Mapping structural variants to rare disease genes using long-read whole genome sequencing and trait-relevant polygenic scores.

Recent studies have revealed the pervasive landscape of rare structural variants (rSVs) present in human genomes. rSVs can have extreme effects on the expression of proximal genes and, in a rare disease context, have been implicated in patient cases where no diagnostic single nucleotide variant (SNV) was found. Approaches for integrating rSVs to date have focused on targeted approaches in known Mendelian rare disease genes. This approach is intractable for rare diseases with many causal loci or patients with complex, multi-phenotype syndromes. We hypothesized that integrating trait-relevant polygenic scores (PGS) would provide a substantial reduction in the number of candidate disease genes in which to assess rSV effects. We further implemented a method for ranking PGS genes to define a set of core/key genes where a rSV has the potential to exert relatively larger effects on disease risk. Among a subset of patients enrolled in the Genomic Answers for Kids (GA4K) rare disease program (N=497), we used PacBio HiFi long-read whole genome sequencing (lrWGS) to identify rSVs intersecting genes in trait-relevant PGSs. Illustrating our approach in Autism (N=54 cases), we identified 22, 019 deletions, 2,041 duplications, 87,826 insertions, and 214 inversions overlapping putative core/key PGS genes. Additionally, by integrating genomic constraint annotations from gnomAD, we observed that rare duplications overlapping putative core/key PGS genes were frequently in higher constraint regions compared to controls (P = 1×10-03). This difference was not observed in the lowest-ranked gene set (P = 0.15). Overall, our study provides a framework for the annotation of long-read rSVs from lrWGS data and prioritization of disease-linked genomic regions for downstream functional validation of rSV impacts. To enable reuse by other researchers, we have made SV allele frequencies and gene associations freely available.

RNA Sequencing in Disease Diagnosis.

RNA sequencing (RNA-seq) enables the accurate measurement of multiple transcriptomic phenotypes for modeling the impacts of disease variants. Advances in technologies, experimental protocols, and analysis strategies are rapidly expanding the application of RNA-seq to identify disease biomarkers, tissue- and cell-type-specific impacts, and the spatial localization of disease-associated mechanisms. Ongoing international efforts to construct biobank-scale transcriptomic repositories with matched genomic data across diverse population groups are further increasing the utility of RNA-seq approaches by providing large-scale normative reference resources. The availability of these resources, combined with improved computational analysis pipelines, has enabled the detection of aberrant transcriptomic phenotypes underlying rare diseases. Further expansion of these resources, across both somatic and developmental tissues, is expected to soon provide unprecedented insights to resolve disease origin, mechanism of action, and causal gene contributions, suggesting the continued high utility of RNA-seq in disease diagnosis. Expected final online publication date for the Annual Review of Genomics and Human Genetics, Volume 25 is August 2024. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Complex trait associations in rare diseases and impacts on Mendelian variant interpretation.

Emerging evidence implicates common genetic variation - aggregated into polygenic scores (PGS) - impacting the onset and phenotypic presentation of rare diseases. In this study, we quantified individual polygenic liability for 1,151 previously published PGS in a cohort of 2,374 probands enrolled in the Genomic Answers for Kids (GA4K) rare disease study, revealing widespread associations between rare disease phenotypes and PGSs for common complex diseases and traits, blood protein levels, and brain and other organ morphological measurements. We observed increased polygenic burden in probands with variants of unknown significance (VUS) compared to unaffected carrier parents. We further observed an enrichment in overlap between diagnostic and candidate rare disease genes and large-effect PGS genes. Overall, our study supports and expands on previous findings of complex trait associations in rare disease phenotypes and provides a framework for identifying novel candidate rare disease genes and in understanding variable penetrance of candidate Mendelian disease variants.

Extravillous trophoblast cell lineage development is associated with active remodeling of the chromatin landscape.

