Open AccessAnalysis of Heritability Using Genome‐Wide Data
Author(s) -
Hall Jacob B.,
Bush William S.
Publication year - 2016
Publication title -
current protocols in human genetics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.282
H-Index - 30
eISSN - 1934-8258
pISSN - 1934-8266
DOI - 10.1002/cphg.25
Subject(s) - heritability , population stratification , genome wide association study , genome , missing heritability problem , genetic association , confounding , biology , population , computational biology , computer science , genetics , genotype , statistics , genetic variants , mathematics , gene , single nucleotide polymorphism , medicine , environmental health
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.