PhenoQC: An integrated toolkit for quality control of phenotypic data in genomic research

Large-scale genomic research requires robust, consistent phenotypic datasets for meaningful genotype–phenotype correlations. However, diverse collection protocols, incomplete entries, and heterogeneous terminologies frequently compromise data quality and slows downstream analysis. To address these issues, we present PhenoQC, a high-throughput, configuration-driven toolkit that unifies schema validation, ontology-based semantic alignment, and missing-data imputation in a single workflow. Its modular architecture leverages chunk-based parallelism to handle large datasets, while customizable schemas enforce structural and type constraints. PhenoQC applies user-defined and state-of-the-art machine learning-based imputation and performs multi-ontology mapping with fuzzy matching to harmonize phenotype text. It also quantifies potential imputation-induced distributional shifts by reporting standardized mean difference, variance ratio, and Kolmogorov–Smirnov statistics for numeric variables, and population stability index and Cramér’s V for categorical variables, with user-configurable thresholds. The toolkit provides command-line and graphical interfaces for seamless integration into automated pipelines and interactive curation environments. We benchmarked PhenoQC on synthetic datasets with up to 100,000 records and it demonstrated near-linear scalability and full recovery of artificially missing numeric values.Moreover, PhenoQC’s ontology alignment achieved over 97% accuracy under textual corruption. Finally, using two real clinical datasets, PhenoQC successfully imputed missing values, enforced schema compliance, and flagged data anomalies without significant overhead. PhenoQC saves manual curation time and ensures consistent, analysis-ready phenotypic data through its streamlined system. Its adaptable design adjusts to evolving ontologies and domain-specific rules, empowering researchers to conduct more reliable studies. • PhenoQC is a toolkit for schema validation, ontology alignment, and missing-data imputation. • PhenoQC uses chunk-based parallelism for large datasets with customizable schema validation. • PhenoQC applies user-defined or state-of-the-art imputation (mean/median/mode, KNN, MICE, Iterative SVD) and quantifies post-imputation bias with validated distributional metrics (SMD, variance ratio, KS; PSI, Cramér’s V ). • Both command-line and graphical interfaces are provided for smooth integration into automated pipelines or interactive curation environments.