has been hampered by poor reproducibility and inter-study variability that is due largely to methodological differences between studies. While recent studies indicate that circulating extracellular miRNAs are promising biomarkers for cardiovascular disease, evidence for clinical implementation is lacking. This highlights the need for larger, well-designed studies that use standardized methods for sample preparation, miRNA isolation, quantification, and normalization.

A new experimental evidence-weighted signaling pathway resource in FlyBase

Research in model organisms is central to the characterization of signaling pathways in multicellular organisms. Here, we present the comprehensive and systematic curation of 17 Drosophila signaling pathways using the Gene Ontology framework to establish a dynamic resource that has been incorporated into FlyBase, providing visualization and data integration tools to aid research projects. By restricting to experimental evidence reported in the research literature and quantifying the amount of such evidence for each gene in a pathway, we captured the landscape of empirical knowledge of signaling pathways in Drosophila.

An evaluation of GPT models for phenotype concept recognition

OBJECTIVE

Clinical deep phenotyping and phenotype annotation play a critical role in both the diagnosis of patients with rare disorders as well as in building computationally-tractable knowledge in the rare disorders field. These processes rely on using ontology concepts, often from the Human Phenotype Ontology, in conjunction with a phenotype concept recognition task (supported usually by machine learning methods) to curate patient profiles or existing scientific literature. With the significant shift in the use of large language models (LLMs) for most NLP tasks, we examine the performance of the latest Generative Pre-trained Transformer (GPT) models underpinning ChatGPT as a foundation for the tasks of clinical phenotyping and phenotype annotation.

MATERIALS AND METHODS

The experimental setup of the study included seven prompts of various levels of specificity, two GPT models (gpt-3.5-turbo and gpt-4.0) and two established gold standard corpora for phenotype recognition, one consisting of publication abstracts and the other clinical observations.

RESULTS

The best run, using in-context learning, achieved 0.58 document-level F1 score on publication abstracts and 0.75 document-level F1 score on clinical observations, as well as a mention-level F1 score of 0.7, which surpasses the current best in class tool. Without in-context learning, however, performance is significantly below the existing approaches.

CONCLUSION

Our experiments show that gpt-4.0 surpasses the state of the art performance if the task is constrained to a subset of the target ontology where there is prior knowledge of the terms that are expected to be matched. While the results are promising, the non-deterministic nature of the outcomes, the high cost and the lack of concordance between different runs using the same prompt and input make the use of these LLMs challenging for this particular task.

Star allele search: a pharmacogenetic annotation database and user-friendly search tool of publicly available 1000 Genomes Project biospecimens

Here we describe a new public pharmacogenetic (PGx) annotation database of a large (n = 3,202) and diverse biospecimen collection of 1000 Genomes Project cell lines and DNAs. The database is searchable with a user friendly, web-based tool ( www.coriell.org/StarAllele/Search ). This resource leverages existing whole genome sequencing data and PharmVar annotations to characterize *alleles for each biospecimen in the collection. This new tool is designed to facilitate in vitro functional characterization of *allele haplotypes and diplotypes as well as support clinical PGx assay development, validation, and implementation.

CRISPR screening identifies BET and mTOR inhibitor synergy in cholangiocarcinoma through serine glycine one carbon

Patients with cholangiocarcinoma have poor clinical outcomes due to late diagnoses, poor prognoses, and limited treatment strategies. To identify drug combinations for this disease, we have conducted a genome-wide CRISPR screen anchored on the bromodomain and extraterminal domain (BET) PROTAC degrader ARV825, from which we identified anticancer synergy when combined with genetic ablation of members of the mTOR pathway. This combination effect was validated using multiple pharmacological BET and mTOR inhibitors, accompanied by increased levels of apoptosis and cell cycle arrest. In a xenograft model, combined BET degradation and mTOR inhibition induced tumor regression. Mechanistically, the 2 inhibitor classes converged on H3K27ac-marked epigenetic suppression of the serine glycine one carbon (SGOC) metabolism pathway, including the key enzymes PHGDH and PSAT1. Knockdown of PSAT1 was sufficient to replicate synergy with single-agent inhibition of either BET or mTOR. Our results tie together epigenetic regulation, metabolism, and apoptosis induction as key therapeutic targets for further exploration in this underserved disease.

