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Estimating Population Standard Deviation01:26

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When the population standard deviation is unknown and the sample size is large, the sample standard deviation s is commonly used as a point estimate of σ. However, it can sometimes under or overestimate the population standard deviation. To overcome this drawback, confidence intervals are determined to estimate population parameters and eliminate any calculation bias accurately. However, this only applies to random samples from normally distributed populations. Knowing the sample mean and...
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A point estimate of the population mean is obtained from a single sample. Such a point estimate does not represent a population well because it needs to account for variability in the population. Single point estimate can also be biased despite the sample being selected randomly. Thus, a point estimate is often unreliable. A confidence interval is needed to reduce this unreliability.
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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.

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|May 10, 2019
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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.

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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.