Approximately 300 million people worldwide suffer from rare diseases. 10 With the majority of patients having an onset in childhood and over half of cases associated with a reduced lifespan, rare diseases are one of the hottest topics in current genomic research. 2, 5 Around half of patients remain undiagnosed, while more than three-fourths of cases are estimated to have a genetic origin. 8, 3
Short reads, as the state of the art in genetic diagnostics, are unsurpassed for massive high-throughput sequencing with the highest accuracy. However, in a fair number of cases, this method reaches its limits when dealing with large structural variations, mutations in repetitive regions, and imprinting disorders, which are reported to underlie several rare diseases.
Published studies examine the proportion of additional diagnoses made using long-read sequencing in patient cohorts that had not received diagnoses via short-read sequencing alone. These studies showed that the use of PacBio sequencing platforms remarkably increased diagnostic rates, affirming its role as a complementary analysis to short-read sequencing. 9, 6, 1
But why is this? Long-Read Sequencing technologies can detect the above-mentioned variations, through more accurate, unambiguous mapping, even across repetitive or structurally complex regions.
Many genes associated with rare diseases are located in repetitive regions. Correct mapping in these areas can be challenging for short-read sequencing, leading to many variants not being detected. 4 Since the sequenced reads can no longer be clearly assigned to a genomic position, so-called short read dark regions arise in the genome. It was shown that long-read sequencing enables the analysis of these previously difficult-to-access regions, as challenging areas are partially or even completely spanned. 12
Furthermore, this capability allows the elucidation of large structural variations, a common underlying process in many rare disease cases. A specific type of structural variation, known as copy number variation (CNVs), has been identified as accounting for 5-35% of pathogenic variants in Mendelian disease genes. 11
Finally, epigenetic modifications (epimutations) can contribute to rare diseases by altering gene activity. The most commonly detected methylation is at the 5th carbon of cytosine (5-methylcytosine, 5mC) at CpG sites. 7 The PacBio sequencing platform automatically detects this modification during the sequencing run, thereby providing important information, for example, on imprinting disorders. These arise from errors in parent-specific epigenetic gene regulation, for example, due to uniparental disomies or epimutations.
To further advance research and provide more patients with diagnoses, we have integrated various tools into our updated bioinformatics pipeline. This enables the analysis of new diagnostically relevant variants.
If you are interested in taking your rare disease research to the next level with our HiFi Whole Genome Sequencing and bioinformatic solutions tailored to meet your research goals, don’t hesitate to get in touch with us.
Resources
1 Fabian-Morales, G. et al. Resolving the diagnostic odyssey in inherited retinal dystrophies through long-read genome sequencing. medRxiv; 10.1101/2024.08.28.24312668 (2024).
2 Ferreira, C. R. The burden of rare diseases. American journal of medical genetics. Part A 179 (6), 885–892; 10.1002/ajmg.a.61124 (2019).
3 Graessner, H.; Zurek, B.; Hoischen, A.; Beltran, S. Solving the unsolved rare diseases in Europe. European journal of human genetics: EJHG 29 (9), 1319–1320; 10.1038/s41431-021-00924-8 (2021).
4 Mandelker, D. et al. Navigating highly homologous genes in a molecular diagnostic setting: a resource for clinical next-generation sequencing. Genetics in medicine: official journal of the American College of Medical Genetics 18 (12), 1282–1289; 10.1038/gim.2016.58 (2016).
5 Nguengang Wakap, S. et al. Estimating cumulative point prevalence of rare diseases: analysis of the Orphanet database. European journal of human genetics: EJHG 28 (2), 165–173; 10.1038/s41431-019-0508-0 (2020).
6 Redfield, S. E. et al. Long-Read Sequencing Increases Diagnostic Yield for Pediatric Sensorineural Hearing Loss. medRxiv; 10.1101/2024.09.30.24314377 (2024).
7 Schübeler, D. Function and information content of DNA methylation. Nature 517 (7534), 321–326; 10.1038/nature14192 (2015).
8 Sernadela, P. et al. Linked Registries: Connecting Rare Diseases Patient Registries through a Semantic Web Layer. BioMed research international 2017, 8327980; 10.1155/2017/8327980 (2017).
9 Steyaert, W. et al. Unravelling undiagnosed rare disease cases by HiFi long-read genome sequencing. medRxiv: the preprint server for health sciences; 10.1101/2024.05.03.24305331 (2024).
10 The Lancet Global Health The landscape for rare diseases in 2024. The Lancet. Global health 12 (3), e341; 10.1016/S2214-109X(24)00056-1 (2024).
11 Truty, R. et al. Prevalence and properties of intragenic copy-number variation in Mendelian disease genes. Genetics in medicine: official journal of the American College of Medical Genetics 21 (1), 114–123; 10.1038/s41436-018-0033-5 (2019).
12 Wenger, A. M. et al. Accurate circular consensus long-read sequencing improves variant detection and assembly of a human genome. Nature biotechnology 37 (10), 1155–1162; 10.1038/s41587-019-0217-9 (2019).