SURGE: uncovering context-specific genetic-regulation of gene expression from single-cell RNA sequencing using latent-factor models

Genetic regulation of gene expression is a complex process, with genetic effects known to vary across cellular contexts such as cell types and environmental conditions. We developed SURGE, a method for unsupervised discovery of context-specific expression quantitative trait loci (eQTLs) from single-cell transcriptomic data. This allows discovery of the contexts or cell types modulating genetic regulation without prior knowledge. Applied to peripheral blood single-cell eQTL data, SURGE contexts capture continuous representations of distinct cell types and groupings of biologically related cell types. We demonstrate the disease-relevance of SURGE context-specific eQTLs using colocalization analysis and stratified LD-score regression.

Integrative analysis identifies gene signatures mediating the effect of DNA methylation on asthma severity and lung function

DNA methylation (DNAm) changes play a key role in regulating gene expression in asthma. To investigate the role of epigenetics and transcriptomics change in asthma, we used publicly available DNAm (asthmatics, n = 96 and controls, n = 46) and gene expression (asthmatics, n = 79 and controls, n = 39) data derived from bronchial epithelial cells (BECs). We performed differential methylation/expression and weighted co-methylation/co-expression network analyses to identify co-methylated and co-expressed modules associated with asthma severity and lung function. For subjects with both DNAm and gene expression data (asthmatics, n = 79 and controls, n = 39), machine-learning technique was used to prioritize CpGs and differentially expressed genes (DEGs) for asthma risk prediction, and mediation analysis was used to uncover DEGs that mediate the effect of DNAm on asthma severity and lung function in BECs. Finally, we validated CpGs and their associated DEGs and the asthma risk prediction model in airway epithelial cells (AECs) dataset. The asthma risk prediction model based on 18 CpGs and 28 DEGs showed high accuracy in both the discovery BEC dataset with area under the receiver operating characteristic curve (AUC) = 0.99 and the validation AEC dataset (AUC = 0.82). Genes in the three co-methylated and six co-expressed modules were enriched in multiple pathways including WNT/beta-catenin signaling and notch signaling. Moreover, we identified 35 CpGs correlated with DEGs in BECs, of which 17 CpGs including cg01975495 (SERPINE1), cg10528482 (SLC9A3), cg25477769 (HNF1A) and cg26639146 (CD9), cg17945560 (TINAGL1) and cg10290200 (FLNC) were replicated in AECs. These DEGs mediate the association between DNAm and asthma severity and lung function. Overall, our study investigated the role of DNAm and gene expression change in asthma and provided an insight into the mechanisms underlying the effects of DNA methylation on asthma, asthma severity and lung function.

Developing and evaluating pediatric phecodes (Peds-Phecodes) for high-throughput phenotyping using electronic health records

OBJECTIVE

Pediatric patients have different diseases and outcomes than adults; however, existing phecodes do not capture the distinctive pediatric spectrum of disease. We aim to develop specialized pediatric phecodes (Peds-Phecodes) to enable efficient, large-scale phenotypic analyses of pediatric patients.

MATERIALS AND METHODS

We adopted a hybrid data- and knowledge-driven approach leveraging electronic health records (EHRs) and genetic data from Vanderbilt University Medical Center to modify the most recent version of phecodes to better capture pediatric phenotypes. First, we compared the prevalence of patient diagnoses in pediatric and adult populations to identify disease phenotypes differentially affecting children and adults. We then used clinical domain knowledge to remove phecodes representing phenotypes unlikely to affect pediatric patients and create new phecodes for phenotypes relevant to the pediatric population. We further compared phenome-wide association study (PheWAS) outcomes replicating known pediatric genotype-phenotype associations between Peds-Phecodes and phecodes.

RESULTS

The Peds-Phecodes aggregate 15 533 ICD-9-CM codes and 82 949 ICD-10-CM codes into 2051 distinct phecodes. Peds-Phecodes replicated more known pediatric genotype-phenotype associations than phecodes (248 vs 192 out of 687 SNPs, P < .001).

DISCUSSION

We introduce Peds-Phecodes, a high-throughput EHR phenotyping tool tailored for use in pediatric populations. We successfully validated the Peds-Phecodes using genetic replication studies. Our findings also reveal the potential use of Peds-Phecodes in detecting novel genotype-phenotype associations for pediatric conditions. We expect that Peds-Phecodes will facilitate large-scale phenomic and genomic analyses in pediatric populations.

CONCLUSION

Peds-Phecodes capture higher-quality pediatric phenotypes and deliver superior PheWAS outcomes compared to phecodes.

Using a chat-based informed consent tool in large-scale genomic research

OBJECTIVE

We implemented a chatbot consent tool to shift the time burden from study staff in support of a national genomics research study.

MATERIALS AND METHODS

We created an Institutional Review Board-approved script for automated chat-based consent. We compared data from prospective participants who used the tool or had traditional consent conversations with study staff.

