It is estimated that 350 million individuals worldwide suffer from rare diseases, which are predominantly caused by mutation in a single gene1. The current molecular diagnostic rate is estimated at 50%, with whole-exome sequencing (WES) among the most successful approaches2,3,4,5. For patients in whom WES is uninformative, RNA sequencing (RNA-seq) has shown diagnostic utility in specific tissues and diseases6,7,8. This includes muscle biopsies from patients with undiagnosed rare muscle disorders6,9, and cultured fibroblasts from patients with mitochondrial disorders7. However, for many individuals, biopsies are not performed for clinical care, and tissues are difficult to access. We sought to assess the utility of RNA-seq from blood as a diagnostic tool for rare diseases of different pathophysiologies. We generated whole-blood RNA-seq from 94 individuals with undiagnosed rare diseases spanning 16 diverse disease categories. We developed a robust approach to compare data from these individuals with large sets of RNA-seq data for controls (n = 1,594 unrelated controls and n = 49 family members) and demonstrated the impacts of expression, splicing, gene and variant filtering strategies on disease gene identification. Across our cohort, we observed that RNA-seq yields a 7.5% diagnostic rate, and an additional 16.7% with improved candidate gene resolution.
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UDN data are accessible through the UDN Gateway and through dbGaP entry at phs001232.v1.p1. DGN RNA-seq data are available by application through the NIMH Center for Collaborative Genomic Studies on Mental Disorders. Instructions for requesting access to data can be found at https://www.nimhgenetics.org/access_data_biomaterial.php, and inquiries should reference the ‘Depression Genes and Networks study (D. Levinson, PI)’. The GTEx Analysis v.7 release allele-specific expression data are available from dbGaP (dbGaP Accession phs000424.v7.p2). PIVUS RNA-seq data are accessible on the European Genome-Phenome Archive (EGAS00001003583). The Care4Rare data are available through Genomics4RD.
Code for running the analysis and producing the figures throughout the manuscript is available at https://github.com/lfresard/blood_rnaseq_rare_disease_paper. Our pipeline to highlight candidate variants is available at https://github.com/lfresard/blood_rnaseq_rare_disease_paper/blob/master/pipeline.md
Amberger, J. S., Bocchini, C. A., Schiettecatte, F., Scott, A. F. & Hamosh, A. OMIM.org: Online Mendelian Inheritance in Man (OMIM®), an online catalog of human genes and genetic disorders. Nucleic Acids Res. 43, D789–D798 (2015).
Boycott, K. M. et al. International cooperation to enable the diagnosis of all rare genetic diseases. Am. J. Hum. Genet. 100, 695–705 (2017).
Gilissen, C., Hoischen, A., Brunner, H. G. & Veltman, J. A. Unlocking Mendelian disease using exome sequencing. Genome Biol. 12, 228 (2011).
Yang, Y. et al. Clinical whole-exome sequencing for the diagnosis of Mendelian disorders. N. Engl. J. Med. 369, 1502–1511 (2013).
Ewans, L. J. et al. Whole-exome sequencing reanalysis at 12 months boosts diagnosis and is cost-effective when applied early in Mendelian disorders. Genet. Med. 20, 1564–1574 (2018).
Cummings, B. B. et al. Improving genetic diagnosis in Mendelian disease with transcriptome sequencing. Sci. Transl. Med. 9, eaal5209 (2017).
Kremer, L. S. et al. Genetic diagnosis of Mendelian disorders via RNA sequencing. Nat. Commun. 8, 15824 (2017).
Kernohan, K. D. et al. Whole-transcriptome sequencing in blood provides a diagnosis of spinal muscular atrophy with progressive myoclonic epilepsy. Hum. Mutat. 38, 611–614 (2017).
Hamanaka, K. et al. RNA sequencing solved the most common but unrecognized NEB pathogenic variant in Japanese nemaline myopathy. Genet. Med. https://doi.org/10.1038/s41436-018-0360-6. (2018).
Battle, A. et al. Characterizing the genetic basis of transcriptome diversity through RNA-sequencing of 922 individuals. Genome Res. 24, 14–24 (2014).
Lind, L. A comparison of three different methods to evaluate endothelium-dependent vasodilation in the elderly: the Prospective Investigation of the Vasculature in Uppsala Seniors (PIVUS) Study. Arterioscler. Thromb. Vasc. Biol. 25, 2368–2375 (2005).
GTEx Consortium Genetic effects on gene expression across human tissues. Nature 550, 204–213 (2017).
