Federal Project Grant Summary

The University of Oregon received a $553,321 Project Grant awarded June 15, 2026, through the National Science Foundation's (NSF) Biological Sciences program (CFDA 47.074) to develop methodology, software, and applications for tree-sequence-based linear mixed modeling (TSLMM). The project, extending through May 31, 2029, addresses computational and methodological challenges in analyzing large-scale genomic datasets by combining advanced genetic models with ancestral recombination graphs—detailed maps of genome inheritance over time. The research team will develop fast, open-source software capable of processing millions of genomes efficiently and implement linear mixed models using tree-sequence representations of genomic data, with particular focus on improving genomic prediction applications in breeding and genome-wide association studies that have been constrained by traditional reliance on large genetic relatedness matrices.

The project deliverables include theory and algorithms for applying linear mixed models to tree-sequence encoded data, scalable open-source software implementations, and validation testing using both simulated and large real-world datasets. The resulting tools are expected to enhance genomic risk prediction across agriculture and medicine, improve breeding efficiency, and enable researchers to better leverage modern artificial intelligence methods in biotechnology and genetics research. The work represents research at the intersection of quantitative genetics, statistical modeling, and high-performance computing, with anticipated benefits across multiple biological domains.