Sci-LMM

Sci-LMM performs linear mixed-model fitting and heritability estimation on population-scale pedigree data to analyze genetic relationships and traits such as longevity and reproductive fitness in large historical family trees.

Key Features:

• Large-Scale Pedigree Analysis: Constructs relationship matrices representing trillions of individual pairs from extensive family trees.
• Efficient Computational Framework: Uses Sparse Cholesky factorization to efficiently fit linear mixed models (LMMs) to massive pedigree datasets, enabling analyses within several hours for large datasets.
• Integration of Diverse Methodologies: Combines techniques from animal and plant breeding literature with human genetics approaches to leverage cross-field methods for pedigree analysis.
• Application in Heritability Studies: Validated by simulation studies and applied to real-world genealogical data to estimate heritability of traits such as longevity and reproductive fitness (number of children) across historical family trees spanning over five centuries.

Scientific Applications:

• Epidemiological history analysis: Analyzes genealogical records to explore the epidemiological history of human populations.
• Sociological and epidemiological trend investigation: Investigates sociological and epidemiological trends across large temporal and population scales using extensive pedigree data.
• Population genetics of longevity and reproductive success: Estimates heritability and genetic contributions to longevity and reproductive fitness in historical pedigrees.

Methodology:

Uses Sparse Cholesky factorization to construct and factor relationship matrices and to fit linear mixed models (LMMs); validated by simulation studies and application to real-world genealogical data.