Testing the key assumption of heritability estimates based on genome-wide genetic relatedness

Dalton Conley, Mark L. Siegal, Benjamin W. Domingue, Kathleen Mullan Harris, Matthew B. McQueen, Jason D. Boardman

Research output: Contribution to journalArticle

Abstract

Comparing genetic and phenotypic similarity among unrelated individuals seems a promising way to quantify the genetic component of traits while avoiding the problematic assumptions plaguing twin-and other kin-based estimates of heritability. One approach uses a Genetic Relatedness Estimation through Maximum Likelihood (GREML) model for individuals who are related at less than 0.025 to predict their phenotypic similarity by their genetic similarity. Here we test the key underlying assumption of this approach: that genetic relatedness is orthogonal to environmental similarity. Using data from the Health and Retirement Study (and two other surveys), we show two unrelated individuals may be more likely to have been reared in a similar environment (urban versus nonurban setting) if they are genetically similar. This effect is not eliminated by controls for population structure. However, when we include this environmental confound in GREML models, heritabilities do not change substantially and thus potential bias in estimates of most biological phenotypes is probably minimal.

Original languageEnglish (US)
Pages (from-to)342-345
Number of pages4
JournalJournal of Human Genetics
Volume59
Issue number6
DOIs
StatePublished - Jun 2014

Keywords

  • GREML
  • environmental confound
  • heritability

ASJC Scopus subject areas

  • Genetics
  • Genetics(clinical)

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