MapPop

MapPop optimizes selection of individuals from large mapping populations to maximize genomic breakpoint coverage and linkage-map resolution for genome-wide linkage mapping while reducing the need to genotype all markers in every individual.

Key Features:

• Selective Mapping Approach: Implements selective mapping by choosing a reduced subset of individuals with complementary recombinational or radiation-induced breakpoints that are evenly distributed across the genome.
• Discrete Stochastic Optimization: Formulates sample selection as a discrete stochastic optimization problem to identify sample sets that maximize genomic coverage and map resolution.
• Evaluation on Simulated and Empirical Data: Includes sample-selection methods evaluated on simulated datasets and empirical mapping populations, including the Lister and Dean Arabidopsis thaliana recombinant inbred population and the GeneBridge 4 human radiation hybrid panel.
• High-Resolution Mapping with Reduced Samples: Produces high-density linkage maps and high map resolution while requiring substantially fewer genotyped individuals than genotyping entire populations.

Scientific Applications:

• Genome-wide linkage mapping: Enables construction of high-density, genome-wide linkage maps from large mapping populations.
• Genotyping-effort reduction in model and human panels: Reduces genotyping burden in studies such as the Lister and Dean Arabidopsis thaliana recombinant inbred population and the GeneBridge 4 human radiation hybrid panel.

Methodology:

Identifies individuals that collectively provide evenly spaced recombinational or radiation-induced breakpoints, applies discrete stochastic optimization to select optimal subsets, and evaluates selection strategies using simulation and empirical data from established mapping populations.