Modeling fragment counts improves single-cell ATAC-seq analysis.

Single-cell ATAC sequencing coverage in regulatory regions is typically binarized as an indicator of open chromatin. Here we show that binarization is an unnecessary step that neither improves goodness of fit, clustering, cell type identification nor batch integration. Fragment counts, but not read counts, should instead be modeled, which preserves quantitative regulatory information. These results have immediate implications for single-cell ATAC sequencing analysis.

Improved detection of aberrant splicing with FRASER 2.0 and the intron Jaccard index.

Detection of aberrantly spliced genes is an important step in RNA-seq-based rare-disease diagnostics. We recently developed FRASER, a denoising autoencoder-based method that outperformed alternative methods of detecting aberrant splicing. However, because FRASER's three splice metrics are partially redundant and tend to be sensitive to sequencing depth, we introduce here a more robust intron-excision metric, the intron Jaccard index, that combines the alternative donor, alternative acceptor, and intron-retention signal into a single value. Moreover, we optimized model parameters and filter cutoffs by using candidate rare-splice-disrupting variants as independent evidence. On 16,213 GTEx samples, our improved algorithm, FRASER 2.0, called typically 10 times fewer splicing outliers while increasing the proportion of candidate rare-splice-disrupting variants by 10-fold and substantially decreasing the effect of sequencing depth on the number of reported outliers. To lower the multiple-testing correction burden, we introduce an option to select the genes to be tested for each sample instead of a transcriptome-wide approach. This option can be particularly useful when prior information, such as candidate variants or genes, is available. Application on 303 rare-disease samples confirmed the relative reduction in the number of outlier calls for a slight loss of sensitivity; FRASER 2.0 recovered 22 out of 26 previously identified pathogenic splicing cases with default cutoffs and 24 when multiple-testing correction was limited to OMIM genes containing rare variants. Altogether, these methodological improvements contribute to more effective RNA-seq-based rare diagnostics by drastically reducing the amount of splicing outlier calls per sample at minimal loss of sensitivity.

Cellular energy regulates mRNA degradation in a codon-specific manner.

Codon optimality is a major determinant of mRNA translation and degradation rates. However, whether and through which mechanisms its effects are regulated remains poorly understood. Here we show that codon optimality associates with up to 2-fold change in mRNA stability variations between human tissues, and that its effect is attenuated in tissues with high energy metabolism and amplifies with age. Mathematical modeling and perturbation data through oxygen deprivation and ATP synthesis inhibition reveal that cellular energy variations non-uniformly alter the effect of codon usage. This new mode of codon effect regulation, independent of tRNA regulation, provides a fundamental mechanistic link between cellular energy metabolism and eukaryotic gene expression.

Genome and RNA sequencing were essential to reveal cryptic intronic variants associated to defective ATP6AP1 mRNA processing.

The diagnosis of Mendelian disorders has notably advanced with integration of whole exome and genome sequencing (WES and WGS) in clinical practice. However, challenges in variant interpretation and uncovered variants by WES still leave a substantial percentage of patients undiagnosed. In this context, integrating RNA sequencing (RNA-seq) improves diagnostic workflows, particularly for WES inconclusive cases. Additionally, functional studies are often necessary to elucidate the impact of prioritized variants on gene expression and protein function. Our study focused on three unrelated male patients (P1-P3) with ATP6AP1-CDG (congenital disorder of glycosylation), presenting with intellectual disability and varying degrees of hepatopathy, glycosylation defects, and an initially inconclusive diagnosis through WES. Subsequent RNA-seq was pivotal in identifying the underlying genetic causes in P1 and P2, detecting ATP6AP1 underexpression and aberrant splicing. Molecular studies in fibroblasts confirmed these findings and identified the rare intronic variants c.289-233C > T and c.289-289G > A in P1 and P2, respectively. Trio-WGS also revealed the variant c.289-289G > A in P3, which was a de novo change in both patients. Functional assays expressing the mutant alleles in HAP1 cells demonstrated the pathogenic impact of these variants by reproducing the splicing alterations observed in patients. Our study underscores the role of RNA-seq and WGS in enhancing diagnostic rates for genetic diseases such as CDG, providing new insights into ATP6AP1-CDG molecular bases by identifying the first two deep intronic variants in this X-linked gene. Additionally, our study highlights the need to integrate RNA-seq and WGS, followed by functional validation, in routine diagnostics for a comprehensive evaluation of patients with an unidentified molecular etiology.

