HiFi long-read genomes for difficult-to-detect clinically relevant variants
基因组
计算生物学
生物
遗传学
进化生物学
计算机科学
基因
作者
Wolfram Hoeps,Marjan M. Weiss,Ronny Derks,Jordi Corominas Galbany,Amber den Ouden,Simone van den Heuvel,Raoul Timmermans,Jos G.A. Smits,Tom Mokveld,Egor Dolzhenko,Xiao Chen,Arthur van den Wijngaard,Michael A. Eberle,Helger G. Yntema,Alexander Hoischen,Christian Gilissen,Lisenka E.L.M. Vissers
出处
期刊:Cold Spring Harbor Laboratory - medRxiv日期:2024-09-19被引量:1
标识
DOI:10.1101/2024.09.17.24313798
摘要
Clinical short-read exome and genome sequencing approaches have positively impacted diagnostic testing for rare diseases. Yet, technical limitations associated with short reads challenge their use for detection of disease-associated variation in complex regions of the genome. Long-read sequencing (LRS) technologies may overcome these challenges, potentially qualifying as a first-tier test for all rare diseases. To test this hypothesis, we performed LRS (30x HiFi genomes) for 100 samples with 145 known clinically relevant germline variants that are challenging to detect using short-read sequencing and necessitate a broad range of complementary test modalities in diagnostic laboratories. We show that relevant variant callers readily re-identify the majority of variants (120/145, 83%), including ~90% of structural variants, SNVs/InDels in homologous sequences and expansions of short tandem repeats. Another 10% (n=14) was visually apparent in the data but not automatically detected. Our analyses also identified systematic challenges for the remaining 7% (n=11) of variants such as the detection of AG-rich repeat expansions. Titration analysis showed that 89% of all automatically called variants could also be identified using 15-fold coverage. Thus, long-read genomes identified 93% of pathogenic variants that are most challenging to detect using short-read technologies. Even with reduced coverage, the vast majority of variants remained detectable, possibly enhancing cost-effective diagnostic implementation. Most importantly, we show the potential to use a single technology to accurately identify all types of clinically relevant variants.