Publications by authors named "Nicole Washington"

Purpose: This study aimed to evaluate the performance of different genetic screening approaches to identify women at high risk of breast cancer in the general population.

Methods: We retrospectively studied 25,591 women with available electronic health records and genetic data, participants in the Healthy Nevada Project.

Results: Family history of breast cancer was ascertained on or after the record of breast cancer for 78% of women with both, indicating that this risk assessment method is not being properly utilized for early screening.

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Background: Natural HbA1c levels in GCK Maturity-onset diabetes of the young (GCK-MODY) patients often sit above the diagnostic threshold for type 2 diabetes (T2D). Treatments to lower HbA1c levels show reduced effectiveness in these individuals, yet in case studies to date, GCK-MODY patients often evade secondary T2D complications. Given these deviations, genetic screening of GCK may be clinically useful, but population studies are needed to more broadly understand T2D-related complications in GCK variant carriers.

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Within a multistate viral genomic surveillance program, we evaluated whether proportions of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections attributed to the JN.1 variant and to XBB-lineage variants (including HV.1 and EG.

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Systematic determination of novel variant pathogenicity remains a major challenge, even when there is an established association between a gene and phenotype. Here we present Power Window (PW), a sliding window technique that identifies the impactful regions of a gene using population-scale clinico-genomic datasets. By sizing analysis windows on the number of variant carriers, rather than the number of variants or nucleotides, statistical power is held constant, enabling the localization of clinical phenotypes and removal of unassociated gene regions.

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Within a multistate clinical cohort, SARS-CoV-2 antiviral prescribing patterns were evaluated from April 2022-June 2023 among nonhospitalized patients with SARS-CoV-2 with risk factors for severe COVID-19. Among 3247 adults, only 31.9% were prescribed an antiviral agent (87.

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The maturation of genomic surveillance in the past decade has enabled tracking of the emergence and spread of epidemics at an unprecedented level. During the COVID-19 pandemic, for example, genomic data revealed that local epidemics varied considerably in the frequency of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) lineage importation and persistence, likely due to a combination of COVID-19 restrictions and changing connectivity. Here, we show that local COVID-19 epidemics are driven by regional transmission, including across international boundaries, but can become increasingly connected to distant locations following the relaxation of public health interventions.

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This study examined the prevalence of anorexia nervosa (AN) and bulimia nervosa (BN) diagnoses among college students from different racial/ethnic backgrounds. Utilizing archival data from the American College Health Association - National College Health Assessment II-C (ACHA-NCHA II-C), information from 426,425 college students collected between 2015 and 2019 was examined. Binary logistic regression analyses were conducted to determine the prevalence of AN and BN diagnoses among various racial and ethnic groups.

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Objectives: Autonomy is necessary for resident professional development and well-being. A recent focus on patient safety has increased supervision and decreased trainee autonomy. Few validated interventions exist to improve resident autonomy.

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Purpose: Weight loss behaviors are prevalent among college students and are associated with adverse physical and psychological outcomes, such as an elevated risk of developing an eating disorder. While cross-ethnic differences have been reported, no consistent pattern has emerged. The purpose of this study was to examine racial and ethnic differences in weight loss behaviors among female and male college students.

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Purpose: TTN truncating variants (TTNtvs) represent the largest known genetic cause of dilated cardiomyopathies (DCMs), however their penetrance for DCM in general populations is low. More broadly, patients with cardiomyopathies (CMs) often exhibit other cardiac conditions, such as atrial fibrillation (Afib), which has also been linked to TTNtvs. This retrospective analysis aims to characterize the relationship between different cardiac conditions in those with TTNtvs and identify individuals with the highest risk of DCM.

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Background: Between November 2021 and February 2022, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) Delta and Omicron variants co-circulated in the United States, allowing for co-infections and possible recombination events.

Methods: We sequenced 29,719 positive samples during this period and analyzed the presence and fraction of reads supporting mutations specific to either the Delta or Omicron variant.

Findings: We identified 18 co-infections, one of which displayed evidence of a low Delta-Omicron recombinant viral population.