An Integrated Transcriptomics and Genomics Approach Detects an X/Autosome Translocation in a Female with Duchenne Muscular Dystrophy.

Duchenne and Becker muscular dystrophies, caused by pathogenic variants in DMD, are the most common inherited neuromuscular conditions in childhood. These diseases follow an X-linked recessive inheritance pattern, and mainly males are affected. The most prevalent pathogenic variants in the DMD gene are copy number variants (CNVs), and most patients achieve their genetic diagnosis through Multiplex Ligation-dependent Probe Amplification (MLPA) or exome sequencing. Here, we investigated a female patient presenting with muscular dystrophy who remained genetically undiagnosed after MLPA and exome sequencing. RNA sequencing (RNAseq) from the patient's muscle biopsy identified an 85% reduction in DMD expression compared to 116 muscle samples included in the cohort. A de novo balanced translocation between chromosome 17 and the X chromosome (t(X;17)(p21.1;q23.2)) disrupting the DMD and BCAS3 genes was identified through trio whole genome sequencing (WGS). The combined analysis of RNAseq and WGS played a crucial role in the detection and characterisation of the disease-causing variant in this patient, who had been undiagnosed for over two decades. This case illustrates the diagnostic odyssey of female DMD patients with complex structural variants that are not detected by current panel or exome sequencing analysis.

Bi-Allelic UQCRFS1 Variants Are Associated with Mitochondrial Complex III Deficiency, Cardiomyopathy, and Alopecia Totalis.

Isolated complex III (CIII) deficiencies are among the least frequently diagnosed mitochondrial disorders. Clinical symptoms range from isolated myopathy to severe multi-systemic disorders with early death and disability. To date, we know of pathogenic variants in genes encoding five out of 10 subunits and five out of 13 assembly factors of CIII. Here we describe rare bi-allelic variants in the gene of a catalytic subunit of CIII, UQCRFS1, which encodes the Rieske iron-sulfur protein, in two unrelated individuals. Affected children presented with low CIII activity in fibroblasts, lactic acidosis, fetal bradycardia, hypertrophic cardiomyopathy, and alopecia totalis. Studies in proband-derived fibroblasts showed a deleterious effect of the variants on UQCRFS1 protein abundance, mitochondrial import, CIII assembly, and cellular respiration. Complementation studies via lentiviral transduction and overexpression of wild-type UQCRFS1 restored mitochondrial function and rescued the cellular phenotype, confirming UQCRFS1 variants as causative for CIII deficiency. We demonstrate that mutations in UQCRFS1 can cause mitochondrial disease, and our results thereby expand the clinical and mutational spectrum of CIII deficiencies.

Diagnostic Odyssey in an Adult Patient with Ophthalmologic Abnormalities and Hearing Loss: Contribution of RNA-Seq to the Diagnosis of a PEX1 Deficiency.

Peroxisomal biogenesis disorders (PBDs) are a heterogeneous group of genetic diseases. Multiple peroxisomal pathways are impaired, and very long chain fatty acids (VLCFA) are the first line biomarkers for the diagnosis. The clinical presentation of PBDs may range from severe, lethal multisystemic disorders to milder, late-onset disease. The vast majority of PBDs belong to Zellweger Spectrum Disordes (ZSDs) and represents a continuum of overlapping clinical symptoms, with Zellweger syndrome being the most severe and Heimler syndrome the less severe disease. Mild clinical conditions frequently present normal or slight biochemical alterations, making the diagnosis of these patients challenging. In the present study we used a combined WES and RNA-seq strategy to diagnose a patient presenting with retinal dystrophy as the main clinical symptom. Results showed the patient was compound heterozygous for mutations in PEX1. VLCFA were normal, but retrospective analysis of lysosphosphatidylcholines (LPC) containing C22:0-C26:0 species was altered. This simple test could avoid the diagnostic odyssey of patients with mild phenotype, such as the individual described here, who was diagnosed very late in adult life. We provide functional data in cell line models that may explain the mild phenotype of the patient by demonstrating the hypomorphic nature of a deep intronic variant altering PEX1 mRNA processing.

OUTRIDER: A Statistical Method for Detecting Aberrantly Expressed Genes in RNA Sequencing Data.

