AUTHOR=Zimmermann Michael T. 

TITLE=The Importance of Biologic Knowledge and Gene Expression Context for Genomic Data Interpretation

JOURNAL=Frontiers in Genetics

VOLUME=Volume 9 - 2018

YEAR=2018

URL=https://www.frontiersin.org/journals/genetics/articles/10.3389/fgene.2018.00670

DOI=10.3389/fgene.2018.00670

ISSN=1664-8021

ABSTRACT=Background: Genomic sequencing, including whole exome sequencing (WES), is enabling research and the medical practice to increase the resolution with which we define diseases, understand mechanisms, and practice clinical care. However, WES routinely identifies many genomic variants for which the functional associations are unknown. Furthering the uncertainty in WES data interpretation is that many genes express multiple transcripts and this pattern may differ by body tissue. In order to interpret WES data, we not only need to understand which transcript is most relevant for a novel genomic variant, but what tissue is most relevant.
Methods: In this work, we quantify how frequently these two features impact WES data interpretation at the level of gene, pathway, disease, and biologic network. We combined and analyzed multiple large and publically available datasets to inform genomic data interpretation.
Results: Across well-established biologic pathways and genetic contributors to disease, 73% and 58% have a different protein coding effect by transcript selection for, respectively, 25% and 50% of the genes contained. Additionally, strong differences in human tissue expression levels affect 32% and 18% of the same set of pathways and diseases for, respectively, 25% and 50% of the genes contained.
Conclusion: WES identifies genomic variants, but to interpret the functional effects of those variants in high-resolution will require a next generations of knowledge-generating systems that can take into account multiple biologic factors including transcript selection and cross-tissue gene expression levels. Such systems will further expand the variety of information available for genomics data interpretation.