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    Area of Science:

    • Population genetics
    • Computational biology
    • Bioinformatics

    Background:

    • Computer simulations are crucial for studying population genetic models and predicting outcomes.
    • In-silico population construction with specific characteristics is vital for breeding optimization.
    • Existing methods may lack efficiency for large-scale genetic studies.

    Purpose of the Study:

    • To introduce two novel linear-time Simulation using Best-fit Algorithms (SimBA) for in-silico population construction.
    • To address problems involving linkage disequilibrium, allele frequency, founder-haplotypes, and polyploid allele dosage.
    • To enhance efficiency and accuracy in large-scale population genetic simulations.

    Main Methods:

    • Developed SimBA-LD for linkage disequilibrium and minimum allele frequency distributions.
    • Developed SimBA-hap for founder-haplotype and polyploid allele dosage distributions.
    • Utilized an incremental gap-filling approach for SimBA-LD and evaluated greedy and mixed-integer programming algorithms for SimBA-hap.

    Main Results:

    • Demonstrated accurate fitting of target distributions with the incremental SimBA-LD, enabling efficient large-scale simulations.
    • Validated SimBA-hap's accuracy and efficiency in simulating tetraploid populations with diverse founder haplotypes.
    • Showcased the performance of both linear-time greedy and optimal mixed-integer programming solutions within SimBA-hap.

    Conclusions:

    • SimBA provides efficient and accurate tools for in-silico population construction in genetic studies.
    • The developed algorithms facilitate large-scale simulations for breeding optimization and population genetic research.
    • SimBA is publicly available, promoting its application in the scientific community.