RNA sequencing (RNA-seq) is gaining popularity as a complementary assay to genome sequencing for precisely identifying the molecular causes of rare disorders. A powerful approach is to identify aberrant gene expression levels as potential pathogenic events. However, existing methods for detecting aberrant read counts in RNA-seq data either lack assessments of statistical significance, so that establishing cutoffs is arbitrary, or rely on subjective manual corrections for confounders. Here, we describe OUTRIDER (Outlier in RNA-Seq Finder), an algorithm developed to address these issues. The algorithm uses an autoencoder to model read-count expectations according to the gene covariation resulting from technical, environmental, or common genetic variations. Given these expectations, the RNA-seq read counts are assumed to follow a negative binomial distribution with a gene-specific dispersion. Outliers are then identified as read counts that significantly deviate from this distribution. The model is automatically fitted to achieve the best recall of artificially corrupted data. Precision-recall analyses using simulated outlier read counts demonstrated the importance of controlling for covariation and significance-based thresholds. OUTRIDER is open source and includes functions for filtering out genes not expressed in a dataset, for identifying outlier samples with too many aberrantly expressed genes, and for detecting aberrant gene expression on the basis of false-discovery-rate-adjusted p values. Overall, OUTRIDER provides an end-to-end solution for identifying aberrantly expressed genes and is suitable for use by rare-disease diagnostic platforms.

Analysis of 3760 hematologic malignancies reveals rare transcriptomic aberrations of driver genes.

BACKGROUND: Rare oncogenic driver events, particularly affecting the expression or splicing of driver genes, are suspected to substantially contribute to the large heterogeneity of hematologic malignancies. However, their identification remains challenging. METHODS: To address this issue, we generated the largest dataset to date of matched whole genome sequencing and total RNA sequencing of hematologic malignancies from 3760 patients spanning 24 disease entities. Taking advantage of our dataset size, we focused on discovering rare regulatory aberrations. Therefore, we called expression and splicing outliers using an extension of the workflow DROP (Detection of RNA Outliers Pipeline) and AbSplice, a variant effect predictor that identifies genetic variants causing aberrant splicing. We next trained a machine learning model integrating these results to prioritize new candidate disease-specific driver genes. RESULTS: We found a median of seven expression outlier genes, two splicing outlier genes, and two rare splice-affecting variants per sample. Each category showed significant enrichment for already well-characterized driver genes, with odds ratios exceeding three among genes called in more than five samples. On held-out data, our integrative modeling significantly outperformed modeling based solely on genomic data and revealed promising novel candidate driver genes. Remarkably, we found a truncated form of the low density lipoprotein receptor LRP1B transcript to be aberrantly overexpressed in about half of hairy cell leukemia variant (HCL-V) samples and, to a lesser extent, in closely related B-cell neoplasms. This observation, which was confirmed in an independent cohort, suggests LRP1B as a novel marker for a HCL-V subclass and a yet unreported functional role of LRP1B within these rare entities. CONCLUSIONS: Altogether, our census of expression and splicing outliers for 24 hematologic malignancy entities and the companion computational workflow constitute unique resources to deepen our understanding of rare oncogenic events in hematologic cancers.

Phenotype-driven genomics enhance diagnosis in children with unresolved neuromuscular diseases.

Establishing a molecular diagnosis remains challenging in half of individuals with childhood-onset neuromuscular diseases (NMDs) despite exome sequencing. This study evaluates the diagnostic utility of combining genomic approaches in undiagnosed NMD patients. We performed deep phenotyping of 58 individuals with unsolved childhood-onset NMDs that have previously undergone inconclusive exome studies. Genomic approaches included trio genome sequencing and RNASeq. Genetic diagnoses were reached in 23 out of 58 individuals (40%). Twenty-one individuals carried causal single nucleotide variants (SNVs) or small insertions and deletions, while 2 carried pathogenic structural variants (SVs). Genomic sequencing identified pathogenic variants in coding regions or at the splice site in 17 out of 21 resolved cases, while RNA sequencing was additionally required for the diagnosis of 4 cases. Reasons for previous diagnostic failures included low coverage in exonic regions harboring the second pathogenic variant and involvement of genes that were not yet linked to human diseases at the time of the first NGS analysis. In summary, our systematic genetic analysis, integrating deep phenotyping, trio genome sequencing and RNASeq, proved effective in diagnosing unsolved childhood-onset NMDs. This approach holds promise for similar cohorts, offering potential improvements in diagnostic rates and clinical management of individuals with NMDs.

Unravelling undiagnosed rare disease cases by HiFi long-read genome sequencing.

