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 |
| FullText | Text: Availability: 0 CustomLinks: – Url: https://doi.org/10.7916/159w-bw62# Name: EDS - BASE (ns324271) Category: fullText Text: View record from BASE |
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| Header | DbId: edsbas DbLabel: BASE An: edsbas.7502D99 RelevancyScore: 933 AccessLevel: 3 PubType: Dissertation/ Thesis PubTypeId: dissertation PreciseRelevancyScore: 932.635681152344 |
| IllustrationInfo | |
| Items | – Name: Title Label: Title Group: Ti Data: Privacy-Preserving Techniques for Genotype-Phenotype Association Studies – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Choi%2C+Yoolim%22">Choi, Yoolim</searchLink> – Name: DatePubCY Label: Publication Year Group: Date Data: 2026 – Name: Subset Label: Collection Group: HoldingsInfo Data: Columbia University: Academic Commons – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Bioinformatics%22">Bioinformatics</searchLink><br /><searchLink fieldCode="DE" term="%22Biometry%22">Biometry</searchLink><br /><searchLink fieldCode="DE" term="%22Genomics%22">Genomics</searchLink><br /><searchLink fieldCode="DE" term="%22Phenotype%22">Phenotype</searchLink><br /><searchLink fieldCode="DE" term="%22Personalized+medicine%22">Personalized medicine</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Gene+expression--Data+processing%22">Gene expression--Data processing</searchLink><br /><searchLink fieldCode="DE" term="%22Genetic+transcription%22">Genetic transcription</searchLink><br /><searchLink fieldCode="DE" term="%22Cryptography%22">Cryptography</searchLink> – Name: Abstract Label: Description Group: Ab Data: 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 ... – Name: TypeDocument Label: Document Type Group: TypDoc Data: thesis – Name: Language Label: Language Group: Lang Data: English – Name: DOI Label: DOI Group: ID Data: 10.7916/159w-bw62 – Name: URL Label: Availability Group: URL Data: https://doi.org/10.7916/159w-bw62 – Name: AN Label: Accession Number Group: ID Data: edsbas.7502D99 |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsbas&AN=edsbas.7502D99 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.7916/159w-bw62 Languages: – Text: English Subjects: – SubjectFull: Bioinformatics Type: general – SubjectFull: Biometry Type: general – SubjectFull: Genomics Type: general – SubjectFull: Phenotype Type: general – SubjectFull: Personalized medicine Type: general – SubjectFull: Machine learning Type: general – SubjectFull: Gene expression--Data processing Type: general – SubjectFull: Genetic transcription Type: general – SubjectFull: Cryptography Type: general Titles: – TitleFull: Privacy-Preserving Techniques for Genotype-Phenotype Association Studies Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Choi, Yoolim IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 01 Type: published Y: 2026 Identifiers: – Type: issn-locals Value: edsbas – Type: issn-locals Value: edsbas.oa |
| ResultId | 1 |