scholarly journals Transcriptomic signatures across human tissues identify functional rare genetic variation

Science ◽  
2020 ◽  
Vol 369 (6509) ◽  
pp. eaaz5900 ◽  
Author(s):  
Nicole M. Ferraro ◽  
Benjamin J. Strober ◽  
Jonah Einson ◽  
Nathan S. Abell ◽  
Francois Aguet ◽  
...  
Keyword(s):  
Gene Expression ◽  
Rare Variants ◽  
Sequencing Data ◽  
Specific Expression ◽  
Aberrant Gene Expression ◽  
Heart Study ◽  
Rare Genetic Variation ◽  
Allele Specific ◽  
The Uk ◽  
Aberrant Gene

Rare genetic variants are abundant across the human genome, and identifying their function and phenotypic impact is a major challenge. Measuring aberrant gene expression has aided in identifying functional, large-effect rare variants (RVs). Here, we expanded detection of genetically driven transcriptome abnormalities by analyzing gene expression, allele-specific expression, and alternative splicing from multitissue RNA-sequencing data, and demonstrate that each signal informs unique classes of RVs. We developed Watershed, a probabilistic model that integrates multiple genomic and transcriptomic signals to predict variant function, validated these predictions in additional cohorts and through experimental assays, and used them to assess RVs in the UK Biobank, the Million Veterans Program, and the Jackson Heart Study. Our results link thousands of RVs to diverse molecular effects and provide evidence to associate RVs affecting the transcriptome with human traits.

scholarly journals Diverse transcriptomic signatures across human tissues identify functional rare genetic variation

2019 ◽  
Author(s):  
Nicole M. Ferraro ◽  
Benjamin J. Strober ◽  
Jonah Einson ◽  
Xin Li ◽  
Francois Aguet ◽  
...  
Keyword(s):  
Gene Expression ◽  
Genetic Variation ◽  
Rare Variants ◽  
Sequencing Data ◽  
Specific Expression ◽  
Aberrant Gene Expression ◽  
Rare Genetic Variation ◽  
Allele Specific ◽  
The Uk ◽  
Aberrant Gene

AbstractRare genetic variation is abundant in the human genome, yet identifying functional rare variants and their impact on traits remains challenging. Measuring aberrant gene expression has aided in identifying functional, large-effect rare variants. Here, we expand detection of genetically driven transcriptome abnormalities by evaluating and integrating gene expression, allele-specific expression, and alternative splicing from multi-tissue RNA-sequencing data. We demonstrate that each signal informs unique classes of rare variants. We further develop Watershed, a probabilistic model that integrates multiple genomic and transcriptomic signals to predict variant function. Assessing rare variants prioritized by Watershed in the UK Biobank and Million Veterans Program, we identify large effects across 34 traits, and 33 rare variant-trait combinations with both high Watershed scores and large trait effect sizes. Together, we provide a comprehensive analysis of the transcriptomic impact of rare variation and a framework to prioritize functional rare variants and assess their trait relevance.One-sentence summaryIntegrating expression, allelic expression and splicing across tissues identifies rare variants with relevance to traits.

scholarly journals OUTRIDER: A statistical method for detecting aberrantly expressed genes in RNA sequencing data

2018 ◽  
Author(s):  
Felix Brechtmann ◽  
Agnė Matusevičiūtė ◽  
Christian Mertes ◽  
Vicente A Yépez ◽  
Žiga Avsec ◽  
...  
Keyword(s):  
Gene Expression ◽  
Rna Sequencing ◽  
Negative Binomial ◽  
Statistical Significance ◽  
P Value ◽  
Rna Seq ◽  
Sequencing Data ◽  
Data Set ◽  
Aberrant Gene Expression ◽  
Aberrant Gene

AbstractRNA sequencing (RNA-seq) is gaining popularity as a complementary assay to genome sequencing for precisely identifying the molecular causes of rare disorders. A powerful approach is to identify aberrant gene expression levels as potential pathogenic events. However, existing methods for detecting aberrant read counts in RNA-seq data either lack assessments of statistical significance, so that establishing cutoffs is arbitrary, or rely on subjective manual corrections for confounders. Here, we describe OUTRIDER (OUTlier in RNA-seq fInDER), an algorithm developed to address these issues. The algorithm uses an autoencoder to model read count expectations according to the co-variation among genes resulting from technical, environmental, or common genetic variations. Given these expectations, the RNA-seq read counts are assumed to follow a negative binomial distribution with a gene-specific dispersion. Outliers are then identified as read counts that significantly deviate from this distribution. The model is automatically fitted to achieve the best correction of artificially corrupted data. Precision–recall analyses using simulated outlier read counts demonstrated the importance of combining correction for co-variation and significance-based thresholds. OUTRIDER is open source and includes functions for filtering out genes not expressed in a data set, for identifying outlier samples with too many aberrantly expressed genes, and for the P-value-based detection of aberrant gene expression, with false discovery rate adjustment. Overall, OUTRIDER provides a computationally fast and scalable end-to-end solution for identifying aberrantly expressed genes, suitable for use by rare disease diagnostic platforms.