Solve-RD is a pan-European rare disease (RD) research program that aims to identify disease-causing genetic variants in previously undiagnosed RD families. We utilised 10-fold coverage HiFi long-read sequencing (LRS) for detecting causative structural variants (SVs), single nucleotide variants (SNVs), insertion-deletions (InDels), and short tandem repeat (STR) expansions in extensively studied RD families without clear molecular diagnoses. Our cohort includes 293 individuals from 114 genetically undiagnosed RD families selected by European Rare Disease Network (ERN) experts. Of these, 21 families were affected by so-called 'unsolvable' syndromes for which genetic causes remain unknown, and 93 families with at least one individual affected by a rare neurological, neuromuscular, or epilepsy disorder without genetic diagnosis despite extensive prior testing. Clinical interpretation and orthogonal validation of variants in known disease genes yielded thirteen novel genetic diagnoses due to de novo and rare inherited SNVs, InDels, SVs, and STR expansions. In an additional four families, we identified a candidate disease-causing SV affecting several genes including an MCF2 / FGF13 fusion and PSMA3 deletion. However, no common genetic cause was identified in any of the 'unsolvable' syndromes. Taken together, we found (likely) disease-causing genetic variants in 13.0% of previously unsolved families and additional candidate disease-causing SVs in another 4.3% of these families. In conclusion, our results demonstrate the added value of HiFi long-read genome sequencing in undiagnosed rare diseases.

Improved detection of aberrant splicing using the Intron Jaccard Index.

Detection of aberrantly spliced genes is an important step in RNA-seq-based rare disease diagnostics. We recently developed FRASER, a denoising autoencoder-based method for aberrant splicing detection that outperformed alternative approaches. However, as FRASER's three splice metrics are partially redundant and tend to be sensitive to sequencing depth, we introduce here a more robust intron excision metric, the Intron Jaccard Index, that combines alternative donor, alternative acceptor, and intron retention signal into a single value. Moreover, we optimized model parameters and filter cutoffs using candidate rare splice-disrupting variants as independent evidence. On 16,213 GTEx samples, our improved algorithm called typically 10 times fewer splicing outliers while increasing the proportion of candidate rare splice-disrupting variants by 10 fold and substantially decreasing the effect of sequencing depth on the number of reported outliers. Application on 303 rare disease samples confirmed the reduction fold-change of the number of outlier calls for a slight loss of sensitivity (only 2 out of 22 previously identified pathogenic splicing cases not recovered). Altogether, these methodological improvements contribute to more effective RNA-seq-based rare diagnostics by a drastic reduction of the amount of splicing outlier calls per sample at minimal loss of sensitivity.

Aberrant splicing prediction across human tissues.

Aberrant splicing is a major cause of genetic disorders but its direct detection in transcriptomes is limited to clinically accessible tissues such as skin or body fluids. While DNA-based machine learning models can prioritize rare variants for affecting splicing, their performance in predicting tissue-specific aberrant splicing remains unassessed. Here we generated an aberrant splicing benchmark dataset, spanning over 8.8 million rare variants in 49 human tissues from the Genotype-Tissue Expression (GTEx) dataset. At 20% recall, state-of-the-art DNA-based models achieve maximum 12% precision. By mapping and quantifying tissue-specific splice site usage transcriptome-wide and modeling isoform competition, we increased precision by threefold at the same recall. Integrating RNA-sequencing data of clinically accessible tissues into our model, AbSplice, brought precision to 60%. These results, replicated in two independent cohorts, substantially contribute to noncoding loss-of-function variant identification and to genetic diagnostics design and analytics.

Leigh syndrome is the main clinical characteristic of PTCD3 deficiency.

