The absence of CFHR3 and CFHR1 genes from the T2T-CHM13 assembly can limit the molecular diagnosis of complement-related diseases

Hamza, Abderaouf; El-Sissy, Carine; Yousfi, Nadhir; Martins, Paula Vieira; Rafat, Cédric; Masliah‐Planchon, Julien; Frémeaux‐Bacchi, Véronique; Mesnard, Laurent

doi:10.1038/s41431-023-01350-8

Cited by 2 publications

(2 citation statements)

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“…For example, hg19 and hg38-expressed CFHR-Factor H complex genes CFHR1 and CFHR3 are linked with atypical hemolytic uremic syndrome 36, 37 and fall within a region harboring population-specific copy number variations. The absence of these genes in CHM13 could be due to the reliance on a single cell line, especially in contrast to the genetic diversity from multiple cell lines underlying hg38 38 . We detected quantification of CFHR1 in fibroblast and muscle, and CFHR3 in iPS and iPS neural progenitor cells for hg19 and hg38.…”

Section: Resultsmentioning

confidence: 99%

“…We detected quantification of CFHR1 in fibroblast and muscle, and CFHR3 in iPS and iPS neural progenitor cells for hg19 and hg38. One study reports that detection of the disease-causing structural variants was not possible when aligning to CHM13, even with long-read sequencing 38 , suggesting CFHR-Factor H complex disorders should not be evaluated using CHM13v2. The absence of these genes in CHM13v2 likely influences mapping in CHM13 of other CFHR-Factor H complex genes – CFHR4 (also linked to atypical hemolytic uremic syndrome) is detected as quantified only in CHM13 in iPSC NPC with a median TPM of 4.4.…”

Section: Resultsmentioning

confidence: 99%

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Impact of genome build on RNA-seq interpretation and diagnostics

Ungar,

Goddard,

Jensen

et al. 2024

Preprint

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Transcriptomics is a powerful tool for unraveling the molecular effects of genetic variants and disease diagnosis. Prior studies have demonstrated that choice of genome build impacts variant interpretation and diagnostic yield for genomic analyses. To identify the extent genome build also impacts transcriptomics analyses, we studied the effect of the hg19, hg38, and CHM13 genome builds on expression quantification and outlier detection in 386 rare disease and familial control samples from both the Undiagnosed Diseases Network (UDN) and Genomics Research to Elucidate the Genetics of Rare Disease (GREGoR) Consortium. We identified 2,800 genes with build-dependent quantification across six routinely-collected biospecimens, including 1,391 protein-coding genes and 341 known rare disease genes. We further observed multiple genes that only have detectable expression in a subset of genome builds. Finally, we characterized how genome build impacts the detection of outlier transcriptomic events. Combined, we provide a database of genes impacted by build choice, and recommend that transcriptomics-guided analyses and diagnoses are cross-referenced with these data for robustness.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%