| Title: | The performance of AlphaMissense to identify genes influencing disease |
| Journal: | Human Genetics and Genomics Advances |
| Published: | 22 Aug 2024 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/39180217/ |
| DOI: | https://doi.org/10.1016/j.xhgg.2024.100344 |
Publication 13103
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Abstract
A novel algorithm, AlphaMissense, has been shown to have an improved ability to predict the pathogenicity of rare missense genetic variants. However, it is not known whether AlphaMissense improves the ability of gene-based testing to identify disease-influencing genes. Using whole-exome sequencing data from the UK Biobank, we compared gene-based association analysis strategies including sets of deleterious variants: predicted loss-of-function (pLoF) variants only, pLoF plus AlphaMissense pathogenic variants, pLoF with missense variants predicted to be deleterious by any of five commonly utilized annotation methods (Missense (1/5)) or only variants predicted to be deleterious by all five methods (Missense (5/5)). We measured performance to identify 519 previously identified positive control genes, which can lead to Mendelian diseases, or are the targets of successfully developed medicines. These strategies identified 850k pLoF variants and 5 million deleterious missense variants, including 22k likely pathogenic missense variants identified exclusively by AlphaMissense. The gene-based association tests found 608 significant gene associations (at P<1.25x10-7) across 24 common traits and diseases. Compared to pLoFs plus Missense (5/5), tests using pLoFs and AlphaMissense variants found slightly more significant gene-disease and gene-trait associations, albeit with a marginally lower proportion of positive control genes. Nevertheless, their overall performance was similar. Merging AlphaMissense with Missense (5/5), whether through their intersection or union, did not yield any further enhancement in performance. In summary, employing AlphaMissense to select deleterious variants for gene-based testing did not improve the ability to identify genes that are known to influence disease.</p>
9 Authors
- Yiheng Chen
- Guillaume Butler-Laporte
- Kevin Y H Liang
- Yann Ilboudo
- Summaira Yasmeen
- Takayoshi Sasako
- Claudia Langenberg
- Celia M T Greenwood
- J Brent Richards
1 Application
| Application ID | Title |
|---|---|
| 27449 | Genetic Association to Phenotypic Variance and its Role in Human Health |
Enabling scientific discoveries that improve human health