Hamosh, A., Scott, A. F., Amberger, J. S., Bocchini, C. A. & McKusick, V. A. Online Mendelian Inheritance in Man (OMIM), a knowledgebase of human genes and genetic disorders. Nucleic Acids Res. 33, D514–D517 (2005).
Lek, M. et al. Analysis of protein-coding genetic variation in 60,706 humans. Nature 536, 285–291 (2016).
Zeng, Y. et al. Aberrant gene expression in humans. PLOS Genet. 11, e1004942 (2015).
Zhao, J. et al. A burden of rare variants associated with extremes of gene expression in human peripheral blood. Am. J. Hum. Genet. 98, 299–309 (2016).
Pala, M. et al. Population- and individual-specific regulatory variation in Sardinia. Nat. Genet. 49, 700–707 (2017).
Cao, D. & Parker, R. Computational modeling and experimental analysis of nonsense-mediated decay in yeast. Cell 113, 533–545 (2003).
Lykke-Andersen, S. & Jensen, T. H. Nonsense-mediated mRNA decay: an intricate machinery that shapes transcriptomes. Nat. Rev. Mol. Cell Biol. 16, 665–677 (2015).
Nickless, A., Bailis, J. M. & You, Z. Control of gene expression through the nonsense-mediated RNA decay pathway. Cell Biosci. 7, 26 (2017).
Li, X. et al. The impact of rare variation on gene expression across tissues. Nature 550, 239–243 (2017).
Köhler, S. et al. The Human Phenotype Ontology in 2017. Nucleic Acids Res. 45, D865–D876 (2017).
Estivill, X. Genetic variation and alternative splicing. Nat. Biotechnol. 33, 357–359 (2015).
Xiong, H. Y. et al. The human splicing code reveals new insights into the genetic determinants of disease. Science 347, 1254806–1254806 (2015).
Walter, K. et al. The UK10K project identifies rare variants in health and disease. Nature 526, 82–90 (2015).
Soens, Z. T. et al. Leveraging splice-affecting variant predictors and a minigene validation system to identify Mendelian disease-causing variants among exon-captured variants of uncertain significance. Hum. Mutat. 38, 1521–1533 (2017).
Albers, C. A. et al. Compound inheritance of a low-frequency regulatory SNP and a rare null mutation in exon-junction complex subunit RBM8A causes TAR syndrome. Nat. Genet. 44, S1–S2 (2012).
Reinius, B. & Sandberg, R. Random monoallelic expression of autosomal genes: stochastic transcription and allele-level regulation. Nat. Rev. Genet. 16, 653–664 (2015).
Barbosa, M. et al. Identification of rare de novo epigenetic variations in congenital disorders. Nat. Commun. 9, 2064 (2018).
Avramidou, A. et al. The novel adaptor protein Swiprosin-1 enhances BCR signals and contributes to BCR-induced apoptosis. Cell Death Differ. 14, 1936–1947 (2007).
Kroczek, C. et al. Swiprosin-1/EFhd2 controls B cell receptor signaling through the assembly of the B cell receptor, Syk, and phospholipase C gamma2 in membrane rafts. J. Immunol. 184, 3665–3676 (2010).
Dütting, S., Brachs, S. & Mielenz, D. Fraternal twins: Swiprosin-1/EFhd2 and Swiprosin-2/EFhd1, two homologous EF-hand containing calcium binding adaptor proteins with distinct functions. Cell Commun. Signal. 9, 2 (2011).
Thylur, R. P., Gowda, R., Mishra, S. & Jun, C.-D. Swiprosin-1: its expression and diverse biological functions. J. Cell. Biochem. 119, 150–156 (2018).
Heimer, G. et al. MECR mutations cause childhood-onset dystonia and optic atrophy, a mitochondrial fatty acid synthesis disorder. Am. J. Hum. Genet. 99, 1229–1244 (2016).
Kircher, M. et al. A general framework for estimating the relative pathogenicity of human genetic variants. Nat. Genet. 46, 310–315 (2014).
Eldomery, M. K. et al. Lessons learned from additional research analyses of unsolved clinical exome cases. Genome Med. 9, 26 (2017).
Wright, C. F. et al. Making new genetic diagnoses with old data: iterative reanalysis and reporting from genome-wide data in 1,133 families with developmental disorders. Genet. Med. 20, 1216–1223 (2018).
Dewey, F. E. et al. Distribution and clinical impact of functional variants in 50,726 whole-exome sequences from the DiscovEHR study. Science 354, aaf6814 (2016).
