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

Background:

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.

Methodology:

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.

Results:

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.

Conclusions:

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.