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Direct and Indirect Effects of Maternal, Paternal, and Offspring Genotypes: Trio-GCTA

  • Espen Moen Eilertsen*
  • , Eshim Shahid Jami
  • , Tom A. McAdams
  • , Laurie J. Hannigan
  • , Alexandra S. Havdahl
  • , Per Magnus
  • , David M. Evans
  • , Eivind Ystrom
  • *Corresponding author for this work

Research output: Contribution to JournalArticleAcademicpeer-review

Abstract

Indirect genetic effects from relatives may result in misleading quantifications of heritability, but can also be of interest in their own right. In this paper we propose Trio-GCTA, a model for separating direct and indirect genetic effects when genome-wide single nucleotide polymorphism data have been collected from parent-offspring trios. The model is applicable to phenotypes obtained from any of the family members. We discuss appropriate parameter interpretations and apply the method to three exemplar phenotypes: offspring birth weight, maternal relationship satisfaction, and paternal body-mass index, using real data from the Norwegian Mother, Father and Child Cohort Study (MoBa).

Original languageEnglish
Pages (from-to)154-161
Number of pages8
JournalBehavior Genetics
Volume51
Early online date2 Jan 2021
DOIs
Publication statusPublished - Mar 2021

Bibliographical note

Publisher Copyright:
© 2021, The Author(s), under exclusive licence to Springer Science+Business Media, LLC part of Springer Nature.

Copyright:
Copyright 2021 Elsevier B.V., All rights reserved.

Funding

The Norwegian Mother, Father and Child Cohort Study is supported by the Norwegian Ministry of Health and Care Services and the Ministry of Education and Research. We are grateful to all the participating families in Norway who take part in this on-going cohort study. This publication is a part of the project “Intergenerational Transmission of Internalizing and Externalizing Psychopathological Spectra: A Genome-Wide Complex Trait Study” supported by the Research Council of Norway (262177). Espen Moen Eilertsen and Eivind Ystrom was supported by the Norwegian Research Council (262177 and 288083). Laurie Hannigan was supported by a grant from the South-Eastern Norway Regional Health Authority (2018059). Alexandra Havdahl was supported by the South-Eastern Norway Regional Health Authority (2018058 and 2020022). Tom A. McAdams was supported by a Sir Henry Dale Fellowship, jointly funded by the Wellcome Trust and the Royal Society (107706/Z/15/Z) and the Norwegian Research Council (288083). Eshim S. Jami was supported by the European Union’s Horizon 2020 research and innovation programme, Marie Sklodowska Curie Actions (721567). This work was partly supported by the Research Council of Norway through its Centres of Excellence funding scheme, project number 262700. We thank the Norwegian Institute of Public Health (NIPH) for generating high-quality genomic data. This research is part of the HARVEST collaboration, supported by the Research Council of Norway (229624). We further thank the Center for Diabetes Research, the University of Bergen for providing genotype data and performing quality control and imputation of the data funded by the ERC AdG project SELECTionPREDISPOSED, Stiftelsen Kristian Gerhard Jebsen, Trond Mohn Foundation, the Research Council of Norway, the Novo Nordisk Foundation, the University of Bergen, and the Western Norway health Authorities (Helse Vest).

FundersFunder number
Sir Henry Dale Fellowship
Haridus- ja Teadusministeerium
Stiftelsen Kristian Gerhard Jebsen
Trond Mohn stiftelse
ULB Center for Diabetes Research
Helse- og Omsorgsdepartementet
Western Norway health Authorities
Norwegian Institute of Public Health
Universitetet i Bergen
European Research Council
Novo Nordisk Fonden
Horizon 2020 Framework Programme721567
Royal Society107706/Z/15/Z
HORIZON EUROPE Marie Sklodowska-Curie Actions229624, 262700
Norges forskningsråd262177, 288083
Helse Sør-Øst RHF2018058, 2020022, 2018059
Wellcome Trust107706

    UN SDGs

    This output contributes to the following UN Sustainable Development Goals (SDGs)

    1. SDG 3 - Good Health and Well-being
      SDG 3 Good Health and Well-being

    Keywords

    • Gene–environment correlation
    • Indirect genetic effects
    • MoBa
    • Trio-GCTA
    • Within-family

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