Microbial Cell-Free DNA Identifies the Causative Pathogen in Infective Endocarditis and Remains Detectable Longer Than Conventional Blood Culture in Patients with Prior Antibiotic Therapy.

BACKGROUND: The diagnosis of infective endocarditis (IE) can be difficult, particularly if blood cultures fail to yield a pathogen. This study evaluates the potential utility of microbial cell-free DNA (mcfDNA) as a tool to identify the microbial etiology of IE. METHODS: Blood samples from patients with suspected IE were serially collected. mcfDNA was extracted from plasma and underwent next-generation sequencing. Reads were aligned against a library containing DNA sequences belonging to >1400 different pathogens. mcfDNA from organisms present above a statistical threshold were reported and quantified in molecules per milliliter (MPM). Additional mcfDNA was collected on each subject every 2-3 days for a total of 7 collections or until discharge. RESULTS: Of 30 enrolled patients with suspected IE, 23 had definite IE, 2 had possible IE, and IE was rejected in 5 patients by modified Duke Criteria. Only the 23 patients with definite IE were included for analysis. Both mcfDNA and blood cultures achieved a sensitivity of 87%. The median duration of positivity from antibiotic treatment initiation was estimated to be approximately 38.1 days for mcfDNA versus 3.7 days for blood culture (proportional odds, 2.952; P = .02771), using a semiparametric survival analysis. mcfDNA (log10) levels significantly declined (-0.3 MPM log10 units, 95% credible interval -0.45 to -0.14) after surgical source control was performed (pre- vs postprocedure, posterior probability >0.99). CONCLUSION: mcfDNA accurately identifies the microbial etiology of IE. Sequential mcfDNA levels may ultimately help to individualize therapy by estimating a patient's burden of infection and response to treatment.

Estimating the causal effects of multiple intermittent treatments with application to COVID-19.

To draw real-world evidence about the comparative effectiveness of multiple time-varying treatments on patient survival, we develop a joint marginal structural survival model and a novel weighting strategy to account for time-varying confounding and censoring. Our methods formulate complex longitudinal treatments with multiple start/stop switches as the recurrent events with discontinuous intervals of treatment eligibility. We derive the weights in continuous time to handle a complex longitudinal dataset without the need to discretize or artificially align the measurement times. We further use machine learning models designed for censored survival data with time-varying covariates and the kernel function estimator of the baseline intensity to efficiently estimate the continuous-time weights. Our simulations demonstrate that the proposed methods provide better bias reduction and nominal coverage probability when analyzing observational longitudinal survival data with irregularly spaced time intervals, compared to conventional methods that require aligned measurement time points. We apply the proposed methods to a large-scale COVID-19 dataset to estimate the causal effects of several COVID-19 treatments on the composite of in-hospital mortality and ICU admission.

Long-read HiFi sequencing of NUDT15: Phased full-gene haplotyping and pharmacogenomic allele discovery.

To determine the phase of NUDT15 sequence variants for more comprehensive star (*) allele diplotyping, we developed a novel long-read single-molecule real-time HiFi amplicon sequencing method. A 10.5 kb NUDT15 amplicon assay was validated using reference material positive controls and additional samples for specimen type and blinded accuracy assessment. Triplicate NUDT15 HiFi sequencing of two reference material samples had nonreference genotype concordances of >99.9%, indicating that the assay is robust. Notably, short-read genome sequencing of a subset of samples was unable to determine the phase of star (*) allele-defining NUDT15 variants, resulting in ambiguous diplotype results. In contrast, long-read HiFi sequencing phased all variants across the NUDT15 amplicons, including a *2/*9 diplotype that previously was characterized as *1/*2 in the 1000 Genomes Project v3 data set. Assay throughput was also tested using 8.5 kb amplicons from 100 Ashkenazi Jewish individuals, which identified a novel NUDT15 *1 suballele (c.-121G>A) and a rare likely deleterious coding variant (p.Pro129Arg). Both novel alleles were Sanger confirmed and assigned as *1.007 and *20, respectively, by the PharmVar Consortium. Taken together, NUDT15 HiFi amplicon sequencing is an innovative method for phased full-gene characterization and novel allele discovery, which could improve NUDT15 pharmacogenomic testing and subsequent phenotype prediction.

