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Cited by 321Genomics and Rare DiseasesGenomics and Phylogenetic StudiesConnective tissue disorders research

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

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Undiagnosed Diseases Network, Frésard, Laure, Care4Rare Canada Consortium, Smail, Craig, Ferraro, Nicole M., Teran, Nicole A., Li, Xin, Smith, Kevin S. +28 more·2019·Nature Medicine
DOI 10.1038/s41591-019-0457-8PMC PMC6634302
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Abstract

It is estimated that 350 million individuals worldwide suffer from rare diseases, which are predominantly caused by mutation in a single gene 1. The current molecular diagnostic rate is estimated at 50%, with whole-exome sequencing (WES) among the most successful approaches 2-5. For patients in whom WES is uninformative, RNA sequencing (RNA-seq) has shown diagnostic utility in specific tissues and diseases 6-8. This includes muscle biopsies from patients with undiagnosed rare muscle disorders 6,9, and cultured fibroblasts from patients with mitochondrial disorders 7. 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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  • Previous studies have reported that RNA-Seq helps interpret VUSs identified through WES/WGS when linked to an aberrant transcript event, and detects variants missed by current standard diagnostic approaches in rare diseases.

    “Previous studies have reported that RNA-Seq helps interpret VUSs identified through WES/WGS when linked to an aberrant transcript event, and detects variants missed by current standard diagnostic approaches in rare diseases [42,45,46].”

    From Identification of a Novel Non-Canonical Splice-Site Variant in ABCD1 (2023) · cites this paper as [45]

    The introductory paragraph and summary (section labeled NOTE; no page given) report that blood RNA-seq aided diagnosis after uninformative WES, identifying causal genes and variants in 7.5% of cases. The paper also describes combining exome or genome variants with RNA-seq splicing outliers to narrow candidates. Our copy does not establish that the variants interpreted in those analyses were specifically classified as VUSs, so that part of the claim is more specific than the evidence. (LLM, conf=0.82)

    Experimental — accuracy not yet measured

  • For patients in whom WES is uninformative, RNA-Seq of blood has been reported to show diagnostic utility in rare diseases by generating a 7.5% diagnostic rate and 16.7% improved candidate gene resolution.

    “For patients in whom WES is uninformative, RNA-Seq of blood has been reported to show diagnostic utility in rare diseases by generating a 7.5% diagnostic rate and 16.7% improved candidate gene resolution [45].”

    From Identification of a Novel Non-Canonical Splice-Site Variant in ABCD1 (2023) · cites this paper as [45]

