title: Analysis of Heritability Using Genome-Wide Data.
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.
authors.affiliation: Institute for Computational Biology, Case Western Reserve University, Cleveland, Ohio.
authors.forename: Jacob B
authors.initials: JB
authors.lastname: Hall
authors.affiliation: Institute for Computational Biology, Case Western Reserve University, Cleveland, Ohio.
authors.forename: William S
authors.initials: WS
authors.lastname: Bush
mesh_terms: D015894:Genome, Human / Q000235:genetics*; D055106:Genome-Wide Association Study / Q000379:methods*; D023281:Genomics; D005838:Genotype; D040941:Heredity; D006801:Humans; D016014:Linear Models; D010641:Phenotype
keywords: GCTA; heritability; mixed-model analysis