Microbial Cell-Free DNA Identifies Etiology of Bloodstream Infections, Persists Longer Than Conventional Blood Cultures, and Its Duration of Detection Is Associated With Metastatic Infection in Patients With Staphylococcus aureus and Gram-Negative Bacteremia.

BACKGROUND: Microbial cell-free DNA (mcfDNA) sequencing of plasma can identify the presence of a pathogen in a host. In this study, we evaluated the duration of pathogen detection by mcfDNA sequencing vs conventional blood culture in patients with bacteremia. METHODS: Blood samples from patients with culture-confirmed bloodstream infection were collected within 24 hours of the index positive blood culture and 48 to 72 hours thereafter. mcfDNA was extracted from plasma, and next-generation sequencing was applied. Reads were aligned against a curated pathogen database. Statistical significance was defined with Bonferroni adjustment for multiple comparisons (P < .0033). RESULTS: A total of 175 patients with Staphylococcus aureus bacteremia (n = 66), gram-negative bacteremia (n = 74), or noninfected controls (n = 35) were enrolled. The overall sensitivity of mcfDNA sequencing compared with index blood culture was 89.3% (125 of 140), and the specificity was 74.3%. Among patients with bacteremia, pathogen-specific mcfDNA remained detectable for significantly longer than conventional blood cultures (median 15 days vs 2 days; P < .0001). Each additional day of mcfDNA detection significantly increased the odds of metastatic infection (odds ratio, 2.89; 95% confidence interval, 1.53-5.46; P = .0011). CONCLUSIONS: Pathogen mcfDNA identified the bacterial etiology of bloodstream infection for a significantly longer interval than conventional cultures, and its duration of detection was associated with increased risk for metastatic infection. mcfDNA could play a role in the diagnosis of partially treated endovascular infections.

Pharmacogenomic education among genetic counseling training programs in North America.

The increasing number of genetic counselors participating directly in clinical pharmacogenomic post-test counseling prompted our evaluation of pharmacogenomic education across genetic counseling training programs in North America. Thirty-one program leadership participants from both the United States (U.S.) and Canada responded to a survey assessing pharmacogenomics education and the role of genetic counselors. Eighty-five percent of respondents agreed pharmacogenomics is currently within the scope of genetic counseling practice, and 96.3% indicated their training programs currently provide education on pharmacogenomics, with the majority reporting < 7 hr of education. Lectures on pharmacogenomics were the most common method for didactics; however, some programs also included practical modalities (e.g., case studies, clinical rotations) and online resources. Barriers to expanding pharmacogenomic education included the constrained timeline of training, and lack of resources and local expertise. Moreover, participants suggested that genetic counselors ideally should be able to order pharmacogenomic tests and counsel patients on pharmacogenomics, including result interpretation, as they believe pharmacogenomics does fall within the scope of practice of genetic counseling. Our novel results also confirm that training program leadership support a pharmacogenomic service delivery model that includes a combined effort between genetic counselors and pharmacists to utilize their synergistic expertise. However, this model likely still necessitates expanding pharmacogenomic didactics in genetic counseling training programs through more practical training and/or by leveraging online pharmacogenomic courses dedicated to supporting clinical implementation.

Development and Analytical Validation of a 29 Gene Clinical Pharmacogenetic Genotyping Panel: Multi-Ethnic Allele and Copy Number Variant Detection.

