ArticleBioinformatics (Oxford, England)2023
HAPNEST: efficient, large-scale generation and evaluation of synthetic datasets for genotypes and phenotypes.
Article in Bioinformatics (Oxford, England), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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22 citing papers in PubMed.
- PRISM-G: an interpretable privacy scoring framework for assessing risk in synthetic human genome data.Bioinformatics (Oxford, England) · 2026Article
- Evidence standards for multi-ancestry polygenic prediction.Genome biology · 2026Review
- Detecting gene-environment interactions to guide personalized intervention: Boosting distributional regression for polygenic scores.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- scDesignPop generates realistic population-scale single-cell RNA-seq for power analysis, benchmarking, and privacy protection.bioRxiv : the preprint server for biology · 2026Article
- Empowering genetic discoveries and cardiovascular risk assessment by predicting electrocardiograms from genotype.NPJ digital medicine · 2026Article
- Genotype subtyping approach to identify unnoticed variants in diseases from GWAS data.BioData mining · 2026Article
- Fast phenotype simulation for genotype representation graphs.Bioinformatics advances · 2026Article
- Privacy-hardened and hallucination-resistant synthetic data generation with logic-solvers.Bioinformatics (Oxford, England) · 2025Article
- HIGH-DIMENSIONAL STATISTICAL INFERENCE FOR LINKAGE DISEQUILIBRIUM SCORE REGRESSION AND ITS CROSS-ANCESTRY EXTENSIONS.Annals of statistics · 2025Article
- Generating synthetic genotypes using diffusion models.Bioinformatics (Oxford, England) · 2025Article
- HAP-SAMPLE2: data-based resampling for association studies with admixture.Bioinformatics (Oxford, England) · 2025Article
- Challenges and applications in generative AI for clinical tabular data in physiology.Pflugers Archiv : European journal of physiology · 2025Review
- Article
- Synthetic data can aid the analysis of clinical outcomes: How much can it be trusted?Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- tstrait: a quantitative trait simulator for ancestral recombination graphs.Bioinformatics (Oxford, England) · 2024Article
- A resampling-based approach to share reference panels.Nature computational science · 2024Article
- tstrait: a quantitative trait simulator for ancestral recombination graphs.bioRxiv : the preprint server for biology · 2024Article
- A benchmark study on current GWAS models in admixed populations.Briefings in bioinformatics · 2023Article
- Power of inclusion: Enhancing polygenic prediction with admixed individuals.American journal of human genetics · 2023Article
- Genetic fine-mapping from summary data using a nonlocal prior improves the detection of multiple causal variants.Bioinformatics (Oxford, England) · 2023Article
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13 authors.
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Abstract
motivationExisting methods for simulating synthetic genotype and phenotype datasets have limited scalability, constraining their usability for large-scale analyses. Moreover, a systematic approach for evaluating synthetic data quality and a benchmark synthetic dataset for developing and evaluating methods for polygenic risk scores are lacking.
resultsWe present HAPNEST, a novel approach for efficiently generating diverse individual-level genotypic and phenotypic data. In comparison to alternative methods, HAPNEST shows faster computational speed and a lower degree of relatedness with reference panels, while generating datasets that preserve key statistical properties of real data. These desirable synthetic data properties enabled us to generate 6.8 million common variants and nine phenotypes with varying degrees of heritability and polygenicity across 1 million individuals. We demonstrate how HAPNEST can facilitate biobank-scale analyses through the comparison of seven methods to generate polygenic risk scoring across multiple ancestry groups and different genetic architectures. AVAILABILITY AND IMPLEMENTATION: A synthetic dataset of 1 008 000 individuals and nine traits for 6.8 million common variants is available at https://www.ebi.ac.uk/biostudies/studies/S-BSST936. The HAPNEST software for generating synthetic datasets is available as Docker/Singularity containers and open source Julia and C code at https://github.com/intervene-EU-H2020/synthetic_data.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.