scholarly journals Detection of aberrant gene expression events in RNA sequencing data

2021 ◽  
Vol 16 (2) ◽  
pp. 1276-1296
Author(s):  
Vicente A. Yépez ◽  
Christian Mertes ◽  
Michaela F. Müller ◽  
Daniela Klaproth-Andrade ◽  
Leonhard Wachutka ◽  
...  
Keyword(s):  
Gene Expression ◽  
Rna Sequencing ◽  
Sequencing Data ◽  
Aberrant Gene Expression ◽  
Aberrant Gene

scholarly journals Publisher Correction to: C11orf95-RELA fusion drives aberrant gene expression through the unique epigenetic regulation for ependymoma formation

2021 ◽  
Vol 9 (1) ◽  
Author(s):  
Tatsuya Ozawa ◽  
Syuzo Kaneko ◽  
Frank Szulzewsky ◽  
Zhiwei Qiao ◽  
Mutsumi Takadera ◽  
...  
Keyword(s):  
Gene Expression ◽  
Epigenetic Regulation ◽  
Aberrant Gene Expression ◽  
Aberrant Gene

An amendment to this paper has been published and can be accessed via the original article.

scholarly journals Transcriptome analysis identified aberrant gene expression in pollen developmental pathways leading to CGMS in cotton (Gossypium hirsutum L.)

PLoS ONE ◽  
2019 ◽  
Vol 14 (6) ◽  
pp. e0218381 ◽  
Author(s):  
Rasmieh Hamid ◽  
Hassan Marashi ◽  
Rukam S. Tomar ◽  
Saeid Malekzadeh Shafaroudi ◽  
Pritesh H. Sabara
Keyword(s):  
Gene Expression ◽  
Gossypium Hirsutum ◽  
Transcriptome Analysis ◽  
Developmental Pathways ◽  
Gossypium Hirsutum L ◽  
Aberrant Gene Expression ◽  
Aberrant Gene

scholarly journals Behavior-dependent cis regulation reveals genes and pathways associated with bower building in cichlid fishes

2018 ◽  
Vol 115 (47) ◽  
pp. E11081-E11090 ◽  
Author(s):  
Ryan A. York ◽  
Chinar Patil ◽  
Kawther Abdilleh ◽  
Zachary V. Johnson ◽  
Matthew A. Conte ◽  
...  
Keyword(s):  
Gene Expression ◽  
Neural Plasticity ◽  
Genetic Basis ◽  
Specific Expression ◽  
Preferential Expression ◽  
Cichlid Fishes ◽  
Brain Gene Expression ◽  
Genome Wide ◽  
Allele Specific ◽  
Genomic Regions

Many behaviors are associated with heritable genetic variation [Kendler and Greenspan (2006) Am J Psychiatry 163:1683–1694]. Genetic mapping has revealed genomic regions or, in a few cases, specific genes explaining part of this variation [Bendesky and Bargmann (2011) Nat Rev Gen 12:809–820]. However, the genetic basis of behavioral evolution remains unclear. Here we investigate the evolution of an innate extended phenotype, bower building, among cichlid fishes of Lake Malawi. Males build bowers of two types, pits or castles, to attract females for mating. We performed comparative genome-wide analyses of 20 bower-building species and found that these phenotypes have evolved multiple times with thousands of genetic variants strongly associated with this behavior, suggesting a polygenic architecture. Remarkably, F1 hybrids of a pit-digging and a castle-building species perform sequential construction of first a pit and then a castle bower. Analysis of brain gene expression in these hybrids showed that genes near behavior-associated variants display behavior-dependent allele-specific expression with preferential expression of the pit-digging species allele during pit digging and of the castle-building species allele during castle building. These genes are highly enriched for functions related to neurodevelopment and neural plasticity. Our results suggest that natural behaviors are associated with complex genetic architectures that alter behavior via cis-regulatory differences whose effects on gene expression are specific to the behavior itself.

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