Leveraging electronic health record-linked biobanks to understand the utility and generalizability of genetic risk across heterogeneous population structures Sandra Lapinska

Format:

Book

Thesis/Dissertation

Author/Creator:

Lapinska, Sandra, author.

Contributor:

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

Language:

English

Subjects (All):

Bioinformatics.

Genetics.

Statistics.

Demography.

Biostatistics.

0715.

0369.

0463.

0308.

0938.

Local Subjects:

Bioinformatics.

Genetics.

Statistics.

Demography.

Biostatistics.

0715.

0369.

0463.

0308.

0938.

Genre:

Academic theses

Physical Description:

1 online resource (174 pages)

Contained In:

Dissertations Abstracts International 87-12A

Place of Publication:

Ann Arbor : ProQuest Dissertations and Theses, 2026

Language Note:

English

Summary:

Biobanks are becoming increasingly more representative of diverse populations, providing large-scale genetic data linked with real world phenotypic data from electronic health records (EHR). The biobank era of genomics has created numerous opportunities, including increased power to detect novel associations, improved understanding of the generalizability of risk prediction across diverse populations, and the ability to address existing knowledge gaps in the field of genomics. In addition, polygenic risk scores (PGS), which aggregate the effect of common genetic variants to estimate an individual's genetic predisposition to a disease, have been shown to have utility in a variety of settings like patient risk stratification, disease risk prediction, and psychiatric research. In this dissertation, I investigate the utility and generalizability of PGS across heterogeneous population structures. I begin by gaining insights into the genetics of major depressive disorder (MDD). Prior studies of European ancestry individuals have shown a genetic overlap between treatment resistant depression and major depressive disorder. I leveraged longitudinal data and clinical information to understand the genetic underpinnings of antidepressant response across ancestries using PGS for MDD in the UCLA ATLAS biobank and All of Us Research Program. My findings demonstrated that PGS for MDD can provide new insights into antidepressant response and treatment specificity for MDD in individuals of diverse ancestries. Next, I evaluate the role of PGS within a broader framework of genetic risk assessment. As one of the three components of genetic risk, PGS can identify individuals whose risk is comparable to that conferred by monogenic pathogenic variants and also complimenting family history in assessing inherited disease susceptibility. Recognizing this, the Electronic Medical Records and Genomics (eMERGE) Network has integrated all three components into the genome-informed risk assessment (GIRA) report to implement genomic precision medicine across diverse clinical settings. I assess GIRA's utility and impact in a health care system independent of eMERGE, focusing on 9 adult conditions using the Penn Medicine Biobank. My results demonstrate the accuracy of GIRA as a biomarker to stratify high-risk patients and highlighted the implementation challenges in its impact on the health system if implemented at scale. Finally, I address a major barrier to the real application of PGS in clinical settings: their poor transferability to populations that differ from the GWAS samples used to construct it, with its performance varying across ancestry, age, sex, social determinants of health and even across different biobanks. To better understand this key challenge facing the clinical translation of PGS, I evaluate the magnitude how contextual factors like ancestry, age, sex, self-identified race and ethnicity, and body mass index impact PGS performance for coronary heart disease and breast cancer across 7 biobanks with heterogeneous population structures. I find that while PGS effect sizes are generally consistent across biobanks and contexts, genetic ancestry remains the primary driver of observed differences within biobanks

Notes:

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

Advisors: Pasaniuc, Bogdan Committee members: Kim, Dokyoon; Brown, Brielin C.; Damrauer, Scott M.; Setia Verma, Shefali; Olde Loohuis, Loes M.

Ph.D. University of Pennsylvania 2026

Vendor supplied data

Local Notes:

School code: 0175

ISBN:

9798247982494

Access Restriction:

Restricted for use by site license