Mitochondrial translation defects are a continuously growing group of disorders showing a large variety of clinical symptoms including a wide range of neurological abnormalities. To date, mutations in PTCD3, encoding a component of the mitochondrial ribosome, have only been reported in a single individual with clinical evidence of Leigh syndrome. Here, we describe three additional PTCD3 individuals from two unrelated families, broadening the genetic and phenotypic spectrum of this disorder, and provide definitive evidence that PTCD3 deficiency is associated with Leigh syndrome. The patients presented in the first months of life with psychomotor delay, respiratory insufficiency and feeding difficulties. The neurologic phenotype included dystonia, optic atrophy, nystagmus and tonic-clonic seizures. Brain MRI showed optic nerve atrophy and thalamic changes, consistent with Leigh syndrome. WES and RNA-seq identified compound heterozygous variants in PTCD3 in both families: c.[1453-1G>C];[1918C>G] and c.[710del];[902C>T]. The functional consequences of the identified variants were determined by a comprehensive characterization of the mitochondrial function. PTCD3 protein levels were significantly reduced in patient fibroblasts and, consistent with a mitochondrial translation defect, a severe reduction in the steady state levels of complexes I and IV subunits was detected. Accordingly, the activity of these complexes was also low, and high-resolution respirometry showed a significant decrease in the mitochondrial respiratory capacity. Functional complementation studies demonstrated the pathogenic effect of the identified variants since the expression of wild-type PTCD3 in immortalized fibroblasts restored the steady-state levels of complexes I and IV subunits as well as the mitochondrial respiratory capacity. Additionally, minigene assays demonstrated that three of the identified variants were pathogenic by altering PTCD3 mRNA processing. The fourth variant was a frameshift leading to a truncated protein. In summary, we provide evidence of PTCD3 involvement in human disease confirming that PTCD3 deficiency is definitively associated with Leigh syndrome.

Diagnostic potential of the amniotic fluid cells transcriptome in deciphering mendelian disease: a proof-of-concept.

RNA sequencing (RNA-seq) is emerging in genetic diagnoses as it provides functional support for the interpretation of variants of uncertain significance. However, the use of amniotic fluid (AF) cells for RNA-seq has not yet been explored. Here, we examined the expression of clinically relevant genes in AF cells (n = 48) compared with whole blood and fibroblasts. The number of well-expressed genes in AF cells was comparable to that in fibroblasts and much higher than that in blood across different disease categories. We found AF cells RNA-seq feasible and beneficial in prenatal diagnosis (n = 4) as transcriptomic data elucidated the molecular consequence leading to the pathogenicity upgrade of variants in CHD7 and COL1A2 and revising the in silico prediction of a variant in MYRF. AF cells RNA-seq could become a reasonable choice for postnatal patients with advantages over fibroblasts and blood as it prevents invasive procedures.

Clinical implementation of RNA sequencing for Mendelian disease diagnostics.

BACKGROUND: Lack of functional evidence hampers variant interpretation, leaving a large proportion of individuals with a suspected Mendelian disorder without genetic diagnosis after whole genome or whole exome sequencing (WES). Research studies advocate to further sequence transcriptomes to directly and systematically probe gene expression defects. However, collection of additional biopsies and establishment of lab workflows, analytical pipelines, and defined concepts in clinical interpretation of aberrant gene expression are still needed for adopting RNA sequencing (RNA-seq) in routine diagnostics. METHODS: We implemented an automated RNA-seq protocol and a computational workflow with which we analyzed skin fibroblasts of 303 individuals with a suspected mitochondrial disease that previously underwent WES. We also assessed through simulations how aberrant expression and mono-allelic expression tests depend on RNA-seq coverage. RESULTS: We detected on average 12,500 genes per sample including around 60% of all disease genes-a coverage substantially higher than with whole blood, supporting the use of skin biopsies. We prioritized genes demonstrating aberrant expression, aberrant splicing, or mono-allelic expression. The pipeline required less than 1 week from sample preparation to result reporting and provided a median of eight disease-associated genes per patient for inspection. A genetic diagnosis was established for 16% of the 205 WES-inconclusive cases. Detection of aberrant expression was a major contributor to diagnosis including instances of 50% reduction, which, together with mono-allelic expression, allowed for the diagnosis of dominant disorders caused by haploinsufficiency. Moreover, calling aberrant splicing and variants from RNA-seq data enabled detecting and validating splice-disrupting variants, of which the majority fell outside WES-covered regions. CONCLUSION: Together, these results show that streamlined experimental and computational processes can accelerate the implementation of RNA-seq in routine diagnostics.

How Machine Learning and Statistical Models Advance Molecular Diagnostics of Rare Disorders Via Analysis of RNA Sequencing Data.

