ArticleCell genomics2022
Machine learning optimized polygenic scores for blood cell traits identify sex-specific trajectories and genetic correlations with disease.
Article in Cell genomics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 1 of them a synthesis that pooled it.
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Who cites it
21 citing papers in PubMed, 1 synthesis or guideline pooled it, 32 citations in OpenAlex.
- Discovery and systematic characterization of risk variants and genes for coronary artery disease in over a million participants.Nature genetics · 2022Pooled it
- GeneSIS: enhancing transferability of polygenic scores with variant-level gene-by-sex interaction effects.bioRxiv : the preprint server for biology · 2026Article
- Exploring the use of machine and deep learning in genome-wide association studies: a comprehensive review.BioData mining · 2026Review
- Hundreds of cardiac MRI traits derived using 3D diffusion autoencoders share a common genetic architecture.Nature communications · 2026Article
- A machine learning framework reveals key drivers of cytokine responses in a healthy human cohort.NPJ systems biology and applications · 2026Article
- A deep ensemble encoder network method for improved polygenic risk score prediction.BioData mining · 2026Article
- Complex genetic effects linked to plasma protein abundance in the UK Biobank.Nature communications · 2025Article
- Concordance between male- and female-specific GWAS results helps define underlying genetic architecture of complex traits.Nature communications · 2025Article
- Diabetes mellitus polygenic risk scores: heterogeneity and clinical translation.Nature reviews. Endocrinology · 2025Review
- A survey on deep learning for polygenic risk scores.Briefings in bioinformatics · 2025Review
- Performance of deep-learning-based approaches to improve polygenic scores.Nature communications · 2025Article
- Genome-wide analyses of variance in blood cell phenotypes provide new insights into complex trait biology and prediction.Nature communications · 2025Article
- Modeling gene interactions in polygenic prediction via geometric deep learning.Genome research · 2025Article
- Immunogenetics of lithium response and psychiatric phenotypes in patients with bipolar disorder.Translational psychiatry · 2024Article
- Highly parameterized polygenic scores tend to overfit to population stratification via random effects.bioRxiv : the preprint server for biology · 2024Article
- Deep integrative models for large-scale human genomics.Nucleic acids research · 2023Article
- Article
- Polygenic score development in the era of large-scale biobanks.Cell genomics · 2022Article
- Comparison of Methods Utilizing Sex-Specific PRSs Derived From GWAS Summary Statistics.Frontiers in genetics · 2022Article
- From GWAS to Function: Using Functional Genomics to Identify the Mechanisms Underlying Complex Diseases.Frontiers in genetics · 2020Review
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Authors and funding
13 authors at 4 institutions in 3 countries.
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Abstract
Genetic association studies for blood cell traits, which are key indicators of health and immune function, have identified several hundred associations and defined a complex polygenic architecture. Polygenic scores (PGSs) for blood cell traits have potential clinical utility in disease risk prediction and prevention, but designing PGS remains challenging and the optimal methods are unclear. To address this, we evaluated the relative performance of 6 methods to develop PGS for 26 blood cell traits, including a standard method of pruning and thresholding (P + T) and 5 learning methods: LDpred2, elastic net (EN), Bayesian ridge (BR), multilayer perceptron (MLP) and convolutional neural network (CNN). We evaluated these optimized PGSs on blood cell trait data from UK Biobank and INTERVAL. We find that PGSs designed using common machine learning methods EN and BR show improved prediction of blood cell traits and consistently outperform other methods. Our analyses suggest EN/BR as the top choices for PGS construction, showing improved performance for 25 blood cell traits in the external validation, with correlations with the directly measured traits increasing by 10%-23%. Ten PGSs showed significant statistical interaction with sex, and sex-specific PGS stratification showed that all of them had substantial variation in the trajectories of blood cell traits with age. Genetic correlations between the PGSs for blood cell traits and common human diseases identified well-known as well as new associations. We develop machine learning-optimized PGS for blood cell traits, demonstrate their relationships with sex, age, and disease, and make these publicly available as a resource.
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