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Transcript expression-aware annotation improves rare variant discovery and interpretation

View ORCID ProfileBeryl B. Cummings, View ORCID ProfileKonrad J. Karczewski, Jack A. Kosmicki, Eleanor G. Seaby, Nicholas A. Watts, Moriel Singer-Berk, Jonathan M. Mudge, Juha Karjalainen, F. Kyle Satterstrom, Anne O’Donnell-Luria, Timothy Poterba, Cotton Seed, Matthew Solomonson, Jessica Alföldi, The Genome Aggregation Database Production Team, The Genome Aggregation Database Consortium, Mark J. Daly, Daniel G. MacArthur
doi: https://doi.org/10.1101/554444
Beryl B. Cummings
1Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA. USA.
2Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
3Program in Biological and Biomedical Sciences, Harvard Medical School, Boston, Massachusetts, USA.
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  • ORCID record for Beryl B. Cummings
Konrad J. Karczewski
1Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA. USA.
2Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
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  • ORCID record for Konrad J. Karczewski
Jack A. Kosmicki
1Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA. USA.
2Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
4Program in Bioinformatics and Integrative Genomics, Harvard Medical School, Boston, MA 02115, USA
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Eleanor G. Seaby
1Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA. USA.
2Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
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Nicholas A. Watts
1Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA. USA.
2Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
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Moriel Singer-Berk
2Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
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Jonathan M. Mudge
6European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, CB101SD, UK
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Juha Karjalainen
1Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA. USA.
2Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
5Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
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F. Kyle Satterstrom
1Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA. USA.
2Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
5Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
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Anne O’Donnell-Luria
1Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA. USA.
2Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
7Division of Genetics and Genomics, Boston Children’s Hospital, Boston, MA 02115, USA
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Timothy Poterba
1Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA. USA.
2Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
5Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
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Cotton Seed
1Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA. USA.
2Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
5Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
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Matthew Solomonson
1Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA. USA.
2Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
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Jessica Alföldi
1Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA. USA.
2Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
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Mark J. Daly
1Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA. USA.
2Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
5Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
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Daniel G. MacArthur
1Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA. USA.
2Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
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Abstract

The acceleration of DNA sequencing in patients and population samples has resulted in unprecedented catalogues of human genetic variation, but the interpretation of rare genetic variants discovered using such technologies remains extremely challenging. A striking example of this challenge is the existence of disruptive variants in dosage-sensitive disease genes, even in apparently healthy individuals. Through manual curation of putative loss of function (pLoF) variants in haploinsufficient disease genes in the Genome Aggregation Database (gnomAD)1, we show that one explanation for this paradox involves alternative mRNA splicing, which allows exons of a gene to be expressed at varying levels across cell types. Currently, no existing annotation tool systematically incorporates this exon expression information into variant interpretation. Here, we develop a transcript-level annotation metric, the proportion expressed across transcripts (pext), which summarizes isoform quantifications for variants. We calculate this metric using 11,706 tissue samples from the Genotype Tissue Expression project2 (GTEx) and show that it clearly differentiates between weakly and highly evolutionarily conserved exons, a proxy for functional importance. We demonstrate that expression-based annotation selectively filters 22.4% of falsely annotated pLoF variants found in haploinsufficient disease genes in gnomAD, while removing less than 4% of high-confidence pathogenic variants in the same genes. Finally, we apply our expression filter to the analysis of de novo variants in patients with autism spectrum disorder (ASD) and developmental disorders and intellectual disability (DD/ID) to show that pLoF variants in weakly expressed regions have effect sizes similar to those of synonymous variants, while pLoF variants in highly expressed exons are most strongly enriched among cases versus controls. Our annotation is fast, flexible, and generalizable, making it possible for any variant file to be annotated with any isoform expression dataset, and will be valuable for rare disease diagnosis, rare variant burden analyses in complex disorders, and curation and prioritization of variants in recall-by-genotype studies.

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Posted February 19, 2019.
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Transcript expression-aware annotation improves rare variant discovery and interpretation
Beryl B. Cummings, Konrad J. Karczewski, Jack A. Kosmicki, Eleanor G. Seaby, Nicholas A. Watts, Moriel Singer-Berk, Jonathan M. Mudge, Juha Karjalainen, F. Kyle Satterstrom, Anne O’Donnell-Luria, Timothy Poterba, Cotton Seed, Matthew Solomonson, Jessica Alföldi, The Genome Aggregation Database Production Team, The Genome Aggregation Database Consortium, Mark J. Daly, Daniel G. MacArthur
bioRxiv 554444; doi: https://doi.org/10.1101/554444
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Transcript expression-aware annotation improves rare variant discovery and interpretation
Beryl B. Cummings, Konrad J. Karczewski, Jack A. Kosmicki, Eleanor G. Seaby, Nicholas A. Watts, Moriel Singer-Berk, Jonathan M. Mudge, Juha Karjalainen, F. Kyle Satterstrom, Anne O’Donnell-Luria, Timothy Poterba, Cotton Seed, Matthew Solomonson, Jessica Alföldi, The Genome Aggregation Database Production Team, The Genome Aggregation Database Consortium, Mark J. Daly, Daniel G. MacArthur
bioRxiv 554444; doi: https://doi.org/10.1101/554444

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