The extravillous trophoblast cell lineage is a key feature of placentation and successful pregnancy. Knowledge of transcriptional regulation driving extravillous trophoblast cell development is limited. Here, we map the transcriptome and epigenome landscape as well as chromatin interactions of human trophoblast stem cells and their transition into extravillous trophoblast cells. We show that integrating chromatin accessibility, long-range chromatin interactions, transcriptomic, and transcription factor binding motif enrichment enables identification of transcription factors and regulatory mechanisms critical for extravillous trophoblast cell development. We elucidate functional roles for TFAP2C, SNAI1, and EPAS1 in the regulation of extravillous trophoblast cell development. EPAS1 is identified as an upstream regulator of key extravillous trophoblast cell transcription factors, including ASCL2 and SNAI1 and together with its target genes, is linked to pregnancy loss and birth weight. Collectively, we reveal activation of a dynamic regulatory network and provide a framework for understanding extravillous trophoblast cell specification in trophoblast cell lineage development and human placentation.

Integration of rare expression outlier-associated variants improves polygenic risk prediction.

Polygenic risk scores (PRSs) quantify the contribution of multiple genetic loci to an individual's likelihood of a complex trait or disease. However, existing PRSs estimate this likelihood with common genetic variants, excluding the impact of rare variants. Here, we report on a method to identify rare variants associated with outlier gene expression and integrate their impact into PRS predictions for body mass index (BMI), obesity, and bariatric surgery. Between the top and bottom 10%, we observed a 20.8% increase in risk for obesity (p = 3 × 10-14), 62.3% increase in risk for severe obesity (p = 1 × 10-6), and median 5.29 years earlier onset for bariatric surgery (p = 0.008), as a function of expression outlier-associated rare variant burden when controlling for common variant PRS. We show that these predictions were more significant than integrating the effects of rare protein-truncating variants (PTVs), observing a mean 19% increase in phenotypic variance explained with expression outlier-associated rare variants when compared with PTVs (p = 2 × 10-15). We replicated these findings by using data from the Million Veteran Program and demonstrated that PRSs across multiple traits and diseases can benefit from the inclusion of expression outlier-associated rare variants identified through population-scale transcriptome sequencing.

Stimulant Prescribing Error Assessment Rubric Development.

OBJECTIVE: Stimulant medications are used to treat attention-deficit/hyperactivity disorder (ADHD) in adults. However, stimulants are among the most frequently prescribed medications that have a potential to be used nonmedically. We sought to define types of errors associated with treatment of ADHD in adults and to describe a classification rubric for stimulant-related prescribing faults. METHODS: An expert panel conducted a scoping review of the literature and rubric development. The literature search including relevant English language publications indexed in Medline (1990-present, human) and Embase (1990-present, human). In addition, we reviewed relevant documentation such as medication labels and guides containing information related to medications used for the treatment of adult ADHD. The initial version draft rubric was developed by adapting an existing framework for prescribing errors. The expert panel further defined a classification rubric and developed error subcategories, classifications, and descriptions. RESULTS: Two error categories were identified. Category 1 errors are errors resulting from prescribing faults, which further included errors in decision making/judgment; errors related to monitoring for potential harm of stimulants; possible errors: events that should generally be avoided or be used with caution; and suboptimal prescribing. Category 2 errors result from prescription writing, further defined as failure to communicate essential information and transcription errors. CONCLUSIONS: This study provides a comprehensive description of medication errors associated with stimulant and related medications. Our findings have the potential to assist decision making and to tailor delivery programs, recommendations, guidelines, and clinical decision support health information technology on stimulant prescribing and monitoring.

Nonsense-mediated decay is highly stable across individuals and tissues.