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  1. [1]Amberger JS, Bocchini CA, Schiettecatte F, Scott AF & Hamosh A OMIM.org: Online Mendelian Inheritance in Man (OMIM®), an online catalog of human genes and genetic disorders. Nucleic Acids Research 43, D789– D798 (2015). 25428349 PMC4383985
  2. [2]Boycott KM International Cooperation to Enable the Diagnosis of All Rare Genetic Diseases. The American Journal of Human Genetics 100, 695– 705 (2017). 28475856 PMC5420351
  3. [3]Gilissen C, Hoischen A, Brunner HG & Veltman JA Unlocking Mendelian disease using exome sequencing. Genome Biology 12, 228 (2011). 21920049 PMC3308044
  4. [4]Yang Y Clinical Whole-Exome Sequencing for the Diagnosis of Mendelian Disorders. New England Journal of Medicine 369, 1502– 1511 (2013). 24088041 PMC4211433
  5. [5]Ewans LJ Whole-exome sequencing reanalysis at 12 months boosts diagnosis and is cost-effective when applied early in Mendelian disorders. Genet. Med (2018). doi: 10.1038/gim.2018.39 29595814 · DOI
  6. [6]Cummings BB Improving genetic diagnosis in Mendelian disease with transcriptome sequencing. Sci Transl Med 9, (2017). PMC5548421 28424332
  7. [7]Kremer LS Genetic diagnosis of Mendelian disorders via RNA sequencing. Nat Commun 8, 15824 (2017). 28604674 PMC5499207
  8. [8]Kernohan KD Whole-transcriptome sequencing in blood provides a diagnosis of spinal muscular atrophy with progressive myoclonic epilepsy. Hum. Mutat 38, 611– 614 (2017). 28251733 PMC5889109
  9. [9]Hamanaka K RNA sequencing solved the most common but unrecognized NEB pathogenic variant in Japanese nemaline myopathy. Genetics in Medicine (2018). doi: 10.1038/s41436-018-0360-6 30467404 · DOI
  10. [10]Battle A Characterizing the genetic basis of transcriptome diversity through RNA-sequencing of 922 individuals. Genome Research 24, 14– 24 (2014). 24092820 PMC3875855
  11. [11]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. Arteriosclerosis, Thrombosis, and Vascular Biology 25, 2368– 2375 (2005). 16141402
  12. [12]GTEx Consortium Genetic effects on gene expression across human tissues. Nature 550, 204– 213 (2017). 29022597 PMC5776756
  13. [13]Hamosh A, Scott AF, Amberger JS, Bocchini CA & McKusick VA Online Mendelian Inheritance in Man (OMIM), a knowledgebase of human genes and genetic disorders. Nucleic Acids Res. 33, D514– 517 (2005). 15608251 PMC539987
  14. [14]Lek M Analysis of protein-coding genetic variation in 60,706 humans. Nature 536, 285– 291 (2016). 27535533 PMC5018207
  15. [15]Zeng Y Aberrant Gene Expression in Humans. PLOS Genetics 11, e1004942 (2015). 25617623 PMC4305293
  16. [16]Zhao J A Burden of Rare Variants Associated with Extremes of Gene Expression in Human Peripheral Blood. The American Journal of Human Genetics 98, 299– 309 (2016). 26849112 PMC4746369
  17. [17]Pala M Population- and individual-specific regulatory variation in Sardinia. Nat. Genet 49, 700– 707 (2017). 28394350 PMC5411016
  18. [18]Cao D & Parker R Computational Modeling and Experimental Analysis of Nonsense-Mediated Decay in Yeast. Cell 113, 533– 545 (2003). 12757713
  19. [19]Lykke-Andersen S & Jensen TH Nonsense-mediated mRNA decay: an intricate machinery that shapes transcriptomes. Nature Reviews Molecular Cell Biology 16, 665– 677 (2015). 26397022
  20. [20]Nickless A, Bailis JM & You Z Control of gene expression through the nonsense-mediated RNA decay pathway. Cell & Bioscience 7, (2017). PMC5437625 28533900
  21. [21]Li X The impact of rare variation on gene expression across tissues. Nature 550, 239– 243 (2017). 29022581 PMC5877409
  22. [22]Köhler S The Human Phenotype Ontology in 2017. Nucleic Acids Research 45, D865– D876 (2017). 27899602 PMC5210535
  23. [23]Estivill X Genetic variation and alternative splicing. Nature Biotechnology 33, 357– 359 (2015). 25850059
  24. [24]Xiong HY The human splicing code reveals new insights into the genetic determinants of disease. Science 347, 1254806– 1254806 (2015). 25525159 PMC4362528
  25. [25]Walter K The UK10K project identifies rare variants in health and disease. Nature 526, 82– 90 (2015). 26367797 PMC4773891
  26. [26]Soens ZT Leveraging splice-affecting variant predictors and a minigene validation system to identify Mendelian disease-causing variants among exon-captured variants of uncertain significance. Human Mutation 38, 1521– 1533 (2017). 28714225 PMC5638688
  27. [27]Albers CA 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, 435– 439, S1–2 (2012). 22366785 PMC3428915
  28. [28]Reinius B & Sandberg R Random monoallelic expression of autosomal genes: stochastic transcription and allele-level regulation. Nat. Rev. Genet 16, 653– 664 (2015). 26442639
  29. [29]Barbosa M Identification of rare de novo epigenetic variations in congenital disorders. Nat Commun 9, 2064 (2018). 29802345 PMC5970273
  30. [30]Avramidou A The novel adaptor protein Swiprosin-1 enhances BCR signals and contributes to BCR-induced apoptosis. Cell Death Differ. 14, 1936– 1947 (2007). 17673920
  31. [31]Kroczek C 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). 20194721
  32. [32]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 Communication and Signaling 9, 2 (2011). 21244694 PMC3036668
  33. [33]Thylur RP, Gowda R, Mishra S & Jun C-D Swiprosin-1: Its Expression and Diverse Biological Functions. J. Cell. Biochem 119, 150– 156 (2018). 28590012
  34. [34]Heimer G MECR Mutations Cause Childhood-Onset Dystonia and Optic Atrophy, a Mitochondrial Fatty Acid Synthesis Disorder. The American Journal of Human Genetics 99, 1229– 1244 (2016). 27817865 PMC5142118
  35. [35]Kircher M A general framework for estimating the relative pathogenicity of human genetic variants. Nature Genetics 46, 310– 315 (2014). 24487276 PMC3992975
  36. [37]Wright CF Making new genetic diagnoses with old data: iterative reanalysis and reporting from genome-wide data in 1,133 families with developmental disorders. Genet. Med (2018). doi: 10.1038/gim.2017.246 PMC5912505 29323667 · DOI
  37. [38]Dewey FE Distribution and clinical impact of functional variants in 50,726 whole-exome sequences from the DiscovEHR study. Science 354, (2016). 28008009
  38. [39]Eilbeck K, Quinlan A & Yandell M Settling the score: variant prioritization and Mendelian disease. Nature Reviews Genetics 18, 599– 612 (2017). PMC5935497 28804138
  39. [40]Rao AR & Nelson SF Calculating the statistical significance of rare variants causal for Mendelian and complex disorders. BMC Med Genomics 11, 53 (2018). 29898714 PMC6001062
  40. [41]Li B & Dewey CN RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome. BMC Bioinformatics 12, 323 (2011). 21816040 PMC3163565
  41. [42]Tange O GNU Parallel - The Command-Line Power Tool,;login. The USENIX Magazine (2011).
  42. [43]Li H & Durbin R Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics 25, 1754– 1760 (2009). 19451168 PMC2705234
  43. [44]DePristo MA A framework for variation discovery and genotyping using next-generation DNA sequencing data. Nature Genetics 43, 491– 498 (2011). 21478889 PMC3083463
  44. [45]Ganna A Quantifying the Impact of Rare and Ultra-rare Coding Variation across the Phenotypic Spectrum. The American Journal of Human Genetics 102, 1204– 1211 (2018). 29861106 PMC5992130
  45. [47]Pedersen BS, Layer RM & Quinlan AR Vcfanno: fast, flexible annotation of genetic variants. Genome Biology 17, (2016). PMC4888505 27250555
  46. [48]McLaren W The Ensembl Variant Effect Predictor. Genome Biology 17, (2016). PMC4893825 27268795
  47. [49]Quang D, Chen Y & Xie X DANN: a deep learning approach for annotating the pathogenicity of genetic variants. Bioinformatics 31, 761– 763 (2015). 25338716 PMC4341060
  48. [51]Pollard KS, Hubisz MJ, Rosenbloom KR & Siepel A Detection of nonneutral substitution rates on mammalian phylogenies. Genome Research 20, 110– 121 (2010). 19858363 PMC2798823
  49. [53]Ernst J & Kellis M ChromHMM: automating chromatin-state discovery and characterization. Nature Methods 9, 215– 216 (2012). 22373907 PMC3577932
  50. [56]McKenna A The Genome Analysis Toolkit: A MapReduce framework for analyzing next-generation DNA sequencing data. Genome Research 20, 1297– 1303 (2010). 20644199 PMC2928508