To develop a novel pharmacogenetic genotyping panel, a multidisciplinary team evaluated available evidence and selected 29 genes implicated in interindividual drug response variability, including 130 sequence variants and additional copy number variants (CNVs). Of the 29 genes, 11 had guidelines published by the Clinical Pharmacogenetics Implementation Consortium. Targeted genotyping and CNV interrogation were accomplished by multiplex single-base extension using the MassARRAY platform (Agena Biosciences) and multiplex ligation-dependent probe amplification (MRC Holland), respectively. Analytical validation of the panel was accomplished by a strategic combination of > 500 independent tests performed on 170 unique reference material DNA samples, which included sequence variant and CNV accuracy, reproducibility, and specimen (blood, saliva, and buccal swab) controls. Among the accuracy controls were 32 samples from the 1000 Genomes Project that were selected based on their enrichment of sequence variants included in the pharmacogenetic panel (VarCover.org). Coupled with publicly available samples from the Genetic Testing Reference Materials Coordination Program (GeT-RM), accuracy validation material was available for the majority (77%) of interrogated sequence variants (100% with average allele frequencies > 0.1%), as well as additional structural alleles with unique copy number signatures (e.g., CYP2D6*5, *13, *36, *68; CYP2B6*29; and CYP2C19*36). Accuracy and reproducibility for both genotyping and copy number were > 99.9%, indicating that the optimized panel platforms were precise and robust. Importantly, multi-ethnic allele frequencies of the interrogated variants indicate that the vast majority of the general population carries at least one of these clinically relevant pharmacogenetic variants, supporting the implementation of this panel for pharmacogenetic research and/or clinical implementation programs.

Phased Haplotype Resolution of the SLC6A4 Promoter Using Long-Read Single Molecule Real-Time (SMRT) Sequencing.

The SLC6A4 gene has been implicated in psychiatric disorder susceptibility and antidepressant response variability. The SLC6A4 promoter is defined by a variable number of homologous 20-24 bp repeats (5-HTTLPR), and long (L) and short (S) alleles are associated with higher and lower expression, respectively. However, this insertion/deletion variant is most informative when considered as a haplotype with the rs25531 and rs25532 variants. Therefore, we developed a long-read single molecule real-time (SMRT) sequencing method to interrogate the SLC6A4 promoter region. A total of 120 samples were subjected to SLC6A4 long-read SMRT sequencing, primarily selected based on available short-read sequencing data. Short-read genome sequencing from the 1000 Genomes (1KG) Project (~5X) and the Genetic Testing Reference Material Coordination Program (~45X), as well as high-depth short-read capture-based sequencing (~330X), could not identify the 5-HTTLPR short (S) allele, nor could short-read sequencing phase any identified variants. In contrast, long-read SMRT sequencing unambiguously identified the 5-HTTLPR short (S) allele (frequency of 0.467) and phased SLC6A4 promoter haplotypes. Additionally, discordant rs25531 genotypes were reviewed and determined to be short-read errors. Taken together, long-read SMRT sequencing is an innovative and robust method for phased resolution of the SLC6A4 promoter, which could enable more accurate pharmacogenetic testing for both research and clinical applications.

Members of the public in the USA, UK, Canada and Australia expressing genetic exceptionalism say they are more willing to donate genomic data.

Public acceptance is critical for sharing of genomic data at scale. This paper examines how acceptance of data sharing pertains to the perceived similarities and differences between DNA and other forms of personal data. It explores the perceptions of representative publics from the USA, Canada, the UK and Australia (n = 8967) towards the donation of DNA and health data. Fifty-two percent of this public held 'exceptionalist' views about genetics (i.e., believed DNA is different or 'special' compared to other types of medical information). This group was more likely to be familiar with or have had personal experience with genomics and to perceive DNA information as having personal as well as clinical and scientific value. Those with personal experience with genetics and genetic exceptionalist views were nearly six times more likely to be willing to donate their anonymous DNA and medical information for research than other respondents. Perceived harms from re-identification did not appear to dissuade publics from being willing to participate in research. The interplay between exceptionalist views about genetics and the personal, scientific and clinical value attributed to data would be a valuable focus for future research.

VarCover: Allele Min-Set Cover Software.

