Identification of rare-disease genes using blood transcriptome sequencing and large control cohorts

Frésard, Laure; Smail, Craig; Ferraro, Nicole M.; Teran, Nicole A.; Li, Xin; Smith, Kevin S.; Bonner, Devon; Kernohan, Kristin D.; Marwaha, Shruti; Zappala, Zachary; Balliu, Brunilda; Davis, Joe R.; Liu, Boxiang; Prybol, Cameron J.; Kohler, Jennefer N.; Zastrow, Diane B.; Reuter, Chloe; Fisk, Dianna G.; Grove, Megan E.; Davidson, Jean M.; Hartley, Taila; Joshi, Ruchi; Strober, Benjamin J.; Utiramerur, Sowmithri; Lind, Lars; Ingelsson, Erik; Battle, Alexis; Bejerano, Gill; Bernstein, Jonathan A.; Ashley, Euan A.; Boycott, Kym M.; Merker, Jason D.; Wheeler, Matthew T.; Montgomery, Stephen B.

doi:10.1038/s41591-019-0457-8

Cited by 256 publications

(294 citation statements)

References 57 publications

Supporting

Mentioning

279

Contrasting

Unclassified

Order By: Relevance

“…As the baseline for our approach, we used a simple beta-binomial 12 distribution (BB) with no correction for existing covariation and the parameters and were 13 estimated with the R package VGAM. 49 Further, we implemented a Z score approach similar to 14 the approach described by Frésard et al 16 Instead of regressing out the top principal 15 components accounting for 95% of the variation within the data, we used the top loadings of the 16 PCA maximizing the precision-recall of in silico injected splicing outliers and computed the Z 17 scores according to Equation (16). Finally, we implemented the Leafcutter 18 approach described 18 by Kremer et al, 15 where one sample is compared against all others within the dataset and no 19 control for latent sources of sample covariation is considered.…”

Section: (Equation 16) 18mentioning

confidence: 99%

See 1 more Smart Citation

Detection of aberrant splicing events in RNA-seq data with FRASER

Mertes

Scheller

Yépez

et al. 2019

Preprint

View full text Add to dashboard Cite

12Aberrant splicing is a major cause of rare diseases, yet its prediction from genome 13 sequence remains in most cases inconclusive. Recently, RNA sequencing has proven to 14 be an effective complementary avenue to detect aberrant splicing. Here, we developed 15 FRASER, an algorithm to detect aberrant splicing from RNA sequencing data. Unlike 16 existing methods, FRASER captures not only alternative splicing but also intron retention 17 events. This typically doubles the number of detected aberrant events and identified a 18 pathogenic intron retention in MCOLN1. FRASER automatically controls for latent 19 confounders, which are widespread and substantially affect sensitivity. Moreover, 20 FRASER is based on a count distribution and multiple testing correction, reducing the 21 number of calls by two orders of magnitude over commonly applied z score cutoffs, with 1 a minor sensitivity loss. The application to rare disease diagnostics is demonstrated by 2 reprioritizing a pathogenic aberrant exon truncation in TAZ from a published dataset. 3 FRASER is easy to use and freely available. 4

show abstract

Section: (Equation 16) 18mentioning

confidence: 99%

“…This benchmark also shows that FRASER 4 outperforms both state-of-the-art methods, which are the Leafcutter based approach 15 and the z 5 score based approach. 16 PCA controlled splicing metrics (x-axis). In every panel, the enrichment for each of the 48 GTEx tissues 13 is equal or higher for FRASER (points above the diagonal line).…”

mentioning

confidence: 99%

Detection of aberrant splicing events in RNA-seq data with FRASER

Mertes

Scheller

Yépez

et al. 2019

Preprint

View full text Add to dashboard Cite

show abstract

“…Clinical RNA-seq is one approach by which laboratories can identify splicing aberrations among other changes to the transcriptomes. Previous work in several labs have demonstrated that RNA-seq can enable genetic diagnosis in patients previously unsolved by exome or genome sequencing [13][14][15][16][17][18] .…”

Section: Introductionmentioning

confidence: 99%

“…Several recent studies consider limitations of using RNA-seq from CATs for clinical diagnosis. Frésard et al 2019 demonstrate that RNA-seq in whole blood can make some diagnoses in patients from diverse disease categories 16 . However, Cummings et al 2017, studying a cohort of patients with neuromuscular disease, perform RNA-seq on skeletal muscle biopsies motivated by low gene expression of many known neuromuscular disease genes in whole blood and fibroblasts 13 .…”

