Efficient Data Reduction for Large-Scale Genetic Mapping

We present a fast and accurate algorithm for reducing large-scale genetic marker data to a smaller, less noisy, and more complete set of bins, representing uniquely identifiable loca-tions on a chromosome. Our experimental results on real and synthetic data show that our algorithm runs in near-linear time, allowing for the analysis of millions of markers. Our algorithm reduces the problem scale while preserving accuracy, making it feasible to use existing genetic map-ping tools without resorting to complex, time-intensive pre-processing methods to filter or sample the original data set. Additionally, our approach also decreases the uncertainty in genotype calls, improving the quality of the data. Prelimi-nary results demonstrate that existing methods for marker ordering designed for the small scale settings perform with equivalent accuracy when given our reduced bin set as in-put