Eilbeck, K., Quinlan, A. & Yandell, M. Settling the score: variant prioritization and Mendelian disease. Nat. Rev. Genet. 18, 599–612 (2017).
Rao, A. R. & Nelson, S. F. Calculating the statistical significance of rare variants causal for Mendelian and complex disorders. BMC Med. Genomics 11, 53 (2018).
Li, B. & Dewey, C. N. RSEM: accurate transcript quantification from RNA-seq data with or without a reference genome. BMC Bioinformatics 12, 323 (2011).
Tange, O. GNU Parallel - The Command-Line Power Tool. The USENIX Magazine 36, 42–47 (2011).
Li, H. & Durbin, R. Fast and accurate short read alignment with Burrows–Wheeler transform. Bioinformatics 25, 1754–1760 (2009).
DePristo, M. A. et al. A framework for variation discovery and genotyping using next-generation DNA sequencing data. Nat. Genet. 43, 491–498 (2011).
Ganna, A. et al. Quantifying the impact of rare and ultra-rare coding variation across the phenotypic spectrum. Am. J. Hum. Genet. 102, 1204–1211 (2018).
Quinlan, A. R. & Hall, I. M. BEDTools: a flexible suite of utilities for comparing genomic features. Bioinformatics 26, 841–842 (2010).
Pedersen, B. S., Layer, R. M. & Quinlan, A. R. Vcfanno: fast, flexible annotation of genetic variants. Genome Biol. 17, 118 (2016).
McLaren, W. et al. The Ensembl Variant Effect Predictor. Genome Biol. 17, 122 (2016).
Quang, D., Chen, Y. & Xie, X. DANN: a deep learning approach for annotating the pathogenicity of genetic variants. Bioinformatics 31, 761–763 (2015).
Davydov, E. V. et al. Identifying a high fraction of the human genome to be under selective constraint using GERP++. PLoS Comput. Biol. 6, e1001025 (2010).
Pollard, K. S., Hubisz, M. J., Rosenbloom, K. R. & Siepel, A. Detection of nonneutral substitution rates on mammalian phylogenies. Genome Res. 20, 110–121 (2010).
Siepel, A. Evolutionarily conserved elements in vertebrate, insect, worm, and yeast genomes. Genome Res. 15, 1034–1050 (2005).
Ernst, J. & Kellis, M. ChromHMM: automating chromatin-state discovery and characterization. Nat. Meth. 9, 215–216 (2012).
Stegle, O., Parts, L., Piipari, M., Winn, J. & Durbin, R. Using probabilistic estimation of expression residuals (PEER) to obtain increased power and interpretability of gene expression analyses. Nat. Protoc. 7, 500–507 (2012).
Castel, S. E., Levy-Moonshine, A., Mohammadi, P., Banks, E. & Lappalainen, T.. Tools and best practices for data processing in allelic expression analysis. Genome Biol. 16, 195 (2015).
McKenna, A. et al. The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res. 20, 1297–1303 (2010).
The authors would like to thank the patients and their families for their participation in this study. S.B.M. is supported by NIH grants nos. R01HG008150 (NoVa) and U01HG009080 (GSPAC) and the Glenn Center for Aging at Stanford. L.F. was supported by the Stanford Center for Computational, Evolutionary, and Human Genomics Fellowship. C.S. is supported by a BD2K Training Grant (T32 LM012409). N.M.F. is supported by a National Science Foundation Graduate Research Fellowship. N.A.T. is supported by the Stanford Genome Training Program (2T32HG000044-21). B.L. was supported by the Stanford Computational, Evolutionary, and Human Genomics Fellowship and the National Key R&D Program of China (2016YFD0400800). K.M.B. is supported by a CIHR Foundation grant (FDN-154279). Z.Z. was supported by the CEHG Fellowship, the National Science Foundation GRFP (DGE-114747) and the Stanford Genome Training Program (NIH/NHGRI T32HG000044). B.B. was supported by the Stanford Genome Training Program and Dean’s Postdoctoral Fellowship. J.R.D. was supported by a Lucille P. Markey Biomedical Research 688 Stanford Graduate Fellowship. J.R.D. acknowledges the Stanford Genome Training Program 689 (NIH/NHGRI T32HG000044). C.J.P. is supported by NIST/JIMB grant no. 70NANB15H268. A.B. is supported by NIH grant no. R01HG008150 (NoVa) and the Searle Scholar Fund. Clinical sample collection was supported, in part, by the Care4Rare Canada Consortium funded by Genome Canada, the Canadian Institutes of Health Research, the Ontario Genomics Institute, the Ontario Research Fund and the Children’s Hospital of Eastern Ontario Foundation. Research reported in this manuscript was in part supported by the NIH Common Fund, through the Office of Strategic Coordination/Office of the NIH Director under Award Number U01HG007708. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.
