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

被引:211
作者
Fresard, Laure [1 ]
Smail, Craig [2 ]
Ferraro, Nicole M. [2 ]
Teran, Nicole A. [3 ]
Li, Xin [1 ]
Smith, Kevin S. [1 ,20 ]
Bonner, Devon [4 ,20 ]
Kernohan, Kristin D. [5 ]
Marwaha, Shruti [4 ,6 ,20 ]
Zappala, Zachary [3 ]
Balliu, Brunilda [1 ]
Davis, Joe R. [3 ]
Liu, Boxiang [7 ]
Prybol, Cameron J. [3 ]
Kohler, Jennefer N. [4 ,20 ]
Zastrow, Diane B. [4 ,20 ]
Reuter, Chloe M. [4 ,20 ]
Fisk, Dianna G. [8 ]
Grove, Megan E. [8 ]
Davidson, Jean M. [4 ,20 ]
Hartley, Taila [9 ]
Joshi, Ruchi [8 ]
Strober, Benjamin J. [10 ]
Utiramerur, Sowmithri [8 ]
Lind, Lars [11 ]
Ingelsson, Erik [6 ,12 ]
Battle, Alexis [10 ,13 ]
Bejerano, Gill [14 ,15 ,16 ,17 ]
Bernstein, Jonathan A. [15 ,20 ]
Ashley, Euan A. [3 ,4 ,12 ,20 ]
Boycott, Kym M. [9 ]
Merker, Jason D. [1 ,8 ,18 ,19 ,20 ]
Wheeler, Matthew T. [4 ,6 ,20 ]
Montgomery, Stephen B. [1 ,3 ]
Adams, David R. [20 ]
Aday, Aaron [20 ]
Alejandro, Mercedes E. [20 ]
Allard, Patrick [20 ]
Ashley, Euan A. [3 ,4 ,12 ,20 ]
Azamian, Mahshid S. [20 ]
Bacino, Carlos A. [20 ]
Baker, Eva [20 ]
Balasubramanyam, Ashok [20 ]
Barseghyan, Hayk [20 ]
Batzli, Gabriel F. [20 ]
Beggs, Alan H. [20 ]
Behnam, Babak [20 ]
Bellen, Hugo J. [20 ]
Bernstein, Jonathan A. [15 ,20 ]
Berry, Gerard T. [20 ]
机构
[1] Stanford Univ, Sch Med, Dept Pathol, Stanford, CA 94305 USA
[2] Stanford Univ, Biomed Informat Program, Stanford, CA 94305 USA
[3] Stanford Univ, Sch Med, Dept Genet, Stanford, CA 94305 USA
[4] Stanford Univ, Stanford Ctr Undiagnosed Dis, Stanford, CA USA
[5] Childrens Hosp Eastern Ontario, NSO, Ottawa, ON, Canada
[6] Stanford Univ, Stanford Cardiovasc Inst, Sch Med, Stanford, CA 94305 USA
[7] Stanford Univ, Sch Humanities & Sci, Dept Biol, Stanford, CA 94305 USA
[8] Stanford Univ, Sch Med, Stanford Med Clin Genom Program, Stanford, CA 94305 USA
[9] Univ Ottawa, Childrens Hosp Eastern Ontario, Res Inst, Ottawa, ON, Canada
[10] Johns Hopkins Univ, Dept Biomed Engn, Baltimore, MD USA
[11] Uppsala Univ, Cardiovasc Epidemiol, Dept Med Sci, Uppsala, Sweden
[12] Stanford Univ, Sch Med, Div Cardiovasc Med, Dept Med, Stanford, CA 94305 USA
[13] Johns Hopkins Univ, Dept Comp Sci, Baltimore, MD 21218 USA
[14] Stanford Univ, Dept Comp Sci, Stanford, CA 94305 USA
[15] Stanford Univ, Sch Med, Dept Pediat, Stanford, CA 94305 USA
[16] Stanford Univ, Sch Med, Dept Dev Biol, Stanford, CA 94305 USA
[17] Stanford Univ, Sch Med, Dept Biomed Data Sci, Stanford, CA 94305 USA
[18] Univ N Carolina, Sch Med, Lineberger Comprehens Canc Ctr, Dept Pathol & Lab Med, Chapel Hill, NC 27515 USA
[19] Univ N Carolina, Sch Med, Lineberger Comprehens Canc Ctr, Dept Genet, Chapel Hill, NC 27515 USA
[20] NIH, Undiagnosed Dis Network, Bldg 10, Bethesda, MD 20892 USA
[21] McGill Univ, Montreal, PQ, Canada
[22] Univ Toronto, Toronto, ON, Canada
基金
加拿大健康研究院; 国家重点研发计划; 美国国家科学基金会;
关键词
ONLINE MENDELIAN INHERITANCE; EXPRESSION; DIAGNOSIS; FRAMEWORK; VARIANTS; PATHOGENICITY; SWIPROSIN-1; DISCOVERY; ATROPHY; DECAY;
D O I
10.1038/s41591-019-0457-8
中图分类号
Q5 [生物化学]; Q7 [分子生物学];
学科分类号
071010 ; 081704 ;
摘要
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.
引用
收藏
页码:911 / +
页数:24
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