Diverse transcriptomic signatures across human tissues identify functional rare genetic variation

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.

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Transcriptomic signatures across human tissues identify functional rare genetic variation

Science ◽

10.1126/science.aaz5900 ◽

2020 ◽

Vol 369 (6509) ◽

pp. eaaz5900 ◽

Cited By ~ 6

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.

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Interactions between genetic variation and cellular environment in skeletal muscle gene expression

10.1101/105429 ◽

2017 ◽

Author(s):

D. Leland Taylor ◽

David A. Knowles ◽

Laura J. Scott ◽

Andrea H. Ramirez ◽

Franceso Paolo Casale ◽

...

Keyword(s):

Gene Expression ◽

Skeletal Muscle ◽

Genetic Variation ◽

Low Density Lipoprotein ◽

Density Lipoprotein ◽

Specific Expression ◽

Muscle Gene Expression ◽

Cellular Environment ◽

Allele Specific ◽

Muscle Gene

AbstractFrom whole organisms to individual cells, responses to environmental conditions are influenced by genetic makeup, where the effect of genetic variation on a trait depends on the environmental context. RNA-sequencing quantifies gene expression as a molecular trait, and is capable of capturing both genetic and environmental effects. In this study, we explore opportunities of using allele-specific expression (ASE) to discovercisacting genotype-environment interactions (GxE) - genetic effects on gene expression that depend on an environmental condition. Treating 17 common, clinical traits as approximations of the cellular environment of 267 skeletal muscle biopsies, we identify 10 candidate interaction quantitative trait loci (iQTLs) across 6 traits (12 unique gene-environment trait pairs; 10% FDR per trait) including sex, systolic blood pressure, and low-density lipoprotein cholesterol. Although using ASE is in principle a promising approach to detect GxE effects, replication of such signals can be challenging as validation requires harmonization of environmental traits across cohorts and a sufficient sampling of heterozygotes for a transcribed SNP. Comprehensive discovery and replication will require large human transcriptome datasets, or the integration of multiple transcribed SNPs, coupled with standardized clinical phenotyping.

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

10.1101/322149 ◽

2018 ◽

Cited By ~ 2

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.

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Passive and active DNA methylation and the interplay with genetic variation in gene regulation

eLife ◽

10.7554/elife.00523 ◽

2013 ◽

Vol 2 ◽

Cited By ~ 211

Author(s):

Maria Gutierrez-Arcelus ◽

Tuuli Lappalainen ◽

Stephen B Montgomery ◽

Alfonso Buil ◽

Halit Ongen ◽

...

Keyword(s):

Gene Expression ◽

Dna Methylation ◽

Gene Regulation ◽

Genetic Variation ◽

Genomic Sequence ◽

Expression Patterns ◽

Active Role ◽

Epigenetic Mark ◽

Sequencing Data ◽

Specific Expression

DNA methylation is an essential epigenetic mark whose role in gene regulation and its dependency on genomic sequence and environment are not fully understood. In this study we provide novel insights into the mechanistic relationships between genetic variation, DNA methylation and transcriptome sequencing data in three different cell-types of the GenCord human population cohort. We find that the association between DNA methylation and gene expression variation among individuals are likely due to different mechanisms from those establishing methylation-expression patterns during differentiation. Furthermore, cell-type differential DNA methylation may delineate a platform in which local inter-individual changes may respond to or act in gene regulation. We show that unlike genetic regulatory variation, DNA methylation alone does not significantly drive allele specific expression. Finally, inferred mechanistic relationships using genetic variation as well as correlations with TF abundance reveal both a passive and active role of DNA methylation to regulatory interactions influencing gene expression.

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