To facilitate reference-material selection for clinical genetic testing laboratories, we developed VarCover, open-source software hosted on GitHub, which accepts a file of variants and returns an approximately minimum set (min-set) of samples covering the targeted alleles. VarCover employs the SetCoverPy package, sample weights, and preselection of singleton-possessing samples to efficiently solve the min-set cover problem. As a test case, we attempted to find a min-set of reference samples from the 1000 Genomes Project to cover 237 variants considered putatively pathogenic (of which 12 were classified as pathogenic or likely pathogenic) in the original 56 medically actionable genes recommended by the American College of Medical Genetics and Genomics (ACMG). The number of samples, number of alleles, and processing time were measured in subsets of the 237 target alleles. VarCover identified 140 reference-material samples from the 1000 Genomes Project covering the 237 alleles in the 56 ACMG-recommended genes. Sample weights derived from the minor allele frequency spectrum increased the number of alleles in the solution set. Preselection of samples that possessed singleton target alleles reduced computational processing time when the target set size exceeded 100 alleles. VarCover provides a simple programmatic interface for identifying an approximately min-set of reference samples, thereby reducing clinical laboratory effort and molecular genetic test-validation costs.

'Your DNA, Your Say': global survey gathering attitudes toward genomics: design, delivery and methods.

Our international study, 'Your DNA, Your Say', uses film and an online cross-sectional survey to gather public attitudes toward the donation, access and sharing of DNA information. We describe the methodological approach used to create an engaging and bespoke survey, suitable for translation into many different languages. We address some of the particular challenges in designing a survey on the subject of genomics. In order to understand the significance of a genomic result, researchers and clinicians alike use external databases containing DNA and medical information from thousands of people. We ask how publics would like their 'anonymous' data to be used (or not to be used) and whether they are concerned by the potential risks of reidentification; the results will be used to inform policy.

The asthma mobile health study, smartphone data collected using ResearchKit.

Widespread adoption of smart mobile platforms coupled with a growing ecosystem of sensors including passive location tracking and the ability to leverage external data sources create an opportunity to generate an unprecedented depth of data on individuals. Mobile health technologies could be utilized for chronic disease management as well as research to advance our understanding of common diseases, such as asthma. We conducted a prospective observational asthma study to assess the feasibility of this type of approach, clinical characteristics of cohorts recruited via a mobile platform, the validity of data collected, user retention patterns, and user data sharing preferences. We describe data and descriptive statistics from the Asthma Mobile Health Study, whereby participants engaged with an iPhone application built using Apple's ResearchKit framework. Data from 6346 U.S. participants, who agreed to share their data broadly, have been made available for further research. These resources have the potential to enable the research community to work collaboratively towards improving our understanding of asthma as well as mobile health research best practices.

APPLaUD: access for patients and participants to individual level uninterpreted genomic data.

BACKGROUND: There is a growing support for the stance that patients and research participants should have better and easier access to their raw (uninterpreted) genomic sequence data in both clinical and research contexts. MAIN BODY: We review legal frameworks and literature on the benefits, risks, and practical barriers of providing individuals access to their data. We also survey genomic sequencing initiatives that provide or plan to provide individual access. Many patients and research participants expect to be able to access their health and genomic data. Individuals have a legal right to access their genomic data in some countries and contexts. Moreover, increasing numbers of participatory research projects, direct-to-consumer genetic testing companies, and now major national sequencing initiatives grant individuals access to their genomic sequence data upon request. CONCLUSION: Drawing on current practice and regulatory analysis, we outline legal, ethical, and practical guidance for genomic sequencing initiatives seeking to offer interested patients and participants access to their raw genomic data.

Estimating Local Costs Associated With Clostridium difficile Infection Using Machine Learning and Electronic Medical Records.

BACKGROUND Reported per-patient costs of Clostridium difficile infection (CDI) vary by 2 orders of magnitude among different hospitals, implying that infection control officers need precise, local analyses to guide rational decision making between interventions. OBJECTIVE We sought to comprehensively estimate changes in length of stay (LOS) attributable to CDI at a single urban tertiary-care facility using only data automatically extractable from the electronic medical record (EMR). METHODS We performed a retrospective cohort study of 171,938 visits spanning a 7-year period. In total, 23,968 variables were extracted from EMR data recorded within 24 hours of admission to train elastic-net regularized logistic regression models for propensity score matching. To address time-dependent bias (reverse causation), we separately stratified comparisons by time of infection, and we fit multistate models. RESULTS The estimated difference in median LOS for propensity-matched cohorts varied from 3.1 days (95% CI, 2.2-3.9) to 10.1 days (95% CI, 7.3-12.2) depending on the case definition; however, dependency of the estimate on time to infection was observed. Stratification by time to first positive toxin assay, excluding probable community-acquired infections, showed a minimum excess LOS of 3.1 days (95% CI, 1.7-4.4). Under the same case definition, the multistate model averaged an excess LOS of 3.3 days (95% CI, 2.6-4.0). CONCLUSIONS In this study, 2 independent time-to-infection adjusted methods converged on similar excess LOS estimates. Changes in LOS can be extrapolated to marginal dollar costs by multiplying by average costs of an inpatient day. Infection control officers can leverage automatically extractable EMR data to estimate costs of CDI at their own institutions. Infect Control Hosp Epidemiol. 2017;38:1478-1486.

