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 - 2014

Fingerprint

Retirement
Genome
Phenotype
Health
Population
Surveys and Questionnaires

Keywords

  • environmental confound
  • GREML
  • heritability

ASJC Scopus subject areas

  • Genetics(clinical)
  • Genetics
  • Medicine(all)

Cite this

Testing the key assumption of heritability estimates based on genome-wide genetic relatedness. / Conley, Dalton; Siegal, Mark L.; Domingue, Benjamin W.; Mullan Harris, Kathleen; McQueen, Matthew B.; Boardman, Jason D.

In: Journal of Human Genetics, Vol. 59, No. 6, 2014, p. 342-345.

Research output: Contribution to journalArticle

Conley, D, Siegal, ML, Domingue, BW, Mullan Harris, K, McQueen, MB & Boardman, JD 2014, 'Testing the key assumption of heritability estimates based on genome-wide genetic relatedness', Journal of Human Genetics, vol. 59, no. 6, pp. 342-345. https://doi.org/10.1038/jhg.2014.14
Conley, Dalton ; Siegal, Mark L. ; Domingue, Benjamin W. ; Mullan Harris, Kathleen ; McQueen, Matthew B. ; Boardman, Jason D. / Testing the key assumption of heritability estimates based on genome-wide genetic relatedness. In: Journal of Human Genetics. 2014 ; Vol. 59, No. 6. pp. 342-345.
@article{8cc152d7f3804846b8f6c612048880bc,
title = "Testing the key assumption of heritability estimates based on genome-wide genetic relatedness",
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.",
keywords = "environmental confound, GREML, heritability",
author = "Dalton Conley and Siegal, {Mark L.} and Domingue, {Benjamin W.} and {Mullan Harris}, Kathleen and McQueen, {Matthew B.} and Boardman, {Jason D.}",
year = "2014",
doi = "10.1038/jhg.2014.14",
language = "English (US)",
volume = "59",
pages = "342--345",
journal = "Journal of Human Genetics",
issn = "1434-5161",
publisher = "Nature Publishing Group",
number = "6",

}

TY - JOUR

T1 - Testing the key assumption of heritability estimates based on genome-wide genetic relatedness

AU - Conley, Dalton

AU - Siegal, Mark L.

AU - Domingue, Benjamin W.

AU - Mullan Harris, Kathleen

AU - McQueen, Matthew B.

AU - Boardman, Jason D.

PY - 2014

Y1 - 2014

N2 - 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.

AB - 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.

KW - environmental confound

KW - GREML

KW - heritability

UR - http://www.scopus.com/inward/record.url?scp=84903129497&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84903129497&partnerID=8YFLogxK

U2 - 10.1038/jhg.2014.14

DO - 10.1038/jhg.2014.14

M3 - Article

C2 - 24599117

AN - SCOPUS:84903129497

VL - 59

SP - 342

EP - 345

JO - Journal of Human Genetics

JF - Journal of Human Genetics

SN - 1434-5161

IS - 6

ER -