Integrated multi-omics for rapid rare disease diagnosis on a national scale

Abstract

Critically ill infants and children with rare diseases need equitable access to rapid and accurate diagnosis to direct clinical management. Over 2 years, the Acute Care Genomics program provided whole-genome sequencing to 290 families whose critically ill infants and children were admitted to hospitals throughout Australia with suspected genetic conditions. The average time to result was 2.9 d and diagnostic yield was 47%. We performed additional bioinformatic analyses and transcriptome sequencing in all patients who remained undiagnosed. Long-read sequencing and functional assays, ranging from clinically accredited enzyme analysis to bespoke quantitative proteomics, were deployed in selected cases. This resulted in an additional 19 diagnoses and an overall diagnostic yield of 54%. Diagnostic variants ranged from structural chromosomal abnormalities through to an intronic retrotransposon, disrupting splicing. Critical care management changed in 120 diagnosed patients (77%). This included major impacts, such as informing precision treatments, surgical and transplant decisions and palliation, in 94 patients (60%). Our results provide preliminary evidence of the clinical utility of integrating multi-omic approaches into mainstream diagnostic practice to fully realize the potential of rare disease genomic testing in a timely manner.

Fig. 1. Recruitment workflow for the Acute Care Genomics program.

Critically ill infants and children with suspected genetic conditions were proposed to a national panel of experts, with those approved undergoing ultra-rapid WGS. Additional bioinformatic analyses and transcriptome sequencing were performed in all undiagnosed patients. Long-read sequencing and functional assays were deployed in selected cases. ICU, intensive care unit.

Fig. 2. Patient recruitment and key characteristics.

a, Recruitment sites. NT, Northern Territory; QLD, Queensland; WA, Western Australia; ACT, Australian Capital Territory; NSW, New South Wales; SA, South Australia; VIC, Victoria; TAS, Tasmania. b, Study workflow, including patient selection using guidelines and virtual expert panel; electronic resources to support test ordering and consent; sample shipping; diagnostic reporting; and extended analysis and multi-omic approaches in unsolved cases. c, Age of study participants. d, Ancestry/ethnicity of participants. e, Twenty most common HPO terms, coded by major groups.

Fig. 3. Summary of diagnostic outcomes.

a, Time to clinical report for ultra-rapid WGS cohort (n = 290), compared to previous ultra-rapid ES cohort (n = 108). X represents the mean; central line represents the median; top and bottom edges of the boxes are the first and third quartiles; the whiskers show the minima to maxima no greater than 1.5× the interquartile range with remaining outliers plotted individually. TAT, turnaround time. b, Variant types detected by WGS. c, Incremental gain in diagnostic yield from extended analysis and multi-omic approaches. CNV, copy number variant; SV, structural variant; UPD, uniparental disomy. d, Sunburst representing the spectrum of diagnoses. Arranged by number of patients, clockwise, the inner ring represents the principal clinical presentation and the second ring represents diagnostic yield in each group. Genes responsible for diagnoses in multiple individuals represented in the adjacent table, color-coded by principal clinical presentation. Full names of each disorder and Online Mendelian Inheritance in Man numbers are included in Supplementary Tables 1–3.

Fig. 4. Identification and confirmation of intronic insertion variant, likely derived from an SVA retrotransposable element, in the last intron of MECP2.

a, Integrated Genomics Viewer (IGV) proband RNA (top) and DNA (bottom) short-read sequencing data indicating the presence of a DNA insertion resulting in the inclusion of a pseudo-exon in the last intron of MECP2. b, Nanopore sequencing data demonstrating the insertion. c, PCR gel electrophoresis of relevant MECP2 region in the proband (A0131084), parents (A0131084-M and A0131084-P) and a control sample (NA12878), consistent with an insertion of approximately 2.6 kb. This clinically accredited assay was performed once. d, Schematic of the observed splicing outcomes of MECP2 in the proband with the presence of a transposon-derived pseudo-exon, residual canonical splicing, skipping of the penultimate exon and intronic read-through. WT, wild-type.

Fig. 5. Decreased NUP214 steady-state levels and decreased NUP214-containing nuclear pore complex density in fibroblasts from A1131048.

a, Densitometry shows significantly reduced NUP214 levels in fibroblasts compared to controls. n = 6 biological samples per cell line. Data represent mean ± s.d. One-way analysis of variance (ANOVA) with Holm–Sidak’s multiple comparisons test. b, Representative western blot of NUP214 in fibroblasts from A1131048 and two controls (C1 and C2). c, Quantification of NUP214-containing nuclear pore complex pore density (pores per nucleus) in fibroblasts from A1131048 compared to controls shows a significant decrease in fibroblasts from A1131048. n = 3 biological samples per cell line, from three independent experiments, represented is pooled data from from n = 35 for C1, n = 31 for C2 and n = 35 for A1131048. Data represent mean ± s.d. One-way ANOVA with Holm–Sidak’s multiple comparisons test. d, Compressed Z-stack representative images of NUP214 immunostaining (green spots) and nucleus (4,6-diamidino-2-phenylindole (DAPI); blue) in fibroblasts from A1131048 and controls. Scale bars, 5 μm. Images are representative from three experiments. e, Volcano plot showing protein abundances in the fibroblasts from A1131048 relative to healthy controls (n = 5). Nuclear pore complex (NPC) components are indicated in blue. The horizonal line represents P = 0.05 and the vertical lines represent fold changes of ±1.5. Data were derived from a two-sided Student’s t-test. No adjustments were made for multiple comparisons. f, Topographical heat map showing fold changes of NPC proteins identified by proteomics mapped onto the structure of the NPC cytosolic face (Protein Data Bank, 7TBL). Yellow indicates NUP214 subunit. Other subunits, including NUP88, which is coiled around NUP214, are colored according to their fold change relative to controls, as indicated in the inset; gray indicates no data. Source data