Institutional profile: translational pharmacogenomics at the Icahn School of Medicine at Mount Sinai.

For almost 50 years, the Icahn School of Medicine at Mount Sinai has continually invested in genetics and genomics, facilitating a healthy ecosystem that provides widespread support for the ongoing programs in translational pharmacogenomics. These programs can be broadly cataloged into discovery, education, clinical implementation and testing, which are collaboratively accomplished by multiple departments, institutes, laboratories, companies and colleagues. Focus areas have included drug response association studies and allele discovery, multiethnic pharmacogenomics, personalized genotyping and survey-based education programs, pre-emptive clinical testing implementation and novel assay development. This overview summarizes the current state of translational pharmacogenomics at Mount Sinai, including a future outlook on the forthcoming expansions in overall support, research and clinical programs, genomic technology infrastructure and the participating faculty.

Multiplexed analysis of fixed tissue RNA using Ligation in situ Hybridization.

Clinical tissues are prepared for histological analysis and long-term storage via formalin fixation and paraffin embedding (FFPE). The FFPE process results in fragmentation and chemical modification of RNA, rendering it less suitable for analysis by techniques that rely on reverse transcription (RT) such as RT-qPCR and RNA-Seq. Here we describe a broadly applicable technique called 'Ligation in situ Hybridization' ('LISH'), which is an alternative methodology for the analysis of FFPE RNA. LISH utilizes the T4 RNA Ligase 2 to efficiently join adjacent chimeric RNA-DNA probe pairs hybridized in situ on fixed RNA target sequences. Subsequent treatment with RNase H releases RNA-templated ligation products into solution for downstream analysis. We demonstrate several unique advantages of LISH-based assays using patient-derived FFPE tissue. These include >100-plex capability, compatibility with common histochemical stains and suitability for analysis of decade-old materials and exceedingly small microdissected tissue fragments. High-throughput DNA sequencing modalities, including single molecule sequencing, can be used to analyze ligation products from complex panels of LISH probes ('LISH-seq'), which can be amplified efficiently and with negligible bias. LISH analysis of FFPE RNA is a novel methodology with broad applications that range from multiplexed gene expression analysis to the sensitive detection of infectious organisms.

Sequencing the CYP2D6 gene: from variant allele discovery to clinical pharmacogenetic testing.

CYP2D6 is one of the most studied enzymes in the field of pharmacogenetics. The CYP2D6 gene is highly polymorphic with over 100 catalogued star (*) alleles, and clinical CYP2D6 testing is increasingly accessible and supported by practice guidelines. However, the degree of variation at the CYP2D6 locus and homology with its pseudogenes make interrogating CYP2D6 by short-read sequencing challenging. Moreover, accurate prediction of CYP2D6 metabolizer status necessitates analysis of duplicated alleles when an increased copy number is detected. These challenges have recently been overcome by long-read CYP2D6 sequencing; however, such platforms are not widely available. This review highlights the genomic complexities of CYP2D6, current sequencing methods and the evolution of CYP2D6 from allele discovery to clinical pharmacogenetic testing.

The Asthma Mobile Health Study, a large-scale clinical observational study using ResearchKit.

