Published September 12, 2019 | Version v1
Preprint Open

Quantifying genetic regulatory variation in human populations improves transcriptome analysis in rare disease patients

Authors/Creators

  • 1. Scripps Research Translational Institute, La Jolla, CA, USA;Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA, USA;New York Genome Center, New York, NY, USA;Department of Systems Biology, Columbia University, New York, NY, USA
  • 2. New York Genome Center, New York, NY, USA;Department of Systems Biology, Columbia University, New York, NY, USA
  • 3. Analytical and Translation Genetics Unit, Massachusetts General Hospital, Boston, MA, USA;Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
  • 4. Scripps Research Translational Institute, La Jolla, CA, USA;Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA, USA
  • 5. Neuromuscular and Neurogenetic Disorders of Childhood Section, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, USA
  • 6. Section of Genetic Medicine, Department of Medicine, University of Chicago, Chicago, IL, USA
  • 7. Department of Biology, Loyola University Chicago, Chicago, IL, USA;Department of Computer Science, Loyola University Chicago, Chicago, IL, USA

Description

Transcriptome data holds substantial promise for better interpretation of rare genetic variants in basic research and clinical settings. Here, we introduce ANalysis of Expression VAriation (ANEVA) to quantifygenetic variation in genedosage from allelic expression (AE) data in a population. Application to GTEx data showed that this variance estimate is robust across datasets and is correlated with selective constraintin a gene. We next usedANEVA variance estimates in a Dosage Outlier Test (ANEVA-DOT) to identify genes in an individual that are affected by a rare regulatory variant with an unusually strong effect. Applying ANEVA-DOT to AEdata form 70 Mendelian muscular disease patients showed high accuracy in detecting genes with pathogenic variantsin previously resolved cases, and lead to one confirmed and several potential new diagnosesin cases previously unresolved.Using our reference estimates from GTEx data, ANEVA-DOT can be readily incorporated in rare disease diagnostic pipelines to better utilize RNA-seq data

Notes

Research Funding: National Institutes of Health P30DK020595 Hae Kyung Im National Institutes of Health Daniel MacArthur National Institute of Mental Health R01MH106842 Tuuli Lappalainen National Institute of Mental Health R01MH106842 Paul Hoffman National Institute of Mental Health R01MH107666 Hae Kyung Im National Institute of Mental Health R01MH107666 Heather Wheeler National Human Genome Research Institute UM1HG008900 Daniel MacArthur National Institute of General Medical Sciences R01GM122924 Tuuli Lappalainen National Human Genome Research Institute UM1HG008901 Tuuli Lappalainen National Human Genome Research Institute 1K99HG009916-01 Stephane Castel Qualcomm Foundation Pejman Mohamaddi NIH Center for Translational Science Award UL1TR002550-01, and 5UL1 TR001114-05 Pejman Mohamaddi National Human Genome Research Institute UM1HG008901 Jonah Einson National Human Genome Research Institute R15HG009569 Heather Wheeler

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Additional details

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Is supplemented by
10.5281/zenodo.3406690 (DOI)
10.5281/zenodo.3406688 (DOI)
10.5281/zenodo.3406692 (DOI)
10.5281/zenodo.3406717 (DOI)