RESULTS

Chat-based consent, completed on a user's schedule, was shorter than the traditional conversation. This did not lead to a significant change in affirmative consents. Within affirmative consents and declines, more prospective participants completed the chat-based process. A quiz to assess chat-based consent user understanding had a high pass rate with no reported negative experiences.

CONCLUSION

Our report shows that a structured script can convey important information while realizing the benefits of automation and burden shifting. Analysis suggests that it may be advantageous to use chatbots to scale this rate-limiting step in large research projects.

Improved modeling of RNA-binding protein motifs in an interpretable neural model of RNA splicing

Sequence-specific RNA-binding proteins (RBPs) play central roles in splicing decisions. Here, we describe a modular splicing architecture that leverages in vitro-derived RNA affinity models for 79 human RBPs and the annotated human genome to produce improved models of RBP binding and activity. Binding and activity are modeled by separate Motif and Aggregator components that can be mixed and matched, enforcing sparsity to improve interpretability. Training a new Adjusted Motif (AM) architecture on the splicing task not only yields better splicing predictions but also improves prediction of RBP-binding sites in vivo and of splicing activity, assessed using independent data.

Recurrent de novo SPTLC2 variant causes childhood-onset amyotrophic lateral sclerosis (ALS) by excess sphingolipid synthesis

BACKGROUND

Amyotrophic lateral sclerosis (ALS) is a neurodegenerative disease of the upper and lower motor neurons with varying ages of onset, progression and pathomechanisms. Monogenic childhood-onset ALS, although rare, forms an important subgroup of ALS. We recently reported specific SPTLC1 variants resulting in sphingolipid overproduction as a cause for juvenile ALS. Here, we report six patients from six independent families with a recurrent, de novo, heterozygous variant in SPTLC2 c.778G>A [p.Glu260Lys] manifesting with juvenile ALS.

METHODS

Clinical examination of the patients along with ancillary and genetic testing, followed by biochemical investigation of patients' blood and fibroblasts, was performed.

RESULTS

All patients presented with early-childhood-onset progressive weakness, with signs and symptoms of upper and lower motor neuron degeneration in multiple myotomes, without sensory neuropathy. These findings were supported on ancillary testing including nerve conduction studies and electromyography, muscle biopsies and muscle ultrasound studies. Biochemical investigations in plasma and fibroblasts showed elevated levels of ceramides and unrestrained de novo sphingolipid synthesis. Our studies indicate that SPTLC2 variant [c.778G>A, p.Glu260Lys] acts distinctly from hereditary sensory and autonomic neuropathy (HSAN)-causing SPTLC2 variants by causing excess canonical sphingolipid biosynthesis, similar to the recently reported SPTLC1 ALS associated pathogenic variants. Our studies also indicate that serine supplementation, which is a therapeutic in SPTLC1 and SPTCL2-associated HSAN, is expected to exacerbate the excess sphingolipid synthesis in serine palmitoyltransferase (SPT)-associated ALS.

CONCLUSIONS

SPTLC2 is the second SPT-associated gene that underlies monogenic, juvenile ALS and further establishes alterations of sphingolipid metabolism in motor neuron disease pathogenesis. Our findings also have important therapeutic implications: serine supplementation must be avoided in SPT-associated ALS, as it is expected to drive pathogenesis further.

Erythromelalgia caused by the missense mutation p.Arg220Pro in an alternatively spliced exon of SCN9A (NaV1.7)

Erythromelalgia (EM), is a familial pain syndrome characterized by episodic 'burning' pain, warmth, and erythema. EM is caused by monoallelic variants in SCN9A, which encodes the voltage-gated sodium channel (NaV) NaV1.7. Over 25 different SCN9A mutations attributed to EM have been described to date, all identified in the SCN9A transcript utilizing exon 6N. Here we report a novel SCN9A missense variant identified in seven related individuals with stereotypic episodes of bilateral lower limb pain presenting in childhood. The variant, XM_011511617.3:c.659G>C;p.(Arg220Pro), resides in the exon 6A of SCN9A, an exon previously shown to be selectively incorporated by developmentally regulated alternative splicing. The mutation is located in the voltage-sensing S4 segment of domain I, which is important for regulating channel activation. Functional analysis showed the p.Arg220Pro mutation altered voltage-dependent activation and delayed channel inactivation, consistent with a NaV1.7 gain-of-function molecular phenotype. These results demonstrate that alternatively spliced isoforms of SCN9A should be included in all genomic testing of EM.

