ArticleNature genetics2024
Synthetic surrogates improve power for genome-wide association studies of partially missing phenotypes in population biobanks.
Article in Nature genetics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Artificial Intelligence for Alzheimer's Disease Diagnosis: From Traditional Machine Learning to Large Language Models.Biosensors · 2026Review
- DOMAIN-AWARE MATRIX COMPLETION FOR PHENOTYPE IMPUTATION USING ELECTRONIC HEALTH RECORD DATA WITH APPLICATIONS IN GENOMIC RESEARCH.The annals of applied statistics · 2026Article
- In silico generation of gene expression profiles using diffusion models.BMC bioinformatics · 2026Article
- Distributed quantile regression for big data with missing covariates.Statistics in biosciences · 2026Article
- Distinguishing specific from broad genetic associations between external correlates and common factors.Bioinformatics (Oxford, England) · 2025Article
- NeoNet: A Novel Deep Learning Model for Retinal Disease Diagnosis and Localization.Sensors (Basel, Switzerland) · 2025Article
- Genetic analyses of eight complex diseases using predicted continuous representations of disease.Cell reports methods · 2025Article
- Genetic association studies using disease liabilities from deep neural networks.American journal of human genetics · 2025Article
- A statistical framework for multi-trait rare variant analysis in large-scale whole-genome sequencing studies.Nature computational science · 2025Article
- Influence of multi-species data on gene-disease associations in substance use disorder using random walk with restart models.PloS one · 2025Article
- Valid inference for machine learning-assisted genome-wide association studies.Nature genetics · 2024Article
- A statistical framework for powerful multi-trait rare variant analysis in large-scale whole-genome sequencing studies.bioRxiv : the preprint server for biology · 2023Article
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Authors and funding
4 authors.
Funding
Abstract
Within population biobanks, incomplete measurement of certain traits limits the power for genetic discovery. Machine learning is increasingly used to impute the missing values from the available data. However, performing genome-wide association studies (GWAS) on imputed traits can introduce spurious associations, identifying genetic variants that are not associated with the original trait. Here we introduce a new method, synthetic surrogate (SynSurr) analysis, which makes GWAS on imputed phenotypes robust to imputation errors. Rather than replacing missing values, SynSurr jointly analyzes the original and imputed traits. We show that SynSurr estimates the same genetic effect as standard GWAS and improves power in proportion to the quality of the imputations. SynSurr requires a commonly made missing-at-random assumption but relaxes the requirements of existing imputation methods by not requiring correct model specification. We present extensive simulations and ablation analyses to validate SynSurr and apply it to empower the GWAS of dual-energy X-ray absorptiometry traits within the UK Biobank.
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