PSMC-FAC: A Statistical Framework for Correcting Loss of Heterozygosity in Low-Coverage Genomic Demographic Inference

Inferring demographic history from whole-genome data is a fundamental objective in evolutionary and conservation genomics. However, the Pairwise Sequentially Markovian Coalescent (PSMC) framework, the most widely used demographic inference method for whole-genome sequence data, is highly sensitive to sequencing coverage, with low coverage producing systematic underestimation of heterozygosity and biased effective population size trajectories. Here, we present PSMC-FAC, an automated method designed to optimize false-negative rate correction in low-coverage genomes by minimizing geometric distances between corrected and high-coverage demographic trajectories. Whole-genome datasets from humans, gray wolves, and cattle were downsampled across multiple coverage levels and processed through standard demographic inference pipelines. Corrected trajectories were compared using Hausdorff and discrete Fréchet distance metrics projected onto a common temporal grid, and optimal correction factors were modeled as a function of sequencing depth using polynomial regression. Across species and demographic contexts, PSMC-FAC markedly improved concordance between low- and high-coverage trajectories and revealed highly predictable coverage-dependent correction patterns. Overall, PSMC-FAC provides a reproducible and mathematically grounded alternative to subjective correction approaches, enabling reliable demographic inference from moderate-coverage genomes and facilitating broader population-scale genomic analyses.