Section: Introductionmentioning

confidence: 99%

Mapping RNA splicing variations in clinically-accessible and non-accessible tissues to facilitate Mendelian disease diagnosis using RNA-seq

Aicher

Jewell

Vaquero-Garcia

et al. 2019

Preprint

View full text Add to dashboard Cite

Purpose : RNA-seq is a promising approach to improve diagnoses by detecting pathogenic aberrations in RNA splicing that are missed by DNA sequencing. RNA-seq is typically performed on clinically-accessible tissues (CATs) from blood and skin. RNA tissue-specificity makes it difficult to identify aberrations in relevant but non-accessible tissues (non-CATs). We determined how RNA-seq from CATs represent splicing in and across genes and non-CATs.Methods : We quantified RNA splicing in 801 RNA-seq samples from 56 different adult and fetal tissues from GTEx and ArrayExpress. We identified genes and splicing events in each non-CAT and determined when RNA-seq in each CAT would inadequately represent them. We developed an online resource, MAJIQ-CAT, for exploring our analysis for specific genes and tissues.Results : In non-CATs, 39.7% of genes have splicing that is inadequately represented by at least one CAT. 6.2% of genes have splicing inadequately represented by all CATs. A majority (52.8%) of inadequately represented genes are lowly expressed in CATs (TPM < 1), but 6.2% are inadequately represented despite being well expressed (TPM > 10).Conclusion : Many splicing events in non-CATs are inadequately evaluated using RNA-seq from CATs. MAJIQ-CAT allows users to explore which accessible tissues, if any, best represent splicing in genes and tissues of interest.

show abstract

“…Mutations that alter mRNA splicing are known to lead to many human monogenic diseases including spinal muscular atrophy (SMA), neurofibromatosis type 1 (NF1), cystic fibrosis (CF), familial dysautonomia (FD), Duchenne muscular dystrophy (DMD) and myotonic dystrophy (DM), as well as contribute to complex diseases such as cancer and diabetes [3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18] . The emergence of high throughput sequencing of large disease cohorts [19][20][21] , and the remarkable efforts to aggregate and annotate these mutations in an accessible infrastructure such as ClinVar 22 , now provides an unprecedented opportunity to apply novel deep learning approaches to predict mutations that affect pre-mRNA splicing 23 . The potential of developing such models will continue to increase as next generation transcriptome sequencing (RNASeq) data are amassed and curation of the associated mutational processes matures [23][24][25][26] .…”

mentioning

confidence: 99%

A deep learning approach to identify new gene targets of a novel therapeutic for human splicing disorders

Gao

Morini

Salani

et al. 2020

Preprint

View full text Add to dashboard Cite

Pre-mRNA splicing is a key control point in human gene expression. Disturbances in splicing due to mutation or aberrant splicing regulatory networks lead to dysregulated protein expression and contribute to a substantial fraction of human disease. Several classes of active and selective splicing modulator compounds have been recently identified, thus proving that pre-mRNA splicing is a viable target for therapy. We describe herein the identification of BPN-15477, a novel splicing modulator compound, that restores correct splicing of exon 20 in the Elongator complex protein 1 (ELP1) gene carrying the major IVS20+6T>C mutation responsible for familial dysautonomia. We then developed a machine learning approach to evaluate the therapeutic potential of BPN-15477 to correct splicing in other human genetic diseases. Using transcriptome sequencing from compound-treated fibroblast cells, we identified treatment responsive sequence signatures, the majority of which center at the 5' splice site of exons whose inclusion or exclusion is modulated by SMC treatment. We then leveraged this model to identify 155 human disease genes that harbor ClinVar mutations predicted to alter pre-mRNA splicing as potential targets for BPN-15477 treatment. Using in vitro splicing assays, we validated representative predictions by demonstrating successful correction of splicing defects caused by mutations in genes responsible for cystic fibrosis (CFTR), cholesterol ester storage disease (LIPA), Lynch syndrome (MLH1) and familial frontotemporal dementia (MAPT). Our study shows that deep learning techniques can identify a complex set of sequence signatures and predict response to pharmacological modulation, strongly supporting the use of in silico approaches to expand the therapeutic potential of drugs that modulate splicing.

show abstract

Identification of rare-disease genes using blood transcriptome sequencing and large control cohorts

Cited by 256 publications

References 57 publications

Detection of aberrant splicing events in RNA-seq data with FRASER

Detection of aberrant splicing events in RNA-seq data with FRASER

Mapping RNA splicing variations in clinically-accessible and non-accessible tissues to facilitate Mendelian disease diagnosis using RNA-seq

A deep learning approach to identify new gene targets of a novel therapeutic for human splicing disorders

Contact Info

Product

Resources

About