Computational frameworks for phylogenomic analysis of crop genomes with complex evolutionary histories
Novel computational tools including FastNet and adaptive introgression mapping frameworks for analyzing non-tree-like genome evolution in crops such as wheat and rice. Enables discovery of driver genes responsible for stress resistance, plant growth, and other agriculturally relevant traits through phylogeny-aware functional genomic analysis.
Overview
Crop genomes often have complex evolutionary histories shaped by hybridization, introgression, and gene flow between species or populations. These processes make traditional linear phylogenetic analyses inadequate for identifying the genetic basis of important agricultural traits. This offering provides novel computational frameworks designed to analyze genomes with non-tree-like evolutionary histories, enabling researchers and breeders to uncover driver genes linked to biotic and abiotic stress resistance, plant growth, and other commercially relevant phenotypic characteristics in crops such as wheat and rice.
Technical specifications
Core methodologies:
- FastNet: a new method for reconstructing complex non-tree-like phylogenies from large-scale genomic datasets
- Adaptive introgression mapping framework: detects genomic patterns of adaptive introgression, identifying genomic regions acquired through hybridization that contribute to adaptive traits
- Phylogeny-aware functional analysis: integrates evolutionary reconstructions with functional genomic data to identify driver genes underlying agriculturally relevant phenotypes
- Genotype-phenotype association mapping: computational frameworks designed specifically for genomes with complex evolutionary histories
These tools build on proven methods previously validated in animal model organisms, including house mouse and natural Arabidopsis populations, where they successfully uncovered the genomic basis of flowering time.
Technology readiness level
The computational frameworks have been validated in established model systems, with demonstrated success in genotype-phenotype association mapping for Arabidopsis flowering time and phylogenomic analyses of house mouse. Future validation will extend these methods to wheat, rice, and other crops through population genomic data analysis. The approach is ready for collaborative application to crop species, with ongoing development aimed at scaling to large agricultural genomic datasets and integrating adaptive introgression detection with functional gene discovery.
About Michigan State University
Michigan State University is a major public land‑grant research university with a comprehensive academic portfolio and a large research enterprise. Industry partners engage through an on‑campus U.S. Department of Energy national user facility and shared core laboratories with user access. The university provides a chemical process scale‑up pilot plant on Michigan’s lakeshore, a research and technology park, and a Grand Rapids health innovation campus linking researchers with clinical partners. A statewide extension network supports field deployment and workforce training across Michigan’s manufacturing corridor. Research is backed by competitive federal funding from NSF, NIH, DOE, USDA, and DoD, while dedicated tech transfer and corporate engagement teams—supported by an affiliated research foundation—accelerate IP, licensing, startups, and sponsored research.
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