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

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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).

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Code for estimating the model is available at https://github.com/espenmei/trio

References

  • Bates TC, Maher S, Medland SE, McAloney K, Wright MJ, Hansell NK, Kendler KS, Martin NG, Gillespie NA (2018) The nature of nurture: using a virtual-parent design to test parenting effects on children’s educational attainment in genotyped families. Twin Res Hum Genet 21(2):73–83

    Article  Google Scholar 

  • Bijma P (2014) The quantitative genetics of indirect genetic effects: a selective review of modelling issues. Heredity 112(1):61–69

    Article  Google Scholar 

  • Dyrdal GM, Røysamb E, Nes RB, Vittersø J (2011) Can a happy relationship predict a happy life? A populationbased study of maternal well-being during the life transition of pregnancy, infancy, and toddlerhood. J Happiness Stud 12(6):947–962

    Article  Google Scholar 

  • Eaves LJ, Pourcain BS, Smith GD, York TP, Evans DM (2014) Resolving the effects of maternal and offspring genotype on dyadic outcomes in genome wide complex trait analysis (“M-GCTA”). Behav Genet 44(5):445–455

    Article  Google Scholar 

  • Elks CE, Den Hoed M, Zhao JH, Sharp SJ, Wareham NJ, Loos RJF, Ong KK (2012) Variability in the heritability of body mass index: a systematic review and metaregression. Front Endocrinol 3:29

    Article  Google Scholar 

  • Evans DM, Moen G-H, Hwang L-D, Lawlor DA, Warrington NM (2019) Elucidating the role of maternal environmental exposures on offspring health and disease using two-sample Mendelian randomization. Int J Epidemiol 48(3):861–875

    Article  Google Scholar 

  • Helgeland Ø, Vaudel M, Juliusson PB, Holmen OL, Juodakis J, Bacelis J, Jacobsson B, Lindekleiv H, Hveem K, Lie RT et al (2019) Genome-wide association study reveals dynamic role of genetic variation in infant and early childhood growth. Nat Commun 10(1):1–10

    Article  Google Scholar 

  • Kong A, Thorleifsson G, Frigge ML, Vilhjalmsson BJ, Young AI, Thorgeirsson TE, Benonisdottir S, Oddsson A, Halldorsson BV, Masson G et al (2018) The nature of nurture: effects of parental genotypes. Science 359(6374):424–428

    Article  Google Scholar 

  • Laurin C, Cuellar-Partida G, Hemani G, Smith GD, Yang J, Evans DM (2018) Partitioning phenotypic variance due to parent-of-origin effects using genomic relatedness matrices. Behav Genet 48(1):67–79

    Article  Google Scholar 

  • Lunde A, Melve KK, Gjessing HK, Skjærven R, Irgens LM (2007) Genetic and environmental in uences on birth weight, birth length, head circumference, and gestational age by use of population-based parent-offspring data. Am J Epidemiol 165(7):734–741

    Article  Google Scholar 

  • Lynch M, Walsh B et al (1998) Genetics and analysis of quantitative traits, vol 1. Sinauer, Sunderland

    Google Scholar 

  • Maes HHM, Neale MC, Eaves LJ (1997) Genetic and environmental factors in relative body weight and human adiposity. Behav Genet 27(4):325–351

    Article  Google Scholar 

  • Magnus P (1984) Causes of variation in birth weight: a study of offspring of twins. Clin Genet 25(1):15–24

    Article  Google Scholar 

  • Magnus P, Birke C, Vejrup K, Haugan A, Alsaker E, Daltveit AK, Handal M, Haugen M, Høiseth G, Knudsen GP et al (2016) Cohort profile update: the Norwegian mother and child cohort study (MoBa). Int J Epidemiol 45(2):382–388

    Article  Google Scholar 

  • McAdam AG, Dany G, Wilson AJ (2014) The effects of others’ genes: maternal and other indirect genetic effects. Quant Genet Wild. https://doi.org/10.1093/acprof:oso/9780199674237.003.0006

    Article  Google Scholar 

  • McAdams TA, Neiderhiser JM, Rijsdijk FV, Narusyte J, Lichtenstein P, Eley TC (2014) Accounting for genetic and environmental confounds in associations between parent and child characteristics: a systematic review of children-of-twins studies. Psychol Bull 140(4):1138

    Article  Google Scholar 

  • Neale MCCL, Cardon LR (2013) Methodology for genetic studies of twins and families, vol 67. Springer, New York

    Google Scholar 

  • Neale MC, Hunter MD, Pritikin JN, Zahery M, Brick TR, Kirkpatrick RM, Estabrook R, Bates TC, Maes HH, Boker SM (2016) OpenMx 2.0: extended structural equation and statistical modeling. Psychometrika 81(2):535–549. https://doi.org/10.1007/s11336-014-9435-8

    Article  PubMed  Google Scholar 

  • Polderman TJC, Benyamin B, De Leeuw CA, Sullivan PF, Van Bochoven A, Visscher PM, Posthuma D (2015) Meta-analysis of the heritability of human traits based on fifty years of twin studies. Nat Genet 47(7):702

    Article  Google Scholar 

  • Qiao Z, Zheng J, Helgeland Ø, Vaudel M, Johansson S, Njølstad PR, Smith GD, Warrington NM, Evans DM (2020) Introducing M-GCTA a software package to estimate maternal (or paternal) genetic effects on offspring phenotypes. Behav Genet 50(1):51–66

