Integrating Genetics and Socio-Environmental Factors to Predict Disease Progression

/ABSTRACT The trajectory of common, complex diseases is shaped by a dynamic interplay involving genetic and socio- environmental factors that together profoundly impact public health outcomes. Towards the goal of integrating genetic insights into precision medicine, PRS have shown promise as clinical tools for population risk stratification and early detection in highly heritable diseases. However, existing PRS models are often derived from disease risk-associated loci identified in traditional case-control GWAS which models lifetime risk, neglecting the critical timing of disease onset. To refine PRS in precision medicine, it is essential to broaden the genetic study focus from mere susceptibility to include disease progression. This shift is crucial to deepen our understanding of disease mechanisms, improve diagnosis and prognosis, and develop targeted therapies. This expanded scope emphasizes the importance of time-to-event (TTE) factors and the critical yet often overlooked socio-environmental factors, which influence health outcomes throughout the disease course. To address these needs, this project will advance the predictive models of disease progression by developing and applying statistical frameworks that integrate genetic and socio-environmental factors, utilizing rich genetic and longitudinal healthcare data in modern multi-ancestry biobanks. Targeting 16 exemplar TTE phenotypes, the models will be adaptable to various sequential disease progressions. Aim 1 will extend current PRS models using genome-wide survival analysis (GWSA), which can identify genetic variants affecting both disease occurrence and progression, to predict TTE phenotypes. This includes developing both single- and multi- ancestry PRS to improve prediction accuracy and generalizability. Aim 2 will innovate region-based PRS through set-based association analysis of rare variants, revealing new genetic contributors to progression of diseases by combining with common variants-based PRS. Aim 3 will integrate genetic predictors from Aims 1 and 2 with a polysocial score, which encapsulates comprehensive socio-environmental factors, thus enhancing the model’s precision and offering a holistic understanding of their respective roles in disease progression. This research is poised to significantly advance precision medicine by deepening our understanding of disease progression and informing more personalized, effective healthcare strategies. The research and training plan is meticulously designed to cultivate expertise in four key domains: statistical genetics methods development, rare variant association analysis, socio-environmental exposure data analysis, and professional skill enhancement. Conducted under the mentorship from world-renowned scientists, and in an outstanding research environment at Massachusetts General Hospital and the Broad Institute, this project and the K99/R00 award will be instrumental in propelling the candidate towards an independent research career in medical, population, and statistical genetics. Project Number: 1K99HG013969-01 | Fiscal Year: 2025 | NIH Institute/Center: National Human Genome Research Institute (NHGRI) | Principal Investigator: Ying Wang | Institution: MASSACHUSETTS GENERAL HOSPITAL, BOSTON, MA | Award Amount: $138,499 | Activity Code: K99 | Study Section: Genome Research Study Section[GNOM-G] View on NIH RePORTER: https://reporter.nih.gov/project-details/11031585