Analysis of Heritability Using Genome‐Wide Data

Jacob B. Hall1, William S. Bush1

1 Institute for Computational Biology, Case Western Reserve University, Cleveland, Ohio
Publication Name:  Current Protocols in Human Genetics
Unit Number:  Unit 1.30
DOI:  10.1002/cphg.25
Online Posting Date:  October, 2016
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Abstract

Most analyses of genome‐wide association data consider each variant independently without considering or adjusting for the genetic background present in the rest of the genome. New approaches to genome analysis use representations of genomic sharing to better account for confounding factors like population stratification or to directly approximate heritability through the estimated sharing of individuals in a dataset. These approaches use mixed linear models, which relate genotypic sharing to phenotypic sharing, and rely on the efficient computation of genetic sharing among individuals in a dataset. This unit describes the principles and practical application of mixed models for the analysis of genome‐wide association study data. © 2016 by John Wiley & Sons, Inc.

Keywords: mixed‐model analysis; heritability; GCTA

     
 
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Table of Contents

  • Key Concepts
  • Estimating Genetic Relationship Matrices
  • Mixed‐Model Analyses
  • Estimating Trait Variance Explained Using GRMs
  • Discussion of Strategies
  • Summary
  • Acknowledgments
  • Literature Cited
  • Figures
  • Tables
     
 
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Materials

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Literature Cited

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