Functional assays in Drosophila facilitate classification of variants of uncertain significance associated with rare diseases
- Jung-Wan Mok1,2,5,
- Shelley B. Gibson1,2,3,5,
- Haley A. Dostalik1,2,3,5 and
- Shinya Yamamoto1,2,3,4
- 1Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas 77030, USA;
- 2Jan and Dan Duncan Neurological Research Institute, Texas Children's Hospital, Houston, Texas 77030, USA;
- 3Genetics and Genomics Graduate Program, Baylor College of Medicine, Houston, Texas 77030, USA;
- 4Department of Neuroscience, Baylor College of Medicine, Houston, Texas 77030, USA
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↵5 These authors contributed equally to this work.
Abstract
Individuals living with rare diseases often undergo a frustrating and expensive diagnostic odyssey. Clinical geneticists who analyze exome or genome sequencing data from rare disease patients often encounter a list of variants of uncertain significance (VUS) in known disease-causing genes or rare variants in genes of uncertain significance (GUS) that are difficult to interpret, even with the integration of the latest bioinformatic tools. In this Perspective, we review how studies using the fruit fly Drosophila melanogaster have facilitated rare disease diagnosis by uncovering the clinical relevance of GUS and classifying rare variants into specific allelic categories (loss-of-function or gain-of-function, Muller's morphs). We showcase how fly researchers have been collaboratively studying the loss-of-function of orthologous fly genes, assessing the ability of the human genes to rescue the fly mutant phenotypes, determining the effect of overexpressing human proteins, and testing functional consequences of rare variants of interest by generating analogous fly mutants to contribute to rare disease diagnosis. We argue that data obtained using Drosophila can be leveraged to design effective multiplexed assays for variant effects (MAVEs) to decipher the vast human variome.
Footnotes
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[Supplemental material is available for this article.]
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Article published online before print. Article, supplemental material, and publication date are at https://www.genome.org/cgi/doi/10.1101/gr.278291.123.
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