The feasibility of using mobile health applications to conduct observational clinical studies requires rigorous validation. Here, we report initial findings from the Asthma Mobile Health Study, a research study, including recruitment, consent, and enrollment, conducted entirely remotely by smartphone. We achieved secure bidirectional data flow between investigators and 7,593 participants from across the United States, including many with severe asthma. Our platform enabled prospective collection of longitudinal, multidimensional data (e.g., surveys, devices, geolocation, and air quality) in a subset of users over the 6-month study period. Consistent trending and correlation of interrelated variables support the quality of data obtained via this method. We detected increased reporting of asthma symptoms in regions affected by heat, pollen, and wildfires. Potential challenges with this technology include selection bias, low retention rates, reporting bias, and data security. These issues require attention to realize the full potential of mobile platforms in research and patient care.

METHODS FOR CLUSTERING TIME SERIES DATA ACQUIRED FROM MOBILE HEALTH APPS.

In our recent Asthma Mobile Health Study (AMHS), thousands of asthma patients across the country contributed medical data through the iPhone Asthma Health App on a daily basis for an extended period of time. The collected data included daily self-reported asthma symptoms, symptom triggers, and real time geographic location information. The AMHS is just one of many studies occurring in the context of now many thousands of mobile health apps aimed at improving wellness and better managing chronic disease conditions, leveraging the passive and active collection of data from mobile, handheld smart devices. The ability to identify patient groups or patterns of symptoms that might predict adverse outcomes such as asthma exacerbations or hospitalizations from these types of large, prospectively collected data sets, would be of significant general interest. However, conventional clustering methods cannot be applied to these types of longitudinally collected data, especially survey data actively collected from app users, given heterogeneous patterns of missing values due to: 1) varying survey response rates among different users, 2) varying survey response rates over time of each user, and 3) non-overlapping periods of enrollment among different users. To handle such complicated missing data structure, we proposed a probability imputation model to infer missing data. We also employed a consensus clustering strategy in tandem with the multiple imputation procedure. Through simulation studies under a range of scenarios reflecting real data conditions, we identified favorable performance of the proposed method over other strategies that impute the missing value through low-rank matrix completion. When applying the proposed new method to study asthma triggers and symptoms collected as part of the AMHS, we identified several patient groups with distinct phenotype patterns. Further validation of the methods described in this paper might be used to identify clinically important patterns in large data sets with complicated missing data structure, improving the ability to use such data sets to identify at-risk populations for potential intervention.

Whole-Genome Sequencing of a Healthy Aging Cohort.

Studies of long-lived individuals have revealed few genetic mechanisms for protection against age-associated disease. Therefore, we pursued genome sequencing of a related phenotype-healthy aging-to understand the genetics of disease-free aging without medical intervention. In contrast with studies of exceptional longevity, usually focused on centenarians, healthy aging is not associated with known longevity variants, but is associated with reduced genetic susceptibility to Alzheimer and coronary artery disease. Additionally, healthy aging is not associated with a decreased rate of rare pathogenic variants, potentially indicating the presence of disease-resistance factors. In keeping with this possibility, we identify suggestive common and rare variant genetic associations implying that protection against cognitive decline is a genetic component of healthy aging. These findings, based on a relatively small cohort, require independent replication. Overall, our results suggest healthy aging is an overlapping but distinct phenotype from exceptional longevity that may be enriched with disease-protective genetic factors. VIDEO ABSTRACT.

An Examination of the Relationship between Lipid Levels and Associated Genetic Markers across Racial/Ethnic Populations in the Multi-Ethnic Study of Atherosclerosis.

Large genome-wide association studies have reported hundreds of genetic markers associated with lipid levels. However, the discovery and estimated effect of variants at these loci, derived from samples of exclusively European descent, may not generalize to the majority of the world populations. We examined the collective strength of association among these loci in a diverse set of U.S. populations from the Multi-Ethnic Study of Atherosclerosis. We constructed a genetic risk score for each lipid outcome based on previously identified lipid-associated genetic markers, and examined the relationship between the genetic risk scores and corresponding outcomes. We discover this relationship was often moderated by race/ethnicity. Our findings provide insight into the generalizability and predictive utility of large sample size meta-analyses results when leveraging data from a single population. We hope these findings will encourage researchers to investigate genetic susceptibility in more diverse populations and explore the source of such discrepancies. Until then, we caution clinicians, genetic counselors, and genetic testing consumers when interpreting genetic data on complex traits.