Estimating variance components in population scale family trees

• Published in: PLoS genetics Vol. 15; no. 5; p. e1008124
• Main Authors: Shor, Tal, Kalka, Iris, Geiger, Dan, Erlich, Yaniv, Weissbrod, Omer
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 09.05.2019; Public Library of Science (PLoS)
• Subjects: Animals; Biology and Life Sciences; Breeding of animals; Computer science; Computer Simulation; Decomposition; Digitization; Epidemiology; Family; Female; Genealogy; Genealogy and Heraldry; Genetic engineering; Genetic Fitness; Genetics, Population; Heritability; Humans; Linear Models; Longevity - genetics; Male; Medical records; Medical research; Medicine and Health Sciences; Methods; Models, Genetic; Pedigree; Physical Sciences; Plant breeding; Plants - genetics; Population; Quantitative genetics; Reproductive fitness; Research and Analysis Methods; Social Sciences; Software; Sparsity; Trees; New York; United States > US; Israel
• ISSN: 1553-7404; 1553-7390; 1553-7404
• DOI: 10.1371/journal.pgen.1008124

The rapid digitization of genealogical and medical records enables the assembly of extremely large pedigree records spanning millions of individuals and trillions of pairs of relatives. Such pedigrees provide the opportunity to investigate the sociological and epidemiological history of human populations in scales much larger than previously possible. Linear mixed models (LMMs) are routinely used to analyze extremely large animal and plant pedigrees for the purposes of selective breeding. However, LMMs have not been previously applied to analyze population-scale human family trees. Here, we present Sparse Cholesky factorIzation LMM (Sci-LMM), a modeling framework for studying population-scale family trees that combines techniques from the animal and plant breeding literature and from human genetics literature. The proposed framework can construct a matrix of relationships between trillions of pairs of individuals and fit the corresponding LMM in several hours. We demonstrate the capabilities of Sci-LMM via simulation studies and by estimating the heritability of longevity and of reproductive fitness (quantified via number of children) in a large pedigree spanning millions of individuals and over five centuries of human history. Sci-LMM provides a unified framework for investigating the epidemiological history of human populations via genealogical records.