Application of full-genome analysis to diagnose rare monogenic disorders
Joseph T.C. Shieh, Monica Penon‐Portmann, Karen H. Y. Wong, Michal Levy‐Sakin, Michelle Verghese, Anne Michele Slavotinek et al. · 2021 · 56 citationsRead the paper
Current genetic testenhancer and narrows the diagnostic intervals for rare diseases provide a diagnosis in only a modest proportion of cases. The Full-Genome Analysis method, FGA, combines long-range assembly and whole-genome sequencing to detect small variants, structural variants with breakpoint resolution, and phasing. We built a variant prioritization pipeline and tested FGA's utility for diagnosis of rare diseases in a clinical setting. FGA identified structural variants and small variants with an overall diagnostic yield of 40% (20 of 50 cases) and 35% in exome-negative cases (8 of 23 cases), 4 of these were structural variants. FGA detected and mapped structural variants that are missed by short reads, including non-coding duplication, and phased variants across long distances of more than 180 kb. With the prioritization algorithm, longer DNA technologies could replace multiple tests for monogenic disorders and expand the range of variants detected. Our study suggests that genomes produced from technologies like FGA can improve variant detection and provide higher resolution genome maps for future application.
What the full text shows
Working prototype- Overall diagnostic yield for undiagnosed cases — 40% (20 of 50 cases) (Clinical setting, cases with prior negative testing)
- Diagnostic yield for cases previously negative for whole-exome sequencing — 35% (8 of 23 cases) (Exome-negative cases)
- Proportion of exome-negative cases solved by identifying structural variants (SVs) — 50% (4 of 8 cases) (Exome-negative cases)
- Diagnostic yield for cases without prior exome testing — 44% (12 of 27 cases) (No prior exome)
- Reduction in candidate variants for translocations compared to short-read WGS — at least 8-fold fewer candidates (Detection of genomic rearrangements)
- Phasing capability for recessive disorders — variants phased across >180 kb (Single proband test)
- Setup
- 50 undiagnosed clinical cases; 23 had negative prior trio whole-exome sequencing and 42 had negative prior microarray.
- How
- The Full-Genome Analysis (FGA) method combines 10x Genomics linked-read sequencing (~60X coverage) and Bionano optical mapping to create de novo genome assemblies. An in-house automated pipeline uses NLP to parse electronic health records into HPO terms to prioritize and rank variants.
- Limitations
- SV/CNV databases are sparse and inconsistent compared to SNV databases · Most SVs are not recurrent, making it unclear if a de novo SV is diagnostic without a known critical region or distinctive phenotype · Deep intronic variation and short tandem repeats were not systematically annotated
Summarized by Gemma 4 from the open-access full text · 9 Oct 2026. Read it