ArticleNature genetics2024
Valid inference for machine learning-assisted genome-wide association studies.
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 16 papers.
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Who cites it
16 citing papers in PubMed.
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- Neuronal and glial networks interact with traumatic brain injury to modulate cognition in ABCD study.NPJ systems biology and applications · 2026Article
- Genomics of drug target prioritization for complex diseases.Nature reviews. Genetics · 2026Review
- The predicament of heritable confounders.Nature genetics · 2026Review
- Improving estimation efficiencies for family-based GWAS by integrating large external data.medRxiv : the preprint server for health sciences · 2025Article
- Adiposity distribution and risks of 12 obesity-related cancers: a Mendelian randomization analysis.Journal of the National Cancer Institute · 2025Article
- The Genomics Revolution in Nonmodel Species: Predictions vs. Reality for Salmonids.Molecular ecology · 2025Review
- Genetic analyses of eight complex diseases using predicted continuous representations of disease.Cell reports methods · 2025Article
- Towards the Efficient Inference by Incorporating Automated Computational Phenotypes under Covariate Shift.Proceedings of machine learning research · 2025Article
- Improving plant breeding through AI-supported data integration.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2025Review
- Can AI reveal the next generation of high-impact bone genomics targets?Bone reports · 2025Review
- Genetic association studies using disease liabilities from deep neural networks.American journal of human genetics · 2025Article
- ipd: an R package for conducting inference on predicted data.Bioinformatics (Oxford, England) · 2025Article
- Task-Agnostic Machine-Learning-Assisted Inference.Advances in neural information processing systems · 2024Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
Machine learning (ML) has become increasingly popular in almost all scientific disciplines, including human genetics. Owing to challenges related to sample collection and precise phenotyping, ML-assisted genome-wide association study (GWAS), which uses sophisticated ML techniques to impute phenotypes and then performs GWAS on the imputed outcomes, have become increasingly common in complex trait genetics research. However, the validity of ML-assisted GWAS associations has not been carefully evaluated. Here, we report pervasive risks for false-positive associations in ML-assisted GWAS and introduce Post-Prediction GWAS (POP-GWAS), a statistical framework that redesigns GWAS on ML-imputed outcomes. POP-GWAS ensures valid and powerful statistical inference irrespective of imputation quality and choice of algorithm, requiring only GWAS summary statistics as input. We employed POP-GWAS to perform a GWAS of bone mineral density derived from dual-energy X-ray absorptiometry imaging at 14 skeletal sites, identifying 89 new loci and revealing skeletal site-specific genetic architecture. Our framework offers a robust analytic solution for future ML-assisted GWAS.
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