The performance of AlphaMissense to identify genes influencing disease
Yiheng Chen,
Guillaume Butler-Laporte,
Kevin Y.H. Liang,
Yann Ilboudo,
Summaira Yasmeen,
Takayoshi Sasako,
Claudia Langenberg,
Celia M.T. Greenwood,
J. Brent Richards
Affiliations
Yiheng Chen
Department of Human Genetics, McGill University, Montréal, QC, Canada; Lady Davis Institute, Jewish General Hospital, McGill University, Montréal, QC, Canada
Guillaume Butler-Laporte
Lady Davis Institute, Jewish General Hospital, McGill University, Montréal, QC, Canada; Division of Infectious Diseases, Department of Medicine, McGill University, Montréal, QC, Canada
Kevin Y.H. Liang
Lady Davis Institute, Jewish General Hospital, McGill University, Montréal, QC, Canada; Quantitative Life Sciences Program, McGill University, Montréal, QC, Canada
Yann Ilboudo
Lady Davis Institute, Jewish General Hospital, McGill University, Montréal, QC, Canada
Summaira Yasmeen
Computational Medicine, Berlin Institute of Health at Charité—Universitätsmedizin Berlin, Berlin, Germany
Takayoshi Sasako
Lady Davis Institute, Jewish General Hospital, McGill University, Montréal, QC, Canada; Tanaka Diabetes Clinic Omiya, Saitama, Japan; Department of Diabetes and Metabolic Diseases, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
Claudia Langenberg
Precision Healthcare University Research Institute, Queen Mary University of London, London, UK; Computational Medicine, Berlin Institute of Health at Charité—Universitätsmedizin Berlin, Berlin, Germany
Celia M.T. Greenwood
Lady Davis Institute, Jewish General Hospital, McGill University, Montréal, QC, Canada; Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montréal, QC, Canada; Gerald Bronfman Department of Oncology, McGill University, Montréal, QC, Canada
J. Brent Richards
Department of Human Genetics, McGill University, Montréal, QC, Canada; Lady Davis Institute, Jewish General Hospital, McGill University, Montréal, QC, Canada; Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montréal, QC, Canada; 5 Prime Sciences Inc, Montréal, QC, Canada; Department of Medicine, McGill University, Montréal, QC, Canada; Department of Twin Research, King’s College London, London, UK; Corresponding author
Summary: 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 0.85 million pLoF variants and 5 million deleterious missense variants, including 22,131 likely pathogenic missense variants identified exclusively by AlphaMissense. The gene-based association tests found 608 significant gene associations (at p < 1.25 × 10−7) across 24 common traits and diseases. Compared with 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.
