Poster Presentation 30th Lorne Cancer Conference 2018

Predicting the consequences of missense variants using a measure of genic intolerance (#266)

Michael Silk 1
  1. Biochemistry and Molecular Biology, University of Melbourne, Parkville, VICTORIA, Australia

 

Over the past decade, gene panel and exome sequencing have become widely used in the diagnosis of many cancers. While successful, it remains particularly laborious to distinguish between driver and passenger mutations due to the strong heterogeneity in cancers. Predicting the functional consequences of variants remains a significant challenge, with current approaches of limited use in distinguishing pathogenic from benign. Missense variants are especially challenging, as a single amino acid change can have a broad range of effects on interactions and protein structure.

Using gnomAD[1], the largest database of human standing variation, we have created a sequence-based measure of intolerance to missense variation across over 18,000 unique genes named the Missense Tolerance Ratio (MTR)[2]. We demonstrate that patient-ascertained variants preferentially clustered in intolerant, low scoring MTR regions, and could be used to help accurately identify variants responsible for epilepsy.

We hypothesise that the MTR score has further utility in drugability site identification, by examining features under strong evolutionary conservation not previously considered fundamental to the resulting product from a gene.

Our research aims to combine our MTR estimates with protein tertiary structure properties to create a novel and more sensitive measure of intolerance for use in genomic analyses and to identify novel important structural and functional features. We have made the MTR viewer freely available through a user-friendly website at http://mtr-viewer.mdhs.unimelb.edu.au.

[1] Lek M, Karczewski KJ, Minikel EV, Samocha KE, Banks E, Fennell T, O’Donnell-Luria AH, Ware JS, Hill AJ, Cummings BB, et al. 2016. Analysis of protein-coding genetic variation in 60,706 humans. Nature 536: 285–291

[2] Traynelis J,* Silk M,* Wang Q, Berkovic SF, Liu L, Ascher DB, Balding DJ, Petrovski S (2017). Optimizing genomic medicine in epilepsy through a gene-customized approach to missense variant interpretation. Genome Research