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Article Synopsis
  • * This study analyzed rare genetic variants by combining data from 21 cohorts worldwide, involving over 5,000 severe cases and 571,000 controls.
  • * A significant finding showed that a rare harmful variant in the TLR7 gene greatly increases the risk of severe COVID-19, indicating that rare variants could offer valuable insights for understanding and treating the disease.
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Article Synopsis
  • Effective detection of SARS-CoV-2 variants through wastewater analysis can complement existing clinical testing methods, especially in resource-limited areas where traditional testing may be biased.* -
  • The study implemented improved virus concentration techniques and software to enhance the sequencing of multiple virus strains from wastewater, resulting in high-resolution data over 295 days at a university and its surrounding county.* -
  • Wastewater surveillance identified emerging variants up to 14 days earlier than clinical methods and revealed instances of virus spread that clinical testing missed, highlighting its potential for public health monitoring.*
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We report on the sequencing of 74,348 SARS-CoV-2 positive samples collected across the United States and show that the Delta variant, first detected in the United States in March 2021, made up the majority of SARS-CoV-2 infections by July 1, 2021 and accounted for >99.9% of the infections by September 2021. Not only did Delta displace variant Alpha, which was the dominant variant at the time, it also displaced the Gamma, Iota, and Mu variants.

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Purpose: Genomic test results, regardless of laboratory variant classification, require clinical practitioners to judge the applicability of a variant for medical decisions. Teaching and standardizing clinical interpretation of genomic variation calls for a methodology or tool.

Methods: To generate such a tool, we distilled the Clinical Genome Resource framework of causality and the American College of Medical Genetics/Association of Molecular Pathology and Quest Diagnostic Laboratory scoring of variant deleteriousness into the Clinical Variant Analysis Tool (CVAT).

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As SARS-CoV-2 continues to spread and evolve, detecting emerging variants early is critical for public health interventions. Inferring lineage prevalence by clinical testing is infeasible at scale, especially in areas with limited resources, participation, or testing/sequencing capacity, which can also introduce biases. SARS-CoV-2 RNA concentration in wastewater successfully tracks regional infection dynamics and provides less biased abundance estimates than clinical testing.

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COVID-19 vaccines are safe and highly effective, but some individuals experience unpleasant reactions to vaccination. As the majority of adults in the United States have received a COVID-19 vaccine this year, there is an unprecedented opportunity to study the genetics of reactions to vaccination via surveys of individuals who are already part of genetic research studies. Here, we have queried 17,440 participants in the Helix DNA Discovery Project and Healthy Nevada Project about their reactions to COVID-19 vaccination.

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Purpose: To identify conditions that are candidates for population genetic screening based on population prevalence, penetrance of rare variants, and actionability.

Methods: We analyzed exome and medical record data from >220,000 participants across two large population health cohorts with different demographics. We performed a gene-based collapsing analysis of rare variants to identify genes significantly associated with disease status.

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Epidemiological and genetic studies on COVID-19 are currently hindered by inconsistent and limited testing policies to confirm SARS-CoV-2 infection. Recently, it was shown that it is possible to predict COVID-19 cases using cross-sectional self-reported disease-related symptoms. Here, we demonstrate that this COVID-19 prediction model has reasonable and consistent performance across multiple independent cohorts and that our attempt to improve upon this model did not result in improved predictions.

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Hospitals are increasingly motivated to improve the patient and family experience and increase patient satisfaction scores. The manner by which a provider greets patients and their families sets the tone for the hospital stay. This study aimed to improve residents' greetings of caregivers in the inpatient pediatric setting to improve family-centered communication.

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Genomic-guided pharmaceutical prescribing is increasingly recognized as an important clinical application of genetics. Accurate genotyping of pharmacogenomic (PGx) genes can be difficult, owing to their complex genetic architecture involving combinations of single-nucleotide polymorphisms and structural variation. Here, we introduce the Helix PGx database, an open-source star allele, genotype, and resulting metabolic phenotype frequency database for CYP2C9, CYP2C19, CYP2D6, and CYP4F2, based on short-read sequencing of >86,000 unrelated individuals enrolled in the Helix DNA Discovery Project.

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Clinical conditions correlated with elevated triglyceride levels are well-known: coronary heart disease, hypertension, and diabetes. Underlying genetic and phenotypic mechanisms are not fully understood, partially due to lack of coordinated genotypic-phenotypic data. Here we use a subset of the Healthy Nevada Project, a population of 9,183 sequenced participants with longitudinal electronic health records to examine consequences of altered triglyceride levels.

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