Dissertation/ Thesis
Privacy-Preserving Techniques for Genotype-Phenotype Association Studies
| Τίτλος: | Privacy-Preserving Techniques for Genotype-Phenotype Association Studies |
|---|---|
| Συγγραφείς: | Choi, Yoolim |
| Έτος έκδοσης: | 2026 |
| Συλλογή: | Columbia University: Academic Commons |
| Θεματικοί όροι: | Bioinformatics, Biometry, Genomics, Phenotype, Personalized medicine, Machine learning, Gene expression--Data processing, Genetic transcription, Cryptography |
| Περιγραφή: | Genotype-phenotype association studies play a central role in precision medicine by enabling researchers to identify genetic variants that influence complex traits and diseases with applications ranging from disease risk prediction to treatment stratification. Notable approaches include genome-wide association studies (GWAS), quantitative trait loci (QTL) mapping, and increasingly, machine learning models that learn predictive relationships between genotype and phenotype data. The power of these approaches grows substantially in collaborative research settings, where pooling data across cohorts increases the statistical power for variant discovery, and sharing trained machine learning models enables institutions with limited computational resources to perform inference on their own cohorts. However, these collaborative gains come with a fundamental privacy-utility tradeoff: sharing raw genomic and phenotypic data can expose sensitive information about study participants, and sharing machine learning models introduces the risk of information leakage through model parameters. These concerns are amplified by strict data governance policies and institutional silos that restrict data movement, underscoring the urgent need for privacy-preserving frameworks to enable secure, collaborative analysis while maintaining statistical rigor and practical scalability. This dissertation develops end-to-end, cryptography-based solutions for privacy-preserving genotype-phenotype association studies, and addresses the entire analysis pipeline from preprocessing and harmonization of phenotype data to secure multi-site eQTL mapping to inference on sensitive data using machine learning models. The overarching goal is to design frameworks that are designed to (i) provide strong cryptographic security guarantees, (ii) be computationally scalable to large multi-institution cohorts, and (iii) be statistically robust by preserving accuracy comparable to non-secure baselines. In Chapter 3, we design a privacy-preserving gene expression ... |
| Τύπος εγγράφου: | thesis |
| Γλώσσα: | English |
| DOI: | 10.7916/159w-bw62 |
| Διαθεσιμότητα: | https://doi.org/10.7916/159w-bw62 |
| Αριθμός Καταχώρησης: | edsbas.7502D99 |
| Βάση Δεδομένων: | BASE |
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