© 2020 American Association for the Advancement of Science. All rights reserved. INTRODUCTION: The human genome contains tens of thousands of rare (minor allele frequency 800 genomes matched with transcriptomes across 49 tissues, we were able to study RVs that underlie extreme changes in the transcriptome. To capture the diversity of these extreme changes, we developed and integrated approaches to identify expression, allele-specific expression, and alternative splicing outliers, and characterized the RV landscape underlying each outlier signal. We demonstrate that personal genome interpretation and RV discovery is enhanced by using these signals. This approach provides a new means to integrate a richer set of functional RVs into models of genetic burden, improve disease gene identification, and enable the delivery of precision genomics

Abell, Nathan S.

Aguet, Francois

Aradhana,

Ardlie, Kristin G.

Assimes, Themistocles L.

Barbeira, Alvaro N.

Bassik, Michael C.

Battle, Alexis

Brandt, Margot

Brown, Christopher D.

Bucan, Maja

Castel, Stephane E.

Correa, Adolfo

Davis, Joe R.

Einson, Jonah

Ferraro, Nicole M.

Greenwald, Emily

Hall, Ira

Hess, Gaelen T.

Hilliard, Austin T.

Im, Hae Kyung

Kember, Rachel L.

Kotis, Bence

Lappalainen, Tuuli

Li, Xin

Mohammadi, Pejman

Montgomery, Stephen B.

Natarajan, Pradeep

Park, Yo Son

Peloso, Gina

Ramdas, Shweta

Scott, Alexandra J.

Smail, Craig

Strober, Benjamin J.

Tsang, Emily K.

Zekavat, Seyedeh M.

Ziosi, Marcello

Science

English

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.</p

Aradhana

University of Dundee Online Publications

Transcriptomic signatures across human tissues identify functional rare genetic variation

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

Ferraro, Nicole M

Strober, Benjamin J

Einson, Jonah

Abell, Nathan S

Aguet, Francois

Barbeira, Alvaro N

Brandt, Margot

Bucan, Maja

Castel, Stephane E

Davis, Joe R

Greenwald, Emily

Hess, Gaelen T

Hilliard, Austin T

Kember, Rachel L

Kotis, Bence

Park, YoSon

Peloso, Gina

Ramdas, Shweta

Scott, Alexandra J

Smail, Craig

Tsang, Emily K

Zekavat, Seyedeh M

Ziosi, Marcello

Aradhana, 

Ardlie, Kristin G

Assimes, Themistocles L

Bassik, Michael C

Brown, Christopher D

Correa, Adolfo

Hall, Ira

Im, Hae Kyung

Li, Xin

Natarajan, Pradeep

Lappalainen, Tuuli

Mohammadi, Pejman

Montgomery, Stephen B

Battle, Alexis

GTEx Consortium

Mash, Deborah

University of Miami: Scholarship Miami

INTRODUCTION
The human genome contains tens of thousands of rare (minor allele frequency <1%) variants, some of which contribute to disease risk. Using 838 samples with whole-genome and multitissue transcriptome sequencing data in the Genotype-Tissue Expression (GTEx) project version 8, we assessed how rare genetic variants contribute to extreme patterns in gene expression (eOutliers), allelic expression (aseOutliers), and alternative splicing (sOutliers). We integrated these three signals across 49 tissues with genomic annotations to prioritize high-impact rare variants (RVs) that associate with human traits.

RATIONALE
Outlier gene expression aids in identifying functional RVs. Transcriptome sequencing provides diverse measurements beyond gene expression, including allele-specific expression and alternative splicing, which can provide additional insight into RV functional effects.

RESULTS
After identifying multitissue eOutliers, aseOutliers, and sOutliers, we found that outlier individuals of each type were significantly more likely to carry an RV near the corresponding gene. Among eOutliers, we observed strong enrichment of rare structural variants. sOutliers were particularly enriched for RVs that disrupted or created a splicing consensus sequence. aseOutliers provided the strongest enrichment signal when evaluated from just a single tissue.

We developed Watershed, a probabilistic model for personal genome interpretation that improves over standard genomic annotation–based methods for scoring RVs by integrating these three transcriptomic signals from the same individual and replicates in an independent cohort.

To assess whether outlier RVs identified in GTEx associate with traits, we evaluated these variants for association with diverse traits in the UK Biobank, the Million Veterans Program, and the Jackson Heart Study. We found that transcriptome-assisted prioritization identified RVs with larger trait effect sizes and were better predictors of effect size than genomic annotation alone.

CONCLUSION
With >800 genomes matched with transcriptomes across 49 tissues, we were able to study RVs that underlie extreme changes in the transcriptome. To capture the diversity of these extreme changes, we developed and integrated approaches to identify expression, allele-specific expression, and alternative splicing outliers, and characterized the RV landscape underlying each outlier signal. We demonstrate that personal genome interpretation and RV discovery is enhanced by using these signals. This approach provides a new means to integrate a richer set of functional RVs into models of genetic burden, improve disease gene identification, and enable the delivery of precision genomics

Transcriptomic signatures across human tissues identify functional rare genetic variation

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University of Dundee Online Publications

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University of Dundee Online Publications