Integrating high-dimensional biobank data for complex disease prediction Jakob Woerner

Format:

Book

Thesis/Dissertation

Author/Creator:

Woerner, Jakob, author.

Contributor:

University of Pennsylvania. Genomics and Computational Biology., degree granting institution.

Language:

English

Subjects (All):

Genetics.

Bioinformatics.

Medicine.

0369.

0715.

0564.

Local Subjects:

Genetics.

Bioinformatics.

Medicine.

0369.

0715.

0564.

Genre:

Academic theses

Physical Description:

1 online resource (186 pages)

Contained In:

Dissertations Abstracts International 87-12B

Place of Publication:

Ann Arbor : ProQuest Dissertations and Theses, 2026

Language Note:

English

Summary:

The emergence of large-scale biobanks has transformed genetic research from a focus on single-trait discovery to a multimodal analysis of the human phenome. This shift creates a significant opportunity to explore how modeling the shared genetic architecture between traits and integrating new proteomic technologies can optimize risk prediction. This dissertation evaluates these strategies through a series of studies designed to harmonize genetic and molecular data for precision health. First, we used diverse, publicly available genetic association data to conduct a large-scale assessment of pleiotropy across millions of individuals. After building evidence of pervasive pleiotropy, we constructed phenotype networks to model the shared genetic architecture between diseases and blood markers, and assessed how these networks vary across global ancestries. Second, we integrated proteomic and genetic data to evaluate their relative contributions to disease prediction across over 300 conditions. While proteomic risk scores often provide superior short-term predictive power, genetic data remains a critical, independent component for long-term risk stratification. The findings reveal that the most accurate models for precision medicine are those that integrate both inherited genetic risk and dynamic biological markers. This work proposes a framework for multi-omic integration in biobanks, demonstrating that clinical risk assessment benefits from the use of both genomics and proteomics. Enabled by the scale of global biobanks, this dissertation advances the effort toward individualized and accessible healthcare that accounts for both genetics and environmental exposures

Notes:

Source: Dissertations Abstracts International, Volume: 87-12, Section: B.

Advisors: Kim, Dokyoon Committee members: Long, Qi; Shen, Li; Zhao, Bingxin; Xu, Yaomin

Ph.D. University of Pennsylvania 2026

Vendor supplied data

Local Notes:

School code: 0175

ISBN:

9798247980483

Access Restriction:

Restricted for use by site license