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In Vivo Functional Study of Disease-associated Rare Human Variants Using Drosophila

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Variant effect prediction tools assessed using independent, functional assay-based datasets: implications for

Khalid Mahmood¹, Chol-Hee Jung¹, Gayle Philip¹

¹Melbourne Bioinformatics, The University of Melbourne, Melbourne, Australia.

|May 18, 2017

Summary

Genetic variant effect prediction tools may not be as accurate as claimed. New research using functional assay datasets shows lower performance, highlighting the need for improved benchmarking in clinical genomics.

Keywords:

Benchmarking Functional assays Functional datasets Genomic screening Mutation assessment Pathogenicity prediction Protein function Variant effect prediction

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Published on: December 9, 2015

Area of Science:

Genomics
Bioinformatics
Computational Biology

Background:

Genetic variant effect prediction algorithms are crucial in clinical genomics and research.
Understanding algorithm accuracy and limitations is vital due to circularity and error propagation in performance metrics.

Purpose of the Study:

To assess the performance of leading variant effect prediction tools.
To evaluate the impact of benchmarking datasets on reported accuracies.

Main Methods:

Derived three independent, functionally determined human mutation datasets: UniFun, BRCA1-DMS, and TP53-TA.
Employed these datasets alongside previously described ones to benchmark variant effect prediction tools.

Main Results:

Apparent accuracies were significantly influenced by the benchmarking dataset.
Assay-determined datasets (UniFun, BRCA1-DMS) yielded lower areas under the receiver operating characteristic curves (0.52-0.63 and 0.54-0.75) compared to other datasets.

Conclusions:

Current variant effect prediction tools may be less accurate than previously reported, especially for clinical applications.
Functional assay-based datasets, free from prior dependencies, are valuable for developing and accurately benchmarking these tools.