Precise interpretation of the effects of rare protein-truncating variants (PTVs) is important for accurate determination of variant impact. Current methods for assessing the ability of PTVs to induce nonsense-mediated decay (NMD) focus primarily on the position of the variant in the transcript. We used RNA sequencing of the Genotype Tissue Expression v.8 cohort to compute the efficiency of NMD using allelic imbalance for 2,320 rare (genome aggregation database minor allele frequency ≤ 1%) PTVs across 809 individuals in 49 tissues. We created an interpretable predictive model using penalized logistic regression in order to evaluate the comprehensive influence of variant annotation, tissue, and inter-individual variation on NMD. We found that variant position, allele frequency, the inclusion of ultra-rare and singleton variants, and conservation were predictive of allelic imbalance. Furthermore, we found that NMD effects were highly concordant across tissues and individuals. Due to this high consistency, we demonstrate in silico that utilizing peripheral tissues or cell lines provides accurate prediction of NMD for PTVs.

Population-scale tissue transcriptomics maps long non-coding RNAs to complex disease.

Long non-coding RNA (lncRNA) genes have well-established and important impacts on molecular and cellular functions. However, among the thousands of lncRNA genes, it is still a major challenge to identify the subset with disease or trait relevance. To systematically characterize these lncRNA genes, we used Genotype Tissue Expression (GTEx) project v8 genetic and multi-tissue transcriptomic data to profile the expression, genetic regulation, cellular contexts, and trait associations of 14,100 lncRNA genes across 49 tissues for 101 distinct complex genetic traits. Using these approaches, we identified 1,432 lncRNA gene-trait associations, 800 of which were not explained by stronger effects of neighboring protein-coding genes. This included associations between lncRNA quantitative trait loci and inflammatory bowel disease, type 1 and type 2 diabetes, and coronary artery disease, as well as rare variant associations to body mass index.

Identification of rare and common regulatory variants in pluripotent cells using population-scale transcriptomics.

Induced pluripotent stem cells (iPSCs) are an established cellular system to study the impact of genetic variants in derived cell types and developmental contexts. However, in their pluripotent state, the disease impact of genetic variants is less well known. Here, we integrate data from 1,367 human iPSC lines to comprehensively map common and rare regulatory variants in human pluripotent cells. Using this population-scale resource, we report hundreds of new colocalization events for human traits specific to iPSCs, and find increased power to identify rare regulatory variants compared with somatic tissues. Finally, we demonstrate how iPSCs enable the identification of causal genes for rare diseases.

Transcriptomic signatures across human tissues identify functional rare genetic variation.

Rare genetic variants are abundant across the human genome, and identifying their function and phenotypic impact is a major challenge. Measuring aberrant gene expression has aided in identifying functional, large-effect rare variants (RVs). Here, we expanded detection of genetically driven transcriptome abnormalities by analyzing gene expression, allele-specific expression, and alternative splicing from multitissue RNA-sequencing data, and demonstrate that each signal informs unique classes of RVs. We developed Watershed, a probabilistic model that integrates multiple genomic and transcriptomic signals to predict variant function, validated these predictions in additional cohorts and through experimental assays, and used them to assess RVs in the UK Biobank, the Million Veterans Program, and the Jackson Heart Study. Our results link thousands of RVs to diverse molecular effects and provide evidence to associate RVs affecting the transcriptome with human traits.

Properties of structural variants and short tandem repeats associated with gene expression and complex traits.

Structural variants (SVs) and short tandem repeats (STRs) comprise a broad group of diverse DNA variants which vastly differ in their sizes and distributions across the genome. Here, we identify genomic features of SV classes and STRs that are associated with gene expression and complex traits, including their locations relative to eGenes, likelihood of being associated with multiple eGenes, associated eGene types (e.g., coding, noncoding, level of evolutionary constraint), effect sizes, linkage disequilibrium with tagging single nucleotide variants used in GWAS, and likelihood of being associated with GWAS traits. We identify a set of high-impact SVs/STRs associated with the expression of three or more eGenes via chromatin loops and show that they are highly enriched for being associated with GWAS traits. Our study provides insights into the genomic properties of structural variant classes and short tandem repeats that are associated with gene expression and human traits.

Effects of multicomponent primary care-based intervention on immunization rates and missed opportunities to vaccinate adults.