Rare diseases, although individually rare, collectively affect approximately 350 million people worldwide. Currently, nearly 6,000 distinct rare disorders with a known molecular basis have been described, yet establishing a specific diagnosis based on the clinical phenotype is challenging. Increasing integration of whole exome sequencing into routine diagnostics of rare diseases is improving diagnostic rates. Nevertheless, about half of the patients do not receive a genetic diagnosis due to the challenges of variant detection and interpretation. During the last years, RNA sequencing is increasingly used as a complementary diagnostic tool providing functional data. Initially, arbitrary thresholds have been applied to call aberrant expression, aberrant splicing, and mono-allelic expression. With the application of RNA sequencing to search for the molecular diagnosis, the implementation of robust statistical models on normalized read counts allowed for the detection of significant outliers corrected for multiple testing. More recently, machine learning methods have been developed to improve the normalization of RNA sequencing read count data by taking confounders into account. Together the methods have increased the power and sensitivity of detection and interpretation of pathogenic variants, leading to diagnostic rates of 10-35% in rare diseases. In this review, we provide an overview of the methods used for RNA sequencing and illustrate how these can improve the diagnostic yield of rare diseases.

Detection of aberrant splicing events in RNA-seq data using FRASER.

Aberrant splicing is a major cause of rare diseases.  However, its prediction from genome sequence alone remains in most cases inconclusive. Recently, RNA sequencing has proven to be an effective complementary avenue to detect aberrant splicing. Here, we develop FRASER, an algorithm to detect aberrant splicing from RNA sequencing data. Unlike existing methods, FRASER captures not only alternative splicing but also intron retention events. This typically doubles the number of detected aberrant events and identified a pathogenic intron retention in MCOLN1 causing mucolipidosis. FRASER automatically controls for latent confounders, which are widespread and affect sensitivity substantially. Moreover, FRASER is based on a count distribution and multiple testing correction, thus reducing the number of calls by two orders of magnitude over commonly applied z score cutoffs, with a minor loss of sensitivity. Applying FRASER to rare disease diagnostics is demonstrated by reprioritizing a pathogenic aberrant exon truncation in TAZ from a published dataset. FRASER is easy to use and freely available.

Detection of aberrant gene expression events in RNA sequencing data.

RNA sequencing (RNA-seq) has emerged as a powerful approach to discover disease-causing gene regulatory defects in individuals affected by genetically undiagnosed rare disorders. Pioneering studies have shown that RNA-seq could increase the diagnosis rates over DNA sequencing alone by 8-36%, depending on the disease entity and tissue probed. To accelerate adoption of RNA-seq by human genetics centers, detailed analysis protocols are now needed. We present a step-by-step protocol that details how to robustly detect aberrant expression levels, aberrant splicing and mono-allelic expression in RNA-seq data using dedicated statistical methods. We describe how to generate and assess quality control plots and interpret the analysis results. The protocol is based on the detection of RNA outliers pipeline (DROP), a modular computational workflow that integrates all the analysis steps, can leverage parallel computing infrastructures and generates browsable web page reports.

Transcriptome-directed analysis for Mendelian disease diagnosis overcomes limitations of conventional genomic testing.

BACKGROUNDTranscriptome sequencing (RNA-seq) improves diagnostic rates in individuals with suspected Mendelian conditions to varying degrees, primarily by directing the prioritization of candidate DNA variants identified on exome or genome sequencing (ES/GS). Here we implemented an RNA-seq-guided method to diagnose individuals across a wide range of ages and clinical phenotypes.METHODSOne hundred fifteen undiagnosed adult and pediatric patients with diverse phenotypes and 67 family members (182 total individuals) underwent RNA-seq from whole blood and skin fibroblasts at the Baylor College of Medicine (BCM) Undiagnosed Diseases Network clinical site from 2014 to 2020. We implemented a workflow to detect outliers in gene expression and splicing for cases that remained undiagnosed despite standard genomic and transcriptomic analysis.RESULTSThe transcriptome-directed approach resulted in a diagnostic rate of 12% across the entire cohort, or 17% after excluding cases solved on ES/GS alone. Newly diagnosed conditions included Koolen-de Vries syndrome (KANSL1), Renpenning syndrome (PQBP1), TBCK-associated encephalopathy, NSD2- and CLTC-related intellectual disability, and others, all with negative conventional genomic testing, including ES and chromosomal microarray (CMA). Skin fibroblasts exhibited higher and more consistent expression of clinically relevant genes than whole blood. In solved cases with RNA-seq from both tissues, the causative defect was missed in blood in half the cases but none from fibroblasts.CONCLUSIONSFor our cohort of undiagnosed individuals with suspected Mendelian conditions, transcriptome-directed genomic analysis facilitated diagnoses, primarily through the identification of variants missed on ES and CMA.TRIAL REGISTRATIONNot applicable.FUNDINGNIH Common Fund, BCM Intellectual and Developmental Disabilities Research Center, Eunice Kennedy Shriver National Institute of Child Health & Human Development.