Obsa Seid
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Obsa Seid has a total of 84 co-authors. Here are the top co-authors by number of shared publications:
- Airi Hosokai
Brotman Baty Institute for Precision Medicine, University of Washington, Seattle, WA, USA
2 shared publications - Alicia Xu
Brotman Baty Institute for Precision Medicine, University of Washington, Seattle, WA, USA
2 shared publications - Andrew Stergachis
Division of Medical Genetics, Department of Medicine, University of Washington, Seattle, WA, USA
2 shared publications - Cameron Wenman
Brotman Baty Institute for Precision Medicine, University of Washington, Seattle, WA, USA
2 shared publications - Douglas M. Fowler
Department of Genome Sciences, University of Washington, Seattle, WA, USA
2 shared publications - Ivan Woo
Brotman Baty Institute for Precision Medicine, University of Washington, Seattle, WA, USA
2 shared publications
Latest Publications
- Open Access
Saturation Genome Editing reveals the functional impact of RAD51D and XRCC2 variants
Germline pathogenic variants in RAD51D and XRCC2 , which encode RAD51 paralogs that form a heterodimer within the BCDX2 complex, confer increased cancer risk and homologous recombination deficiency. However, most RAD51D and XRCC2 variants in ClinVar are classified as variants of uncertain significance (VUS), limiting clinical utility. Here, we applied saturation genome editing (SGE) to measure the effects of 5,412 RAD51D and 3,743 XRCC2 variants on cellular fitness and additionally assessed 2,876 and 2,069 variants in these genes for effects on RNA expression. Fitness scores discriminated pathogenic from benign variants with near-perfect accuracy (AUC=0.994 for RAD51D ; AUC=1.000 for XRCC2 ). Integration of RNA expression data revealed RAD51D , but not XRCC2 , is exceptionally sensitive to splice-altering variation, with 24% of RAD51D loss-of-function missense variants acting through RNA-mediated mechanisms compared to only 5% in XRCC2 . These SGE datasets provide strong, splice-resolved functional evidence to support variant classification across both genes.
Open AccessSaturation Genome Editing reveals the functional impact of RAD51D and XRCC2 variants
Germline pathogenic variants in RAD51D and XRCC2 , which encode RAD51 paralogs that form a heterodimer within the BCDX2 complex, confer increased cancer risk and homologous recombination deficiency. However, most RAD51D and XRCC2 variants in ClinVar are classified as variants of uncertain significance (VUS), limiting clinical utility. Here, we applied saturation genome editing (SGE) to measure the effects of 5,412 RAD51D and 3,743 XRCC2 variants on cellular fitness and additionally assessed 2,876 and 2,069 variants in these genes for effects on RNA expression. Fitness scores discriminated pathogenic from benign variants with near-perfect accuracy (AUC=0.994 for RAD51D ; AUC=1.000 for XRCC2 ). Integration of RNA expression data revealed RAD51D , but not XRCC2 , is exceptionally sensitive to splice-altering variation, with 24% of RAD51D loss-of-function missense variants acting through RNA-mediated mechanisms compared to only 5% in XRCC2 . These SGE datasets provide strong, splice-resolved functional evidence to support variant classification across both genes.
- Open Access
A scalable approach to resolving variants of uncertain significance
Over 90% of missense variants across ∼4,000 disease-associated genes are variants of uncertain significance (VUS). Experimental variant effect measurements provide critical evidence about pathogenicity and inform disease biology, but most variants lack data and clinical translation has been limited. The Impact of Genomic Variation on Function Consortium generated experimental data for 62,215 variants across ten genes using multiplexed assays and 1,407 variants across 163 genes using arrayed assays, curated 193,139 additional community-generated variant effect measurements across 30 additional genes, and developed automated calibration methods for translating experimental data and variant effect predictions into clinical evidence. To reduce current VUS, we developed a scalable workflow using only experimental and predictive evidence, enabling reclassification of 75% of the 16,115 VUS in these genes as pathogenic or benign with 90,000 unobserved variants; 62% had enough evidence to be “preclassified” as pathogenic or benign. We validated our data, evidence and classifications using All of Us and created interactive resources to enable clinical use of the calibrated data. Thus, for 40 genes, representing 1% of the clinical genome, we resolve most existing VUS and future variants, illustrating how systematic use of scalable evidence can empower genomic medicine.
Open AccessA scalable approach to resolving variants of uncertain significance
Over 90% of missense variants across ∼4,000 disease-associated genes are variants of uncertain significance (VUS). Experimental variant effect measurements provide critical evidence about pathogenicity and inform disease biology, but most variants lack data and clinical translation has been limited. The Impact of Genomic Variation on Function Consortium generated experimental data for 62,215 variants across ten genes using multiplexed assays and 1,407 variants across 163 genes using arrayed assays, curated 193,139 additional community-generated variant effect measurements across 30 additional genes, and developed automated calibration methods for translating experimental data and variant effect predictions into clinical evidence. To reduce current VUS, we developed a scalable workflow using only experimental and predictive evidence, enabling reclassification of 75% of the 16,115 VUS in these genes as pathogenic or benign with 90,000 unobserved variants; 62% had enough evidence to be “preclassified” as pathogenic or benign. We validated our data, evidence and classifications using All of Us and created interactive resources to enable clinical use of the calibrated data. Thus, for 40 genes, representing 1% of the clinical genome, we resolve most existing VUS and future variants, illustrating how systematic use of scalable evidence can empower genomic medicine.