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Introducing M-GCTA a Software Package to Estimate Maternal (or Paternal) Genetic Effects on Offspring Phenotypes

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Abstract

There is increasing interest within the genetics community in estimating the relative contribution of parental genetic effects on offspring phenotypes. Here we describe the user-friendly M-GCTA software package used to estimate the proportion of phenotypic variance explained by maternal (or alternatively paternal) and offspring genotypes on offspring phenotypes. The tool requires large studies where genome-wide genotype data are available on mother- (or alternatively father-) offspring pairs. The software includes several options for data cleaning and quality control, including the ability to detect and automatically remove cryptically related pairs of individuals. It also allows users to construct genetic relationship matrices indexing genetic similarity across the genome between parents and offspring, enabling the estimation of variance explained by maternal (or alternatively paternal) and offspring genetic effects. We evaluated the performance of the software using a range of data simulations and estimated the computing time and memory requirements. We demonstrate the use of M-GCTA on previously analyzed birth weight data from two large population based birth cohorts, the Avon Longitudinal Study of Parents and Children (ALSPAC) and the Norwegian Mother and Child Cohort Study (MoBa). We show how genetic variation in birth weight is predominantly explained by fetal genetic rather than maternal genetic sources of variation.

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Acknowledgements

We are extremely grateful to all the families who took part in ALSPAC, the midwives for their help in recruiting them, and the whole ALSPAC team, which includes interviewers, computer and laboratory technicians, clerical workers, research scientists, volunteers, managers, receptionists and nurses. A comprehensive list of grants funding is available on the ALSPAC website (https://www.bristol.ac.uk/alspac/external/documents/grant-acknowledgements.pdf). The UK Medical Research Council and the Wellcome Trust (Grant ref: 102215/2/13/2) and the University of Bristol provide core support for ALSPAC. The ALSPAC GWAS data were generated by Sample Logistics and Genotyping Facilities at the Wellcome Trust Sanger Institute and LabCorp (Laboratory Corporation of America) using support from 23andMe. This publication is the work of the authors and DME will serve as guarantor for the contents of this paper.

Funding

NMW is supported by a National Health and Medical Research Council Early Career Fellowship (Grant No. ECF1104818). This work and DME are supported by an NHMRC Senior Research Fellowship (Grant No. SRF1137714) and NHMRC project grants (Grant Nos. GNT1085159, GNT1085130, GNT1125141, GNT1125200, GNT1157714). This work was supported by grants (to PRN) from the European Research Council (Grant No. AdG #293574), the Bergen Research Foundation (“Utilizing the Mother and Child Cohort and the Medical Birth Registry for Better Health”), Stiftelsen Kristian Gerhard Jebsen (Translational Medical Center), the University of Bergen, the Research Council of Norway (FRIPRO Grant #240413), the Western Norway Regional Health Authority (Strategic Fund “Personalized Medicine for Children and Adults”), and the Norwegian Diabetes Foundation; and (to SJ) Helse Vest's Open Research Grant.

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Authors and Affiliations

  1. University of Queensland Diamantina Institute, University of Queensland, Brisbane, QLD, Australia

    Zhen Qiao, Nicole M. Warrington & David M. Evans

  2. Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol, BS8 2BN, UK

    Jie Zheng, George Davey Smith & David M. Evans

  3. Bristol Medical School, Population Health Sciences, University of Bristol, Bristol, UK

    Jie Zheng, George Davey Smith & David M. Evans

  4. KG Jebsen Center for Diabetes Research, Department of Clinical Science, University of Bergen, Bergen, Norway

    Øyvind Helgeland, Marc Vaudel, Stefan Johansson & Pål R. Njølstad

  5. Department of Genetics and Bioinformatics, Health Data and Digitalization, Norwegian Institute of Public Health, Oslo, Norway

    Øyvind Helgeland

  6. Department of Medical Genetics, Haukeland University Hospital, Bergen, Norway

    Stefan Johansson

  7. Department of Pediatrics and Adolescents, Haukeland University Hospital, Bergen, Norway

    Pål R. Njølstad

  8. K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Trondheim, Norway

    Nicole M. Warrington

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  1. Zhen Qiao
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  2. Jie Zheng
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  3. Øyvind Helgeland
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  4. Marc Vaudel
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  7. George Davey Smith
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  8. Nicole M. Warrington
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  9. David M. Evans
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Correspondence to David M. Evans.

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Zhen Qiao, Jie Zheng, Øyvind Helgeland, Marc Vaudel, Stefan Johansson, Pål Njølstad, George Davey Smith, Nicole Warrington and David Evans declare that they have no conflict of interest.

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Ethical approval was obtained from the ALSPAC Law and Ethics Committee, MoBa Ethics Board and other relevant ethics committees. All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

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Qiao, Z., Zheng, J., Helgeland, Ø. et al. Introducing M-GCTA a Software Package to Estimate Maternal (or Paternal) Genetic Effects on Offspring Phenotypes. Behav Genet 50, 51–66 (2020). https://doi.org/10.1007/s10519-019-09969-4

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