BACKGROUND: Adult immunization rates are below Healthy People 2020 targets. Our objective was to evaluate the effectiveness of a multicomponent intervention to improve adult immunization rates. METHODS: This prospective interventional before-and-after non-randomized study was conducted through the American Academy of Family Physicians National Research Network with 43 primary care physicians from a large multi-specialty healthcare organization (multicomponent intervention group n = 23; comparator group n = 20) in the United States. The multicomponent intervention included provider reminders, quarterly provider-level performance reports, provider education, patient visual aid materials, and standing orders on adult pneumococcal, influenza, and zoster immunizations. We assessed individual and comparative provider-level vaccination rates and missed opportunities detailing concordance with targets established by Healthy People 2020 for pneumococcal, influenza, and zoster immunizations. RESULTS: Vaccination rates increased after 12 months in intervention and comparator groups respectively for: a). influenza from 44.4 ± 16.7 to 51.3% ± 12.9% (by 6.9 percentage points, p = 0.001) and from 35.1 ± 19.1 to 41.3% ± 14.2%, (by 6.2 percentage points, p = 0.01); b). pneumococcal vaccinations in older adults from 62.8 ± 17.6 to 81.4% ± 16.6% (by 18.6 percentage points, for p < 0.0001) and from 55.9 ± 20.0 to 72.7% ± 18.4% (by 16.7 percentage points, p < 0.0001); and c). zoster from 37.1 ± 13.4 to 41.9% ± 13.1% (by 4.8 percentage points, p < 0.0001) and from 35.0 ± 18.7 to 42.3% ± 20.9% (7.3 percentage points, p = 0.001). Pneumococcal vaccinations in adults at risk did not change from baseline in intervention group (35.7 ± 19.6 to 34.5% ± 19.0%, p = 0.3) and improved slightly in comparator group (24.3 ± 20.1 to 28.2% ± 20.0%, p = 0.003). Missed opportunities reduced after 12 months, most noticeably, for: a). for influenza from 57.7 to 48.6% (by 9.1 percentage points, p < 0.0001) and from 69.7 to 59.6% (by 10.1 percentage points, p < 0.0001); b). pneumococcal vaccinations in older adults from 18.1 to 11.5% (by 6.6 percentage points p < 0.0001) and from 24.6 to 20.4% (by 4.3 percentage points, p < 0.0001) in intervention and comparator groups respectively. CONCLUSIONS: Multicomponent interventions show promise in improving vaccination rates and reducing missed opportunities in older adults for pneumococcal and zoster vaccines and vaccination against influenza. Provider reminders remain the most effective strategy when delivered either as a component of these interventions or alone.

Identification of rare-disease genes using blood transcriptome sequencing and large control cohorts.

It is estimated that 350 million individuals worldwide suffer from rare diseases, which are predominantly caused by mutation in a single gene1. The current molecular diagnostic rate is estimated at 50%, with whole-exome sequencing (WES) among the most successful approaches2-5. For patients in whom WES is uninformative, RNA sequencing (RNA-seq) has shown diagnostic utility in specific tissues and diseases6-8. This includes muscle biopsies from patients with undiagnosed rare muscle disorders6,9, and cultured fibroblasts from patients with mitochondrial disorders7. However, for many individuals, biopsies are not performed for clinical care, and tissues are difficult to access. We sought to assess the utility of RNA-seq from blood as a diagnostic tool for rare diseases of different pathophysiologies. We generated whole-blood RNA-seq from 94 individuals with undiagnosed rare diseases spanning 16 diverse disease categories. We developed a robust approach to compare data from these individuals with large sets of RNA-seq data for controls (n = 1,594 unrelated controls and n = 49 family members) and demonstrated the impacts of expression, splicing, gene and variant filtering strategies on disease gene identification. Across our cohort, we observed that RNA-seq yields a 7.5% diagnostic rate, and an additional 16.7% with improved candidate gene resolution.

SNPs2ChIP: Latent Factors of ChIP-seq to infer functions of non-coding SNPs.

