Mobile element insertions in rare diseases: a comparative benchmark and reanalysis of 60,000 exome samples.

Mobile element insertions (MEIs) are a known cause of genetic disease but have been underexplored due to technical limitations of genetic testing methods. Various bioinformatic tools have been developed to identify MEIs in Next Generation Sequencing data. However, most tools have been developed specifically for genome sequencing (GS) data rather than exome sequencing (ES) data, which remains more widely used for routine diagnostic testing. In this study, we benchmarked six MEI detection tools (ERVcaller, MELT, Mobster, SCRAMble, TEMP2 and xTea) on ES data and on GS data from publicly available genomic samples (HG002, NA12878). For all the tools we evaluated sensitivity and precision of different filtering strategies. Results show that there were substantial differences in tool performance between ES and GS data. MELT performed best with ES data and its combination with SCRAMble increased substantially the detection rate of MEIs. By applying both tools to 10,890 ES samples from Solve-RD and 52,624 samples from Radboudumc we were able to diagnose 10 patients who had remained undiagnosed by conventional ES analysis until now. Our study shows that MELT and SCRAMble can be used reliably to identify clinically relevant MEIs in ES data. This may lead to an additional diagnosis for 1 in 3000 to 4000 patients in routine clinical ES.

Genome sequencing as a generic diagnostic strategy for rare disease.

BACKGROUND: To diagnose the full spectrum of hereditary and congenital diseases, genetic laboratories use many different workflows, ranging from karyotyping to exome sequencing. A single generic high-throughput workflow would greatly increase efficiency. We assessed whether genome sequencing (GS) can replace these existing workflows aimed at germline genetic diagnosis for rare disease. METHODS: We performed short-read GS (NovaSeq™6000; 150 bp paired-end reads, 37 × mean coverage) on 1000 cases with 1271 known clinically relevant variants, identified across different workflows, representative of our tertiary diagnostic centers. Variants were categorized into small variants (single nucleotide variants and indels < 50 bp), large variants (copy number variants and short tandem repeats) and other variants (structural variants and aneuploidies). Variant calling format files were queried per variant, from which workflow-specific true positive rates (TPRs) for detection were determined. A TPR of ≥ 98% was considered the threshold for transition to GS. A GS-first scenario was generated for our laboratory, using diagnostic efficacy and predicted false negative as primary outcome measures. As input, we modeled the diagnostic path for all 24,570 individuals referred in 2022, combining the clinical referral, the transition of the underlying workflow(s) to GS, and the variant type(s) to be detected. RESULTS: Overall, 95% (1206/1271) of variants were detected. Detection rates differed per variant category: small variants in 96% (826/860), large variants in 93% (341/366), and other variants in 87% (39/45). TPRs varied between workflows (79-100%), with 7/10 being replaceable by GS. Models for our laboratory indicate that a GS-first strategy would be feasible for 84.9% of clinical referrals (750/883), translating to 71% of all individuals (17,444/24,570) receiving GS as their primary test. An estimated false negative rate of 0.3% could be expected. CONCLUSIONS: GS can capture clinically relevant germline variants in a 'GS-first strategy' for the majority of clinical indications in a genetics diagnostic lab.

Unravelling undiagnosed rare disease cases by HiFi long-read genome sequencing.

Solve-RD is a pan-European rare disease (RD) research program that aims to identify disease-causing genetic variants in previously undiagnosed RD families. We utilised 10-fold coverage HiFi long-read sequencing (LRS) for detecting causative structural variants (SVs), single nucleotide variants (SNVs), insertion-deletions (InDels), and short tandem repeat (STR) expansions in extensively studied RD families without clear molecular diagnoses. Our cohort includes 293 individuals from 114 genetically undiagnosed RD families selected by European Rare Disease Network (ERN) experts. Of these, 21 families were affected by so-called 'unsolvable' syndromes for which genetic causes remain unknown, and 93 families with at least one individual affected by a rare neurological, neuromuscular, or epilepsy disorder without genetic diagnosis despite extensive prior testing. Clinical interpretation and orthogonal validation of variants in known disease genes yielded thirteen novel genetic diagnoses due to de novo and rare inherited SNVs, InDels, SVs, and STR expansions. In an additional four families, we identified a candidate disease-causing SV affecting several genes including an MCF2 / FGF13 fusion and PSMA3 deletion. However, no common genetic cause was identified in any of the 'unsolvable' syndromes. Taken together, we found (likely) disease-causing genetic variants in 13.0% of previously unsolved families and additional candidate disease-causing SVs in another 4.3% of these families. In conclusion, our results demonstrate the added value of HiFi long-read genome sequencing in undiagnosed rare diseases.