    Article  Google Scholar 

  • R Core Team (2019) R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna. https://www.R-project.org/

  • Røysamb E, Vittersø J, Tambs K (2014) The relationship satisfaction scale-psychometric properties

  • South SC, Krueger RF, Elkins IJ, Iacono WG, McGue M (2016) Romantic relationship satisfaction moderates the etiology of adult personality. Behav Genet 46(1):124–142

    Article  Google Scholar 

  • Visscher PM, Hemani G, Vinkhuyzen AAE, Chen G-B, Lee SH, Wray NR, Goddard ME, Yang J (2014) Statistical power to detect genetic (co) variance of complex traits using SNP data in unrelated samples. PLoS Genet. https://doi.org/10.1371/journal.pgen.1004269

    Article  PubMed  PubMed Central  Google Scholar 

  • Warrington NM, Beaumont RN, Horikoshi M, Day FR, Helgeland Ø, Laurin C, Bacelis J, Peng S, Hao K, Feenstra B et al (2019) Maternal and fetal genetic effects on birth weight and their relevance to cardio-metabolic risk factors. Nat Genet 51(5):804–814

    Article  Google Scholar 

  • Yang J, Bakshi A, Zhu Z, Hemani G, Vinkhuyzen AAE, Lee SH, Robinson MR, Perry JRB, Nolte IM, van Vliet-Ostaptchouk JV et al (2015) Genetic variance estimation with imputed variants finds negligible missing heritability for human height and body mass index. Nat Genet 47(10):1114

    Article  Google Scholar 

  • Yang J, Benyamin B, McEvoy BP, Gordon S, Henders AK, Nyholt DR, Madden PA, Heath AC, Martin NG, Montgomery GW et al (2010) Common SNPs explain a large proportion of the heritability for human height. Nat Genet 42(7):565

    Article  Google Scholar 

  • Yang J, Hong Lee S, Goddard ME, Visscher PM (2011) GCTA: a tool for genome-wide complex trait analysis. Am J Hum Genet 88(1):76–82

    Article  Google Scholar 

  • Yang J, Hong Lee S, Wray NR, Goddard ME, Visscher PM (2016) GCTA-GREML accounts for linkage disequilibrium when estimating genetic variance from genome-wide SNPs. Proc Natll Acad Sci 113(32):E4579–E4580

    Article  Google Scholar 

  • Yang J, Lee SH, Goddard ME, Visscher PM (2013) Genome-wide complex trait analysis (GCTA): methods, data analyses, and interpretations. Genome-wide association studies and genomic prediction. Springer, New York, pp 215–236

    Chapter  Google Scholar 

  • Yang J, Zeng J, Goddard ME, Wray NR, Visscher PM (2017) Concepts, estimation and interpretation of SNP-based heritability. Nat Genet 49(9):1304

    Article  Google Scholar 

  • York TP, Eaves LJ, Lichtenstein P, Neale MC, Svensson A, Latendresse S, Långström N, Strauss III JF (2013) Fetal and maternal genes’ in uence on gestational age in a quantitative genetic analysis of 244,000 Swedish births. Am J Epidemiol 178(4):543–550

    Article  Google Scholar 

  • York TP, Strauss JF, Neale MC, Eaves LJ (2009) Estimating fetal and maternal genetic contributions to premature birth from multiparous pregnancy histories of twins using MCMC and maximum-likelihood approaches. Twin Res Hum Genet 12(4):333–342

    Article  Google Scholar 

  • Young AI (2019) Solving the missing heritability problem. PLoS Genet 15(6):e1008222

    Article  Google Scholar 

  • Young AI, Benonisdottir S, Przeworski M, Kong A (2019) Deconstructing the sources of genotype–phenotype associations in humans. Science 365(6460):1396–1400

    Article  Google Scholar 

  • Young AI, Frigge ML, Gudbjartsson DF, Thorleifsson G, Bjornsdottir G, Sulem P, Masson G, Thorsteinsdottir U, Stefansson K, Kong A (2018) Relatedness disequilibrium regression estimates heritability without environmental bias. Nat Genet 50(9):1304–1310

    Article  Google Scholar 

  • Zhu Z, Bakshi A, Vinkhuyzen AAE, Hemani G, Lee SH, Nolte IM, van Vliet-Ostaptchouk JV, Snieder H, Esko T, Milani L et al (2015) Dominance genetic variation contributes little to the missing heritability for human complex traits. Am J Hum Genet 96(3):377–385

    Article  Google Scholar 

Download references

Acknowledgements

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).

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Correspondence to Espen Moen Eilertsen.

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Espen Moen Eilertsen, Eshim Shahid Jami, Tom A. McAdams, Laurie J. Hannigan, Alexandra S. Havdahl, Per Magnus, David M. Evans, and Eivind Ystrom declare that they have no conflict of interest.

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Eilertsen, E.M., Jami, E.S., McAdams, T.A. et al. Direct and Indirect Effects of Maternal, Paternal, and Offspring Genotypes: Trio-GCTA. Behav Genet 51, 154–161 (2021). https://doi.org/10.1007/s10519-020-10036-6

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