International Journal of Molecular SciencesGenetic & Genomic MedicinePeer-reviewed journal article
Aggregated genomic data as cohort-specific allelic frequencies can boost variants and genes prioritization in non-solved cases of inherited retinal dystrophies
Ionut-Florin IancuiD, Irene Perea‐RomeroiD, Gonzalo Núñez‐MorenoiD, Lorena de la Fuente, Raquel Romero, Almudena Ávila‐Fernández, María José Trujillo-Tiebas, Rosa Riveiro-ÁlvareziD, Berta AlmogueraiD, Inmaculada Martín-Mérida, Marta Del Pozo‐ValeroiD, Alejandra DamiániD, Marta CortóniD, Carmen AyusoiD, Pablo MíngueziD
Abstract
The introduction of NGS in genetic diagnosis has increased the repertoire of variants and genes involved and the amount of genomic information produced. We built an allelic-frequency (AF) database for a heterogeneous cohort of genetic diseases to explore the aggregated genomic information and boost diagnosis in inherited retinal dystrophies (IRD). We retrospectively selected 5683 index-cases with clinical exome sequencing tests available, 1766 with IRD and the rest, with diverse genetic diseases. We calculated subcohorts IRD specific AF and compare it with suitable pseudocontrols. For non-solved IRD cases, we prioritized variants with a significant increment of frequencies, with 8 variants that may contribute to explain the phenotype, and 10/11 of uncertain significance that were reclassified as likely-pathogenic according to ACMG. Besides, we developed a method to highlight genes with more frequent pathogenic variants in IRD cases than in pseudocontrols weighted by the increment of benign variants in the same comparison. We identified 18 genes for further studies that provided new insights in five cases. This resource can also help to calculate the carrier-frequency in IRD genes. A cohort-specific AF database assist with variants and genes prioritization and operate as an engine that provides new hypothesis in non-solved cases, augmenting diagnosis rate.
- organism: human
- assay: whole-exome sequencing
- research focus: variant effect prediction
- research focus: gene regulatory network inference
- approach: statistical model
- Citations
- 10
- FWCI
- 1.06
- Field percentile
- top 26%
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