Genetic variations of the human genome are linked to many disease phenotypes. While whole-genome sequencing and genome-wide association studies (GWAS) have uncovered a number of genotype-phenotype associations, their functional interpretation remains challenging given most single nucleotide polymorphisms (SNPs) fall into the non-coding region of the genome. Advances in chromatin immunoprecipitation sequencing (ChIP-seq) have made large-scale repositories of epigenetic data available, allowing investigation of coordinated mechanisms of epigenetic markers and transcriptional regulation and their influence on biological function. To address this, we propose SNPs2ChIP, a method to infer biological functions of non-coding variants through unsupervised statistical learning methods applied to publicly-available epigenetic datasets. We systematically characterized latent factors by applying singular value decomposition to ChIP-seq tracks of lymphoblastoid cell lines, and annotated the biological function of each latent factor using the genomic region enrichment analysis tool. Using these annotated latent factors as reference, we developed SNPs2ChIP, a pipeline that takes genomic region(s) as an input, identifies the relevant latent factors with quantitative scores, and returns them along with their inferred functions. As a case study, we focused on systemic lupus erythematosus and demonstrated our method's ability to infer relevant biological function. We systematically applied SNPs2ChIP on publicly available datasets, including known GWAS associations from the GWAS catalogue and ChIP-seq peaks from a previously published study. Our approach to leverage latent patterns across genome-wide epigenetic datasets to infer the biological function will advance understanding of the genetics of human diseases by accelerating the interpretation of non-coding genomes.

Proficiency Testing of Standardized Samples Shows Very High Interlaboratory Agreement for Clinical Next-Generation Sequencing-Based Oncology Assays.

CONTEXT.­: Next-generation sequencing-based assays are being increasingly used in the clinical setting for the detection of somatic variants in solid tumors, but limited data are available regarding the interlaboratory performance of these assays. OBJECTIVE.­: To examine proficiency testing data from the initial College of American Pathologists (CAP) Next-Generation Sequencing Solid Tumor survey to report on laboratory performance. DESIGN.­: CAP proficiency testing results from 111 laboratories were analyzed for accuracy and associated assay performance characteristics. RESULTS.­: The overall accuracy observed for all variants was 98.3%. Rare false-negative results could not be attributed to sequencing platform, selection method, or other assay characteristics. The median and average of the variant allele fractions reported by the laboratories were within 10% of those orthogonally determined by digital polymerase chain reaction for each variant. The median coverage reported at the variant sites ranged from 1922 to 3297. CONCLUSIONS.­: Laboratories demonstrated an overall accuracy of greater than 98% with high specificity when examining 10 clinically relevant somatic single-nucleotide variants with a variant allele fraction of 15% or greater. These initial data suggest excellent performance, but further ongoing studies are needed to evaluate the performance of lower variant allele fractions and additional variant types.

Recruiting primary care practices for practice-based research: a case study of a group-randomized study (TRANSLATE CKD) recruitment process.

Background: We assessed the challenging process of recruiting primary care practices in a practice-based research study. Methods: In this descriptive case study of recruitment data collected for a large practice-based study (TRANSLATE CKD), 48 single or multiple-site health care organizations in the USA with a total of 114 practices were invited to participate. We collected quantitative and qualitative measures of recruitment process and outcomes for the first 25 practices recruited. Information about 13 additional practices is not provided due to staff transitions and limited data collection resources. Results: Initial outreach was made to 114 practices (from 48 organizations, 41% small); 52 (45%) practices responded with interest. Practices enrolled in the study (n = 25) represented 22% of the total outreach number, or 48% of those initially interested. Average time to enroll was 71 calendar days (range 11-107). There was no difference in the number of days practices remained under recruitment, based on enrolled versus not enrolled (44.8 ± 30.4 versus 46.8 ± 25.4 days, P = 0.86) or by the organization size, i.e. large versus small (defined by having ≤4 distinct practices; 52 ± 23.6 versus 43.6 ± 27.8 days; P = 0.46). The most common recruitment barriers were administrative, e.g. lack of perceived direct organizational benefit, and were more prominent among large organizations. Conclusions: Despite the general belief that the research topic, invitation method, and interest in research may facilitate practice recruitment, our results suggest that most of the recruitment challenges represent managerial challenges. Future research projects may need to consider relevant methodologies from businesses administration and marketing fields.