The performance of genome sequencing as a first-tier test for neurodevelopmental disorders.

Genome sequencing (GS) can identify novel diagnoses for patients who remain undiagnosed after routine diagnostic procedures. We tested whether GS is a better first-tier genetic diagnostic test than current standard of care (SOC) by assessing the technical and clinical validity of GS for patients with neurodevelopmental disorders (NDD). We performed both GS and exome sequencing in 150 consecutive NDD patient-parent trios. The primary outcome was diagnostic yield, calculated from disease-causing variants affecting exonic sequence of known NDD genes. GS (30%, n = 45) and SOC (28.7%, n = 43) had similar diagnostic yield. All 43 conclusive diagnoses obtained with SOC testing were also identified by GS. SOC, however, required integration of multiple test results to obtain these diagnoses. GS yielded two more conclusive diagnoses, and four more possible diagnoses than ES-based SOC (35 vs. 31). Interestingly, these six variants detected only by GS were copy number variants (CNVs). Our data demonstrate the technical and clinical validity of GS to serve as routine first-tier genetic test for patients with NDD. Although the additional diagnostic yield from GS is limited, GS comprehensively identified all variants in a single experiment, suggesting that GS constitutes a more efficient genetic diagnostic workflow.

The relationship between disease activity and radiologic progression in patients with rheumatoid arthritis: a longitudinal analysis.

OBJECTIVE: Radiologic progression in rheumatoid arthritis (RA) is considered the consequence of persistent inflammatory activity. To determine whether a change in disease activity is related to a change in radiologic progression in individual patients, we investigated the longitudinal relationship between inflammatory disease activity and subsequent radiologic progression. METHODS: The databases of the University Medical Center Nijmegen (UMCN) cohort and the Maastricht Combination Therapy in RA (COBRA) followup study cohort were analyzed. The UMCN cohort included 185 patients with early RA who were followed up for up to 9 years. Patients were assessed every 3 months for disease activity and every 3 years for radiologic damage. The COBRA cohort included 152 patients with early RA who were followed up for up to 6 years. Patients were assessed at least every year for disease activity and every 12 months for radiologic damage. Disease activity was assessed with the Disease Activity Score (DAS) (original DAS in the UMCN cohort, DAS28 in the COBRA cohort). Radiologic damage was measured by the Sharp/van der Heijde score in both cohorts. Data were analyzed with longitudinal regression analysis (generalized estimating equations [GEE]), using autoregression for longitudinal associations and radiologic damage as the dependent variable. Time, time(2) baseline predictors for radiologic progression and their interactions with time, as well as DAS/DAS28 (actual values or interval means and interval SDs of the means) were subsequently modeled as explanatory variables. RESULTS: Data analyzed by GEE showed a decrease in radiologic progression over time (regression coefficient for time(2) -1.0 [95% confidence interval -1.4, -0.6] in the UMCN cohort and -0.4 [95% confidence interval -0.8, 0.0] in the COBRA cohort). After adjustment for time effects and baseline predictors of radiologic progression and their interactions with time, a positive longitudinal relationship was indicated by autoregressive GEE between the mean interval DAS and radiologic progression in the UMCN cohort (regression coefficient 5.4 [95% confidence interval 2.1, 8.6]), and between the DAS28 and radiologic progression in the COBRA cohort (regression coefficient 1.4 [95% confidence interval 0.8, 2.0]). In the UMCN cohort, the SD of the mean interval DAS was independently longitudinally related to the radiologic progression over the same periods (regression coefficient 20.2 [95% confidence interval 7.2, 33.3]). In both cohorts, the longitudinal relationships between (fluctuations in) disease activity and radiologic progression were found selectively in rheumatoid factor (RF)-positive patients. CONCLUSION: Radiologic progression is not linear in individual patients. Fluctuations in disease activity are directly related to changes in radiologic progression, which supports the hypothesis that disease activity causes radiologic damage. This relationship might only exist in RF-positive patients.