MPH: fast REML for large-scale genome partitioning of quantitative genetic variation
Jicai Jiang
Abstract
MOTIVATION: Genome partitioning of quantitative genetic variation is useful for dissecting the genetic architecture of complex traits. However, existing methods, such as Haseman-Elston regression and linkage disequilibrium score regression, often face limitations when handling extensive farm animal datasets, as demonstrated in this study. RESULTS: To overcome this challenge, we present MPH, a novel software tool designed for efficient genome partitioning analyses using restricted maximum likelihood. The computational efficiency of MPH primarily stems from two key factors: the utilization of stochastic trace estimators and the comprehensive implementation of parallel computation. Evaluations with simulated and real datasets demonstrate that MPH achieves comparable accuracy and significantly enhances convergence, speed, and memory efficiency compared to widely used tools like GCTA and LDAK. These advancements facilitate large-scale, comprehensive analyses of complex genetic architectures in farm animals. AVAILABILITY AND IMPLEMENTATION: The MPH software is available at https://jiang18.github.io/mph/.