Population screening shows risk of inherited cancer and familial hypercholesterolemia in Oregon.

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.

Artificial intelligence (AI)-assisted exome reanalysis greatly aids in the identification of new positive cases and reduces analysis time in a clinical diagnostic laboratory.

PURPOSE: Artificial intelligence (AI) and variant prioritization tools for genomic variant analysis are being rapidly developed for use in clinical diagnostic testing. However, their clinical utility and reliability are currently limited. Therefore, we performed a validation of a commercial AI tool (Moon) and a comprehensive reanalysis of previously collected clinical exome sequencing cases using an open-source variant prioritization tool (Exomiser) and the now-validated AI tool to test their feasibility in clinical diagnostics. METHODS: A validation study of Moon was performed with 29 positive cases determined by previous manual analysis. After validation, reanalysis was performed on 80 previously manually analyzed nondiagnostic exome cases using Moon. Finally, a comparison between Moon and Exomiser was completed regarding their ability to identify previously completed positive cases and to identify new positive cases. RESULTS: Moon correctly selected the causal variant(s) in 97% of manually analyzed positive cases and identified 7 new positive cases. Exomiser correctly identified the causal gene in 85% of positive cases and agreed with Moon by ranking the new gene in its top 10 list 43% of the time. CONCLUSION: The use of AI in diagnostic laboratories greatly enhances exome sequencing analysis by reducing analysis time and increasing the diagnostic rate.

Gene expression changes between patent and fused cranial sutures in a nonsyndromic craniosynostosis population.

OBJECTIVE: Craniosynostosis is a premature fusion of 1 or more cranial sutures. It may occur with additional morphological abnormalities (syndromic) or in isolation. Studies suggest that dysregulation of normal cell proliferation, differentiation, and migration has a role in isolated or nonsyndromic craniosynostosis but the molecular mechanisms remain unknown. The aim of this research is to identify genes differentially expressed in prematurely fused human suture compared to patent suture in nonsyndromic craniosynostosis. METHODS: Bone fragments from synostosed and patent sutures of 7 infants with nonsyndromic craniosynostosis were collected during surgical release of fused sutures. RNA was isolated from the fragments (7 patent and 7 fused) and global gene expression profiled using the Illumina WGE-DASL assay and HumanRef 8.0 Beadchip. RESULTS: Comparison of mRNA expression in fused and patent suture identified 68 genes significantly differentially expressed and having fold changes ≤ -2.0 and ≥ 2.0 with a false discovery rate adjusted P value at .10 and 136 with adjusted P value of 0.15. SFRP2 (secreted frizzled-related protein 2) demonstrated the largest decrease in fused sutures. Analysis including only sagittal fused sutures revealed a set of 35 overlapping genes that may be involved in suture patency over all suture types. SPHKAP (sphingosine kinase type 1-interacting protein), a modulator of TGFß signaling, was significant in the sagittal subset. CONCLUSION: Differentially expressed genes were identified in fused suture relative to patent in a nonsyndromic craniosynostosis population. SFRP2 is likely important in suture patency. Genes having significant roles in osteoblastogenesis as negative regulators of canonical Wnt pathway were significantly downregulated.