J.D.M. is on Genoox Scientific advisory board and Rainbow Genomics Clinical advisory board and consults for Illumina. E.A.A. is co-founder of Personalis, DeepCell and advisor to Genome Medical and Sequence Bio. E.I. is a scientific advisor for Precision Wellness for work unrelated to the present project. S.B.M. is on the scientific advisory board for Prime Genomics.
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We used a total of 1,061 whole blood samples from our controls cohorts and rare disease samples. a, Density plot representing the proportion of annotated junctions covered per gene. Those are a subset of genes for which at least one junction is covered with at least five uniquely mapped reads across at least 20% of the samples. On average (blue dashed line) 86%, (median of 100%—red dashed line) of junctions fulfil those criteria. b, Percentage of genes from disease genes panels in which at least one junction is covered with at least five uniquely mapped reads in at least 20% of samples. We observe that about 50% of genes from OMIM, Neurology, Musculoskeletal, Ophthalmology or Hematology panels are fulfilling this criteria. c, Tolerance to different types of mutations (from ExAC) in function of the expression status in a single versus multiple tissues (two-sided Wilcoxon test, Pvalue ≤ 2 × 10−16). Analysis performed on 620 individuals from GTEx v.7 across 22 tissues. Boxplots represent median value, with lower and upper hinges corresponding to the 25th and 75th percentiles, and lower and upper whiskers extend from the hinge to the smallest and largest value at most 1.5× interquartile range of the hinge, respectively. Genes that are expressed in multiple tissues tend to be more sensitive to missense and LoF mutations. d, Number of LoF intolerant genes stratified by expression level in blood. We considered genes with pLI score ≥ 0.9 as LoF intolerant.
Analyses performed on n = 909 DGN samples and 143 rare diseases (cases and family controls). a, Plot of first two principal components run on uncorrected gene expression data. Samples are coloured by batch. Largest cluster (green dots) are DGN control samples (n = 909). b, Plot of first two principal components run on gene expression data after regressing out significant surrogate variables found by SVA. c, Correlation between known covariates and all significant surrogate variables (SVs). We observed that SV2 is highly correlated with the read type, and the sequencing technology corresponding to differences between DGN and the other samples.
a, Normalized gene expression residuals from 1,052 samples in an example gene without correction (left panel), after regressing out significant surrogate variables (SVs) (middle panel) and significant SVs plus regression splines on top SVs significantly associated with batch and study (right panel). Residuals were plotted against SV2 for illustration purposes (SV2 is significantly associated with batch (P value < 1 × 10–30, two-sided t-test from linear regression, no adjustment for multiple correction). b, Mean number of outlier genes per sample (n = 990) in each batch (absolute Z-score > 8) after correction with SVs (left panel) and SVs with regression splines (right panel). Standard deviation is displayed above each bar. Regression splines resulted in a more consistent number of outlier genes across samples in all batches. c, Benjamini & Hochberg adjusted P values resulting from a per-gene likelihood ratio test comparing linear regression model fit both with and without regression splines. Regression splines improve the model fit for 2,644 genes (P value ≤ 0.05,17.6% of all genes in dataset). Red dashed line indicates P value = 0.05 cutoff. d, Change in R2, in decreasing order, across all genes in the dataset (n = 14,988) after correcting data using significant SVs with regression splines, compared to correcting data using significant SVs without regression splines. Mean change in R2 is 0.036 (s.d. = 0.025).
a, Enrichment of case (red, n = 64) under-expression outliers in LoF sensitive genes as we increase the number of controls (7,600 random subsets for each sample size indicated in legend). This enrichment was not observed for rare disease family member controls (gray, n = 34). b, Benjamini & Hochberg adjusted −log10 P value associated with the enrichment at different number of controls (two-sided t-test, n = 64 cases). Horizontal line indicates 0.05 significance cutoff. The P values are decreasing as we increase the number of controls. When switching cases for controls (gray) we observed significant negative log odds when using the a smaller number of controls, but this trend disappeared when using the full set of 900 controls. For a and b, Boxplots represent median value, with lower and upper hinges corresponding to the 25th and 75th percentiles, and lower and upper whiskers extend from the hinge to the smallest and largest value at most 1.5× interquartile range of the hinge, respectively.
