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Estimating variance components in population scale family trees.
Tal Shor1,2, Iris Kalka3,4, Dan Geiger1
1Computer Science Department, Technion-Israel Institute of Technology, Haifa, Israel.
Plos Genetics
|May 10, 2019
Summary
Researchers developed Sparse Cholesky factorization LMM (Sci-LMM) to analyze large human family trees. This new method enables studying population history and heritability using genealogical records at an unprecedented scale.
Area of Science:
- Population genetics
- Computational biology
- Human genetics
Background:
- Digitized genealogical and medical records allow for large-scale pedigree analysis.
- Previous methods were not suitable for population-scale human family trees.
- Linear mixed models (LMMs) are effective for animal and plant pedigrees but not human ones.
Purpose of the Study:
- To develop a computational framework for analyzing population-scale human family trees.
- To enable the investigation of sociological and epidemiological history using large pedigrees.
- To estimate heritability of traits like longevity and reproductive fitness in human populations.
Main Methods:
- Developed Sparse Cholesky factorization LMM (Sci-LMM).
- Combined techniques from animal/plant breeding and human genetics literature.
- Constructed relationship matrices for trillions of individuals and fitted LMMs.
Main Results:
- Sci-LMM can fit LMMs for population-scale pedigrees in hours.
- Successfully estimated heritability of longevity and reproductive fitness.
- Demonstrated capabilities through simulation studies and analysis of a large historical pedigree.
Conclusions:
- Sci-LMM offers a unified framework for analyzing population-scale human family trees.
- Enables large-scale epidemiological and sociological studies using genealogical data.
- Provides new insights into the heritability of human traits over historical timescales.
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