The DO-KB Knowledgebase: a 20-year journey developing the disease open science ecosystem

In 2003, the Human Disease Ontology (DO, https://disease-ontology.org/) was established at Northwestern University. In the intervening 20 years, the DO has expanded to become a highly-utilized disease knowledge resource. Serving as the nomenclature and classification standard for human diseases, the DO provides a stable, etiology-based structure integrating mechanistic drivers of human disease. Over the past two decades the DO has grown from a collection of clinical vocabularies, into an expertly curated semantic resource of over 11300 common and rare diseases linking disease concepts through more than 37000 vocabulary cross mappings (v2023-08-08). Here, we introduce the recently launched DO Knowledgebase (DO-KB), which expands the DO's representation of the diseaseome and enhances the findability, accessibility, interoperability and reusability (FAIR) of disease data through a new SPARQL service and new Faceted Search Interface. The DO-KB is an integrated data system, built upon the DO's semantic disease knowledge backbone, with resources that expose and connect the DO's semantic knowledge with disease-related data across Open Linked Data resources. This update includes descriptions of efforts to assess the DO's global impact and improvements to data quality and content, with emphasis on changes in the last two years.

The Monarch Initiative in 2024: an analytic platform integrating phenotypes, genes and diseases across species

Bridging the gap between genetic variations, environmental determinants, and phenotypic outcomes is critical for supporting clinical diagnosis and understanding mechanisms of diseases. It requires integrating open data at a global scale. The Monarch Initiative advances these goals by developing open ontologies, semantic data models, and knowledge graphs for translational research. The Monarch App is an integrated platform combining data about genes, phenotypes, and diseases across species. Monarch's APIs enable access to carefully curated datasets and advanced analysis tools that support the understanding and diagnosis of disease for diverse applications such as variant prioritization, deep phenotyping, and patient profile-matching. We have migrated our system into a scalable, cloud-based infrastructure; simplified Monarch's data ingestion and knowledge graph integration systems; enhanced data mapping and integration standards; and developed a new user interface with novel search and graph navigation features. Furthermore, we advanced Monarch's analytic tools by developing a customized plugin for OpenAI's ChatGPT to increase the reliability of its responses about phenotypic data, allowing us to interrogate the knowledge in the Monarch graph using state-of-the-art Large Language Models. The resources of the Monarch Initiative can be found at monarchinitiative.org and its corresponding code repository at github.com/monarch-initiative/monarch-app.

The UCSC Genome Browser database: 2024 update

The UCSC Genome Browser (https://genome.ucsc.edu) is a web-based genomic visualization and analysis tool that serves data to over 7,000 distinct users per day worldwide. It provides annotation data on thousands of genome assemblies, ranging from human to SARS-CoV2. This year, we have introduced new data from the Human Pangenome Reference Consortium and on viral genomes including SARS-CoV2. We have added 1,200 new genomes to our GenArk genome system, increasing the overall diversity of our genomic representation. We have added support for nine new user-contributed track hubs to our public hub system. Additionally, we have released 29 new tracks on the human genome and 11 new tracks on the mouse genome. Collectively, these new features expand both the breadth and depth of the genomic knowledge that we share publicly with users worldwide.

DGIdb 5.0: rebuilding the drug-gene interaction database for precision medicine and drug discovery platforms

The Drug-Gene Interaction Database (DGIdb, https://dgidb.org) is a publicly accessible resource that aggregates genes or gene products, drugs and drug-gene interaction records to drive hypothesis generation and discovery for clinicians and researchers. DGIdb 5.0 is the latest release and includes substantial architectural and functional updates to support integration into clinical and drug discovery pipelines. The DGIdb service architecture has been split into separate client and server applications, enabling consistent data access for users of both the application programming interface (API) and web interface. The new interface was developed in ReactJS, and includes dynamic visualizations and consistency in the display of user interface elements. A GraphQL API has been added to support customizable queries for all drugs, genes, annotations and associated data. Updated documentation provides users with example queries and detailed usage instructions for these new features. In addition, six sources have been added and many existing sources have been updated. Newly added sources include ChemIDplus, HemOnc, NCIt (National Cancer Institute Thesaurus), Drugs@FDA, HGNC (HUGO Gene Nomenclature Committee) and RxNorm. These new sources have been incorporated into DGIdb to provide additional records and enhance annotations of regulatory approval status for therapeutics. Methods for grouping drugs and genes have been expanded upon and developed as independent modular normalizers during import. The updates to these sources and grouping methods have resulted in an improvement in FAIR (findability, accessibility, interoperability and reusability) data representation in DGIdb.