ArticleAmerican journal of human genetics2023
Population screening shows risk of inherited cancer and familial hypercholesterolemia in Oregon.
Article in American journal of human genetics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
7 citing papers in PubMed, 12 citations in OpenAlex.
- Feasibility and outcomes of the DNA Screen nationwide adult genomic screening pilot.Nature health · 2026Article
- Using large-scale population-based data to improve disease risk assessment of clinical variants.Nature genetics · 2025Review
- Actionable genetic variants in 4,198 Scottish participants from the Orkney and Shetland founder populations and implementation of return of results.American journal of human genetics · 2025Article
- Reproductive Carrier Screening: Identifying Families at Risk for Familial Hypercholesterolemia in the United States.Circulation. Genomic and precision medicine · 2024Review
- Using implementation science to evaluate a population-wide genomic screening program: Findings from the first 20,000 In Our DNA SC participants.American journal of human genetics · 2024Article
- Novel recruitment approaches and operational results for a statewide population Cohort for cancer research: The Healthy Oregon Project.Journal of clinical and translational science · 2024Article
- Improving representation of Hispanic adults in a population-based cancer genetics cohort: Qualitative findings from the Healthy Oregon Project.Journal of clinical and translational science · 2024Article
Corrections and comments
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Authors and funding
34 authors at 3 institutions in 1 country.
Funding
Abstract
The Healthy Oregon Project (HOP) is a statewide effort that aims to build a large research repository and influence the health of Oregonians through providing no-cost genetic screening to participants for a next-generation sequencing 32-gene panel comprising genes related to inherited cancers and familial hypercholesterolemia. This type of unbiased population screening can detect at-risk individuals who may otherwise be missed by conventional medical approaches. However, challenges exist for this type of high-throughput testing in an academic setting, including developing a low-cost high-efficiency test and scaling up the clinical laboratory for processing large numbers of samples. Modifications to our academic clinical laboratory including efficient test design, robotics, and a streamlined analysis approach increased our ability to test more than 1,000 samples per month for HOP using only one dedicated HOP laboratory technologist. Additionally, enrollment using a HIPAA-compliant smartphone app and sample collection using mouthwash increased efficiency and reduced cost. Here, we present our experience three years into HOP and discuss the lessons learned, including our successes, challenges, opportunities, and future directions, as well as the genetic screening results for the first 13,670 participants tested. Overall, we have identified 730 pathogenic/likely pathogenic variants in 710 participants in 24 of the 32 genes on the panel. The carrier rate for pathogenic/likely pathogenic variants in the inherited cancer genes on the panel for an unselected population was 5.0% and for familial hypercholesterolemia was 0.3%. Our laboratory experience described here may provide a useful model for population screening projects in other states.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.