Computational and statistical methods for genetic association studies of disease course over time

The National Human Genome Research Institute (NHGRI) awarded Massachusetts General Hospital $3.29M under the Human Genome Research program (CFDA 93.172) on September 17, 2025, for a project addressing computational and statistical methodological gaps in genome-wide association studies (GWAS) of disease course over time. The award funds research through August 31, 2029, to develop scalable computational tools and statistical methods that leverage longitudinal electronic health record data from large biobanks such as UK Biobank, All of Us, and FinnGen. Specifically, the project will create innovative approaches for rare variant association testing on time-to-event (TTE) phenotypes while accounting for sample relatedness, population substructure, and censoring; develop novel methodologies to include admixed populations in GWAS of disease onset and progression to enhance genetic discovery equity; and improve GWAS and meta-analysis methods to address biases including left censoring, sampling bias, and collider bias.

The project directly advances NHGRI's mission to accelerate scientific and medical breakthroughs through genomic research by generating broadly applicable computational resources and methodologies that will enable more comprehensive identification of genetic variants influencing disease progression and therapeutic response. These deliverables will support precision medicine applications and reduce disparities in genetic discovery by enabling researchers to analyze diverse populations more effectively using standardized, validated statistical approaches applicable across multiple biobank platforms and cohorts.

Abstract

Abstract genome-wide association studies (gwas) have been remarkably successful at mapping genomic loci for complex human diseases. However, most studies use case-control designs based on disease occurrence at a specific time point, such as diagnosis. Overlooking genetic factors that influence the course of disease over time, including initiation, onset, progression, severity, and therapeutic response. Time-to-event (TTE) phenotypes capture both the occurrence and timing of disease activity. Gwas of TTE phenotypes can identify genetic variants associated with disease onset and progression, providing insights for early prevention and therapies aimed at halting disease progression. Large biobanks like UK biobank, all of US, and finngen, which combine longitudinal electronic health records (EHR) with genomic data, offer unprecedented opportunities to study genetics of TTE phenotypes. However, limitations remain in current computational and statistical tools, especially for rare variants and admixed populations. Additionally, biobank heterogeneity- differences in sampling strategies, follow-up times, and baseline hazards-poses challenges for meta-analysis of gwas on TTE phenotypes. This project aims to overcome these barriers by developing innovative computational tools and statistical methods for gwas of TTE phenotypes in large biobanks and cohorts. Aim 1 focuses on creating scalable methods for rare variant association tests on TTE endpoints, accounting for sample relatedness, population substructure, and high censoring. Aim 2 develops novel approaches to include admixed individuals in gwas of disease onset and progression over time, enhancing inclusivity and reducing disparities in genetic discovery. Aim 3 improves gwas and meta-analysis methodologies for TTE endpoints by addressing biases like left censoring, sampling bias, and collider bias, enabling robust integration of data across biobanks. The proposed methods will be evaluated through extensive simulation studies and applied to multiple biobanks. Successful completion of this project will provide new critical tools to advance our understanding of the genetic basis of complex diseases, reduce health disparities, and fully harness the potential of biobank resources for uncovering genetic factors influencing disease onset, progression, and treatment response. These tools and results will be made available as open-source software and public datasets to ensure broad accessibility to the research community. Additionally, we will continue to develop, distribute, and support open- source software packages for the proposed methods.