%0 Journal Article %J Nat Med %D 2019 %T Identification of rare-disease genes using blood transcriptome sequencing and large control cohorts. %A Frésard, Laure %A Smail, Craig %A Ferraro, Nicole M %A Teran, Nicole A %A Li, Xin %A Smith, Kevin S %A Bonner, Devon %A Kernohan, Kristin D %A Marwaha, Shruti %A Zappala, Zachary %A Balliu, Brunilda %A Davis, Joe R %A Liu, Boxiang %A Prybol, Cameron J %A Kohler, Jennefer N %A Zastrow, Diane B %A Reuter, Chloe M %A Fisk, Dianna G %A Grove, Megan E %A Davidson, Jean M %A Hartley, Taila %A Joshi, Ruchi %A Strober, Benjamin J %A Utiramerur, Sowmithri %A Lind, Lars %A Ingelsson, Erik %A Battle, Alexis %A Bejerano, Gill %A Bernstein, Jonathan A %A Ashley, Euan A %A Boycott, Kym M %A Merker, Jason D %A Wheeler, Matthew T %A Montgomery, Stephen B %K Acid Ceramidase %K Case-Control Studies %K Child %K Child, Preschool %K Cohort Studies %K Female %K Genetic Variation %K Humans %K Male %K Models, Genetic %K Mutation %K Oxidoreductases Acting on CH-CH Group Donors %K Potassium Channels %K Rare Diseases %K RNA %K RNA Splicing %K Sequence Analysis, RNA %K Whole Exome Sequencing %X

It is estimated that 350 million individuals worldwide suffer from rare diseases, which are predominantly caused by mutation in a single gene. The current molecular diagnostic rate is estimated at 50%, with whole-exome sequencing (WES) among the most successful approaches. For patients in whom WES is uninformative, RNA sequencing (RNA-seq) has shown diagnostic utility in specific tissues and diseases. This includes muscle biopsies from patients with undiagnosed rare muscle disorders, and cultured fibroblasts from patients with mitochondrial disorders. 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.

%B Nat Med %V 25 %P 911-919 %8 2019 06 %G eng %N 6 %1 https://www.ncbi.nlm.nih.gov/pubmed/31160820?dopt=Abstract %R 10.1038/s41591-019-0457-8 %0 Journal Article %J Nature %D 2017 %T The impact of rare variation on gene expression across tissues. %A Li, Xin %A Kim, Yungil %A Tsang, Emily K %A Davis, Joe R %A Damani, Farhan N %A Chiang, Colby %A Hess, Gaelen T %A Zappala, Zachary %A Strober, Benjamin J %A Scott, Alexandra J %A Li, Amy %A Ganna, Andrea %A Bassik, Michael C %A Merker, Jason D %A Hall, Ira M %A Battle, Alexis %A Montgomery, Stephen B %K Bayes Theorem %K Female %K Gene Expression Profiling %K Genetic Variation %K Genome, Human %K Genomics %K Genotype %K Humans %K Male %K Models, Genetic %K Organ Specificity %K Sequence Analysis, RNA %X

Rare genetic variants are abundant in humans and are expected to contribute to individual disease risk. While genetic association studies have successfully identified common genetic variants associated with susceptibility, these studies are not practical for identifying rare variants. Efforts to distinguish pathogenic variants from benign rare variants have leveraged the genetic code to identify deleterious protein-coding alleles, but no analogous code exists for non-coding variants. Therefore, ascertaining which rare variants have phenotypic effects remains a major challenge. Rare non-coding variants have been associated with extreme gene expression in studies using single tissues, but their effects across tissues are unknown. Here we identify gene expression outliers, or individuals showing extreme expression levels for a particular gene, across 44 human tissues by using combined analyses of whole genomes and multi-tissue RNA-sequencing data from the Genotype-Tissue Expression (GTEx) project v6p release. We find that 58% of underexpression and 28% of overexpression outliers have nearby conserved rare variants compared to 8% of non-outliers. Additionally, we developed RIVER (RNA-informed variant effect on regulation), a Bayesian statistical model that incorporates expression data to predict a regulatory effect for rare variants with higher accuracy than models using genomic annotations alone. Overall, we demonstrate that rare variants contribute to large gene expression changes across tissues and provide an integrative method for interpretation of rare variants in individual genomes.

%B Nature %V 550 %P 239-243 %8 2017 10 11 %G eng %N 7675 %1 https://www.ncbi.nlm.nih.gov/pubmed/29022581?dopt=Abstract %R 10.1038/nature24267