Filters have various impacts on the number of samples with at least one candidate gene. By combining several layers of filters we are drastically reducing the number of candidate genes but also the number of samples for which we have candidates. We recommend to relax filter stringency after looking at sets of genes that match the most stringent criterion. a, Expression outliers. After filtering for outlier genes matching HPO terms, with a deleterious rare variant within 10 kb, we observed less than 2.6% of samples with over 25 candidate genes. b, Splicing outliers. When keeping only genes with HPO match, and a deleterious rare variant with 20 bp of the outlier junction, we observed less than 1.3% of samples with more than five candidate genes.
Analyses performed on 65 PIVUS samples and 143 rare disease samples. a, Plot of first two principal components (PCs) run on uncorrected splicing ratio data. Samples are coloured by batch. We observed that PC1 was separating PIVUS controls samples (left) from rare disease samples (right). b, Plot of first two PCs on splicing ratios after regressing out PCs explained up to 95% of the variance in the data. Batches were no longer separated on the first PCs. c, Correlation between known covariates 10 first PCs. We observed that PC1 is highly correlated with the batch, whereas PCs 2 and 3 separated samples from one institution (batch 1, CHEO) from others. We also observed that PC1 is highly correlated with RIN, highlighting differences in quality across samples.
a, Prevalence of ASE events in rare diseases samples (n = 112). Results are displayed separately for exome and genome sequencing. b, Difference in proportion of genes matching HPO terms for top 20 ASE outliers per case in comparison to random genes (100 random gene sets for each sample, n = 109 samples). Analysis performed for all genes, genes with pLI ≥ 0.9, genes with a rare variant (RV) and genes with a RV with CADD score ≥ 10. The top 20 ASE outlier genes are enriched for overlap with HPO-associated genes per case, regardless of the filters applied to the extreme ASE genes and background genes (**** P value ≤ 1 × 10−4, two-sided Wilcoxon test). For a and b, Boxplots represent median value, with lower and upper hinges corresponding to the 25th and 75th percentiles, and lower and upper whiskers extend from the hinge to the smallest and largest value at most 1.5× interquartile range of the hinge respectively. c, Rare deleterious variants are biased toward the alternative allele across all samples. A stop–gain variant was highly expressed in EFHD2 for one sample where there were matching symptoms.
a, Overview of cases. Solved: causal gene found and further validated. Strong candidate: Strong candidate after RNA-seq analysis (out of a subset of 30 affected individuals for which we have prior candidate genes information from literature). Unsolved: Other cases for which further investigation is needed. b, Percentage of cases for which prior candidate gene is in final set of filtered genes (outlier with deleterious rare variant in a gene linked to symptoms). Analysis was performed only on a subset of 30 cases for which we have prior candidate gene information and for which we have genetic information. Shuffling candidates corresponds to the percentage of cases for which we observe a prior candidate genes in the most stringent gene list when shuffling gene lists across individuals (10,000 permutations). On average, no match is found. Shuffling genes correspond to the percentage of prior candidate genes we observed within the final set of DNA-only filters when sampling from this list a matched number of genes corresponding to the expression filters. Average matched percentage is 4.1% after 10,000 permutations. Real data corresponds to the percentage of cases for which we found a candidate gene in the most stringent RNA-based filter set. We find a match for 7 affected samples out of 30, that is, 25.9 % of cases. There is significantly more match in real data in comparison to permuted data (two-sided Wilcoxon rank sum test, P value < 10–5). Boxplots represent median value, with lower and upper hinges corresponding to the 25th and 75th percentiles, and lower and upper whiskers extend from the hinge to the smallest and largest value at most 1.5× interquartile range of the hinge, respectively.
MECR case. a, Proband results. After our most stringent filter, there are 11 candidate genes left and MECR is rank 2nd by Z-score. b, Proband’s brother. After filtering, only 15 out of 1,099 expression outliers are left and MECR is ranked 10th in that list.
a, After filtering our detected splicing outliers for genes related to the phenotype (through HPO IDs), only eight genes were left, with ASAH1 being the most extreme outlier (Z-score = 3.9) and for which we previously confirmed the association with SMA-PME phenotype in the case. b, Sashimi plot of the case and 2 controls of the ASAH1 gene. For the case (red track), we observed an alternative transcript skipping exon 6 (supported by 142 reads). This pattern was never observed in controls.
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Frésard, L., Smail, C., Ferraro, N.M. et al. Identification of rare-disease genes using blood transcriptome sequencing and large control cohorts. Nat Med 25, 911–919 (2019). https://doi.org/10.1038/s41591-019-0457-8
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