Whole-Transcriptome Analysis by RNA Sequencing for Genetic Diagnosis of Mendelian Skin Disorders in the Context of Consanguinity

Abstract

Background Among the approximately 8000 Mendelian disorders, >1000 have cutaneous manifestations. In many of these conditions, the underlying mutated genes have been identified by DNA-based techniques which, however, can overlook certain types of mutations, such as exonic-synonymous and deep-intronic sequence variants. Whole-transcriptome sequencing by RNA sequencing (RNA-seq) can identify such mutations and provide information about their consequences . Methods We analyzed the whole transcriptome of 40 families with different types of Mendelian skin disorders with extensive genetic heterogeneity. The RNA-seq data were examined for variant detection and prioritization, pathogenicity confirmation, RNA expression profiling, and genome-wide homozygosity mapping in the case of consanguineous families. Among the families examined, RNA-seq was able to provide information complementary to DNA-based analyses for exonic and intronic sequence variants with aberrant splicing. In addition, we tested the possibility of using RNA-seq as the first-tier strategy for unbiased genome-wide mutation screening without information from DNA analysis. Results We found pathogenic mutations in 35 families (88%) with RNA-seq in combination with other next-generation sequencing methods, and we successfully prioritized variants and found the culprit genes. In addition, as a novel concept, we propose a pipeline that increases the yield of variant calling from RNA-seq by concurrent use of genome and transcriptome references in parallel. Conclusions Our results suggest that “clinical RNA-seq” could serve as a primary approach for mutation detection in inherited diseases, particularly in consanguineous families, provided that tissues and cells expressing the relevant genes are available for analysis.

Whole-transcriptome sequencing–based concomitant detection of viral and human genetic determinants of cutaneous lesions

“…The pipeline was developed using the Python programming language employing third-party tools, including STAR (12), HISAT2 (13), SAMtools (14), eXpress (15), Subread featureCounts (16), FreeBayes (http:// arxiv.org/abs/1207.3907), Salmon (17), and SnpEff packages (18). RNA extraction and whole-transcriptome sequencing were performed as previously described (19). In this process, the quality of RNA-Seq data is assessed by FastQC, and according to the results, trimming is considered.…”

Losartan treatment improves recessive dystrophic epidermolysis bullosa: A case series

“…The diagnosis of RDEB was made on the basis of clinical features and histopathological findings, and confirmed by demonstration of biallelic loss of function mutations in COL7A1 by whole-exome sequencing and whole-transcriptome sequencing (Table 1). 10,11 Clinically, five of the seven patients were considered to be of generalized severe subtype of RDEB, while two (patients' nos. 1 and 6) were diagnosed as intermediate in severity.…”

Improving genetic diagnosis of Mendelian disease with RNA sequencing: a narrative review

“…RNA-seq can be used for variant calling, confirming and extending the information derived from DNA-based analyses. Mapping of RNA-seq data to the genome and transcriptome references concurrently can increase the yield of variant calling [42] . It is proposed that rare variants (allele frequency <0.01 in gnomAD database) covered by at least 5 reads in RNA-seq data are selected for prioritization and validation [22] .…”

“…Significantly, variant calling from RNA-seq has been recently spotlighted in genetic diagnosis of Mendelian disorders [42] …”

“…Mapping of RNA-seq data to the genome and transcriptome references concurrently can increase the yield of variant calling. [42] It is proposed that rare variants (allele frequency <0.01 in gnomAD database) covered by at least 5 reads in RNAseq data are selected for prioritization and validation. [22] Variant calling in RNA-seq data is affected by multiple factors, including uneven coverage resulting from the alterative splicing, variable gene expression, and allele-specific expression.…”