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| Main Authors: | , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2503.18810 |
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| _version_ | 1866908655128936448 |
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| author | Alliance, Atlas of Variant Effects : Calhoun, Jeffrey D. Dawood, Moez Rowlands, Charlie F. Fayer, Shawn Radford, Elizabeth J. McEwen, Abbye E. Tejura, Malvika Turnbull, Clare Spurdle, Amanda B. Starita, Lea M. Jagannathan, Sujatha |
| author_facet | Alliance, Atlas of Variant Effects : Calhoun, Jeffrey D. Dawood, Moez Rowlands, Charlie F. Fayer, Shawn Radford, Elizabeth J. McEwen, Abbye E. Tejura, Malvika Turnbull, Clare Spurdle, Amanda B. Starita, Lea M. Jagannathan, Sujatha |
| contents | With the surge in the number of variants of uncertain significance (VUS) reported in ClinVar in recent years, there is an imperative to resolve VUS at scale. Multiplexed assays of variant effect (MAVEs), which allow the functional consequence of 100s to 1000s of genetic variants to be measured in a single experiment, are emerging as a powerful source of evidence which can be used in clinical gene variant classification. Increasingly, multiple published MAVEs are available for the same gene, sometimes measuring different aspects of variant impact. When multiple functional roles of a gene need to be considered, combining data from multiple MAVEs may provide a more comprehensive measure of the consequence of a genetic variant, which could impact variant classifications. Here, we provide guidance for combining such multiplexed functional data, incorporating a stepwise process from data curation and collection to model generation and validation. We demonstrate the potential and pitfalls of this approach by showing the integration of multiplexed functional data from five MAVEs for the gene TP53, two MAVEs for the gene LDLR and two MAVEs for PTEN. We also present a web applet that allows users to test various methods for combining score sets from multiple assays, calculate integrated functional scores for all variants, and assess whether combining data enables the application of stronger evidence for pathogenicity or benignity. By following these steps with appropriate guardrails, researchers can maximize the value of MAVEs, strengthen the functional evidence for clinical variant classification, and potentially uncover novel mechanisms of pathogenicity for clinically relevant genes. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_18810 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Combining multiplexed functional data to improve variant classification Alliance, Atlas of Variant Effects : Calhoun, Jeffrey D. Dawood, Moez Rowlands, Charlie F. Fayer, Shawn Radford, Elizabeth J. McEwen, Abbye E. Tejura, Malvika Turnbull, Clare Spurdle, Amanda B. Starita, Lea M. Jagannathan, Sujatha Genomics With the surge in the number of variants of uncertain significance (VUS) reported in ClinVar in recent years, there is an imperative to resolve VUS at scale. Multiplexed assays of variant effect (MAVEs), which allow the functional consequence of 100s to 1000s of genetic variants to be measured in a single experiment, are emerging as a powerful source of evidence which can be used in clinical gene variant classification. Increasingly, multiple published MAVEs are available for the same gene, sometimes measuring different aspects of variant impact. When multiple functional roles of a gene need to be considered, combining data from multiple MAVEs may provide a more comprehensive measure of the consequence of a genetic variant, which could impact variant classifications. Here, we provide guidance for combining such multiplexed functional data, incorporating a stepwise process from data curation and collection to model generation and validation. We demonstrate the potential and pitfalls of this approach by showing the integration of multiplexed functional data from five MAVEs for the gene TP53, two MAVEs for the gene LDLR and two MAVEs for PTEN. We also present a web applet that allows users to test various methods for combining score sets from multiple assays, calculate integrated functional scores for all variants, and assess whether combining data enables the application of stronger evidence for pathogenicity or benignity. By following these steps with appropriate guardrails, researchers can maximize the value of MAVEs, strengthen the functional evidence for clinical variant classification, and potentially uncover novel mechanisms of pathogenicity for clinically relevant genes. |
| title | Combining multiplexed functional data to improve variant classification |
| topic | Genomics |
| url | https://arxiv.org/abs/2503.18810 |