TMARG

TMARG leverages ancestral recombination graphs (ARGs) to improve association mapping and identify disease-associated sites in genetic datasets for complex and Mendelian traits.

Key Features:

• Ancestral Recombination Graphs (ARGs): TMARG constructs and utilizes ARGs to model the genealogical history of sampled individuals and represent recombination events.
• Minimized Recombinations (minARGs): TMARG focuses on constructing minARGs that minimize the number of recombinations and implements uniform random sampling from these minimized graphs.
• ARG Sampling Methods: TMARG provides a provably accurate ARG sampling method for moderate-sized datasets and a faster alternative that samples from a well-defined subspace for larger datasets.
• Phenotype Likelihood Extensions: TMARG incorporates extensions to the phenotype likelihood problem to refine identification of causative mutations.

Scientific Applications:

• Genome-Wide Association Studies (GWAS): TMARG is applicable to genome-wide scans to locate genes influencing complex traits and Mendelian disorders.
• Efficient Gene Mapping: By explicitly incorporating genealogical histories via ARGs and minARG sampling, TMARG improves the accuracy and efficiency of mapping disease-associated sites compared to methods that do not utilize ARGs.

Methodology:

Constructing minARGs to minimize recombinations; sampling these graphs uniformly at random for moderate datasets or sampling from a well-defined ARG subspace for larger datasets; and integrating phenotype likelihood extensions into the association mapping framework.