Assessing Genotype by Environment Interaction in Case of Heterogeneous Measurement Error

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Abstract

Considerable effort has been devoted to establish genotype by environment interaction (G E) in case of unmeasured genetic and environmental influences. Although it has been outlined by various authors that the appearance of G E can be dependent on properties of the given measurement scale, a non-biased method to assess G E is still lacking. We show that the incorporation of an explicit measurement model can remedy potential bias due to ceiling and floor effects. By means of a simulation study it is shown that the use of sum scores can lead to biased estimates whereas the proposed method is unbiased. The power of the suggested method is illustrated by means of a second simulation study with different sample sizes and G E effect sizes.
Original languageEnglish
Pages (from-to)394-406
JournalBehavior genetics
Volume44
Issue number4
DOIs
Publication statusPublished - 2014

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genotype
Genotype
Sample Size
simulation
methodology
method
sampling
effect
incorporation

Keywords

  • IR-91499
  • METIS-304569

Cite this

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Assessing Genotype by Environment Interaction in Case of Heterogeneous Measurement Error. / Schwabe, Inga; van den Berg, Stéphanie Martine.

In: Behavior genetics, Vol. 44, No. 4, 2014, p. 394-406.

Research output: Contribution to journalArticleAcademicpeer-review

TY - JOUR

T1 - Assessing Genotype by Environment Interaction in Case of Heterogeneous Measurement Error

AU - Schwabe, Inga

AU - van den Berg, Stéphanie Martine

PY - 2014

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AB - Considerable effort has been devoted to establish genotype by environment interaction (G E) in case of unmeasured genetic and environmental influences. Although it has been outlined by various authors that the appearance of G E can be dependent on properties of the given measurement scale, a non-biased method to assess G E is still lacking. We show that the incorporation of an explicit measurement model can remedy potential bias due to ceiling and floor effects. By means of a simulation study it is shown that the use of sum scores can lead to biased estimates whereas the proposed method is unbiased. The power of the suggested method is illustrated by means of a second simulation study with different sample sizes and G E effect sizes.

KW - IR-91499

KW - METIS-304569

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DO - 10.1007/s10519-014-9649-7

M3 - Article

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SP - 394

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JO - Behavior genetics

JF - Behavior genetics

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