Cited by

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Authors
Undiagnosed Diseases Network, Frésard, L., Care4Rare Canada Consortium, Smail, C., Ferraro, N. M., Teran, N. A., Li, X., Smith, K. S., Bonner, D., Kernohan, K. D., Marwaha, S., Zappala, Z., Balliu, B., Davis, J. R., Liu, B., Prybol, C. J., Kohler, J. N., Zastrow, D. B., Reuter, C. M., Fisk, D. G., Grove, M. E., Davidson, J. M., Hartley, T., Joshi, R., Strober, B. J., Utiramerur, S., Lind, L., Ingelsson, E., Battle, A., Bejerano, G., Bernstein, J. A., Ashley, E. A., Boycott, K. M., Merker, J. D., Wheeler, M. T., & Montgomery, S. B.
Affiliations
  • National Institutes of Health (US)
  • Stanford Medicine (US)
  • Stanford University (US)
  • Children's Hospital of Eastern Ontario (CA)
  • University of Ottawa (CA)
  • Johns Hopkins University (US)
  • Uppsala University (SE)
  • University of North Carolina at Chapel Hill (US)
Corresponding author
  • Frésard, Laure
  • Montgomery, Stephen B.
ORCID
Venue
Nature Medicine
Publisher
Springer Science and Business Media LLC
Published
2019-06
Volume
25
Issue
6
Pages
911-919
ISSN
  • 1078-8956 (ISSN-L)
  • 1546-170X
DOI
10.1038/s41591-019-0457-8
PMC
PMC6634302
OpenAlex
W2948477377
Cited by
321
References
6
Citations by year
2026: 41 · 2025: 35 · 2024: 43 · 2023: 42 · 2022: 52 · 2021: 45 · 2020: 46 · 2019: 14 · 2017: 1 · 2013: 1
FWCI
23.37
Type
journal article
Language
en
Open access
Licence
Locations
  • Nature Medicine (publishedVersion)
  • bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by)
  • PubMed (publishedVersion)
  • Europe PMC (PubMed Central) (acceptedVersion)
  • PubMed Central (acceptedVersion)
  • eScholarship (California Digital Library) (submittedVersion)
Funders
  • National Science Foundation
  • Genome Canada
  • Ontario Genomics
  • Ontario Genomics Institute
  • National Institutes of Health
  • National Institute of Standards and Technology (70NANB15H268)
  • Canadian Institutes of Health Research (154279_1)
  • National Human Genome Research Institute (U01HG010218, R01HG008150, U01HG007708, U01HG009080, T32HG000044)
  • NIH Office of the Director (U01HG007708)
  • U.S. National Library of Medicine (T32LM012409, T15LM007033)
  • Office of Strategic Coordination (U01HG007708)
  • Common Fund (U01HG007708)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genomics and Rare Diseases
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Connective tissue disorders research
Keywords
rare diseases, blood transcriptome, RNA-seq, disease gene identification, undiagnosed patients, splicing, variant filtering, gene expression, diagnostic rate
MeSH terms
Exome Sequencing, Child, Child, Preschool, Female, Humans, Male, Models, Genetic, Mutation, RNA, RNA Splicing, Genetic Variation, Potassium Channels, Cohort Studies, Case-Control Studies, Sequence Analysis, RNA, Rare Diseases, Oxidoreductases Acting on CH-CH Group Donors, Acid Ceramidase

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