Missed opportunities for improving practice performance in adult immunizations: a meta-narrative review of the literature.

BACKGROUND: We sought to characterize how the term "missed opportunities" is reported in the literature in the context of immunization rates and to assess how missed opportunities can be operationalized. METHODS: Peer-reviewed literature searches were conducted in April - May, 2015, to answer: "What methods research studies used to operationalize missed opportunities to vaccinate?" A meta-narrative review methodology was used. RESULTS: Seven studies met inclusion criteria. The methodologies for quantifying missed opportunities fell into two general categories based on: 1. the number of healthcare encounters per patient without appropriate vaccination services, defined as a number of visits per patient with no vaccination related services (Missed opportunities per patient); 2. vaccination status as "non-vaccinated" among a group of patients who had a healthcare encounter where the vaccination should/could have had happened (Missed opportunities per population). CONCLUSIONS: Our study provided an initial overview of the methods reported in the literature, and concluded that the quantifiable missed opportunity holds promise as a measurable outcome (variable) for research and quality improvement projects aimed to increase adult immunization recommendation and uptake in primary care.

Preventing the voltage drop: keeping practice-based research network (PBRN) practices engaged in studies.

INTRODUCTION: Practice-based research continues to evolve and has become a major methodology for many pragmatic studies. While early practice-based network projects were usually short term, current studies often introduce or compare practice innovations that require long-term evaluation. That change requires that practice sites remain engaged in research work for up to 5 years, a time that can allow for a significant "voltage drop," or decline in active participation. METHODS: Over the past 15 years we have developed and adapted several strategies to facilitate and encourage the continued active engagement of practices in practice-based research network studies of up to 5 years' duration. The concepts, details, evaluation, and results (when available) of the strategies are described. RESULTS: Eight strategies that enhance practice sites' attention to enrollment, data collection and continued use of the implemented practice change are described. CONCLUSION: The loss of momentum, or "voltage drop," that happens in longer-term practice-based research network studies can be addressed using multiple strategies.

Protocol for the asthma tools study: a pragmatic practice-based research network trial.

BACKGROUND: Asthma is common among children, adolescents, and adults. However, management of asthma often fails to follow evidence-based guidelines. Control assessments have been developed, validated against expert opinion, and disseminated. However, in primary care, assessment of control is only one step in asthma management. To facilitate integration of the evidence-based guidelines into practice, tools should also guide the next steps in care. The Asthma APGAR tools do just that, incorporating a control assessment as well as assessment of the most common reasons for inadequate and poor control. The Asthma APGAR tool is also linked to a care algorithm based on the 2007 National Heart, Lung, and Blood Institute asthma guidelines. The objective of this study is to assess the impact of implementation of the Asthma APGAR on patient asthma outcomes in primary care practices. METHODS: A total of 1400 patients aged 5-60 years with physician-diagnosed asthma are enrolled in 20 practice-based research network (PBRN) practices randomized to intervention or usual care. The primary outcomes are changes in patient self-reported asthma control, asthma-related quality of life, and rates of exacerbations documented in medical records over the 18-24 months of enrollment. Process measures related to implementation of the Asthma APGAR system into daily care will also be assessed using review of medical records. Qualitative assessments will be used to explore barriers to and facilitators for integrating the Asthma APGAR tools into daily practice in primary care. DISCUSSION: Data from this pivotal pragmatic study are intended to demonstrate the importance of linking assessment of asthma and management tools to improve asthma-related patient outcomes. The study is an effectiveness trial done in real-world PBRN practices using patient-oriented outcome measures, making it generalizable to the largest possible group of asthma care providers and primary care clinics.