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Parent-of-Origin inference for biobanks

Author

Listed:
  • Robin J. Hofmeister

    (University of Lausanne
    Swiss Institute of Bioinformatics (SIB))

  • Simone Rubinacci

    (University of Lausanne
    Swiss Institute of Bioinformatics (SIB))

  • Diogo M. Ribeiro

    (University of Lausanne
    Swiss Institute of Bioinformatics (SIB))

  • Alfonso Buil

    (Copenhagen University Hospital
    University of Copenhagen)

  • Zoltán Kutalik

    (University of Lausanne
    Swiss Institute of Bioinformatics (SIB)
    University Center for Primary Care and Public Health, University of Lausanne)

  • Olivier Delaneau

    (University of Lausanne
    Swiss Institute of Bioinformatics (SIB))

Abstract

Identical genetic variations can have different phenotypic effects depending on their parent of origin. Yet, studies focusing on parent-of-origin effects have been limited in terms of sample size due to the lack of parental genomes or known genealogies. We propose a probabilistic approach to infer the parent-of-origin of individual alleles that does not require parental genomes nor prior knowledge of genealogy. Our model uses Identity-By-Descent sharing with second- and third-degree relatives to assign alleles to parental groups and leverages chromosome X data in males to distinguish maternal from paternal groups. We combine this with robust haplotype inference and haploid imputation to infer the parent-of-origin for 26,393 UK Biobank individuals. We screen 99 phenotypes for parent-of-origin effects and replicate the discoveries of 6 GWAS studies, confirming signals on body mass index, type 2 diabetes, standing height and multiple blood biomarkers, including the known maternal effect at the MEG3/DLK1 locus on platelet phenotypes. We also report a novel maternal effect at the TERT gene on telomere length, thereby providing new insights on the heritability of this phenotype. All our summary statistics are publicly available to help the community to better characterize the molecular mechanisms leading to parent-of-origin effects and their implications for human health.

Suggested Citation

  • Robin J. Hofmeister & Simone Rubinacci & Diogo M. Ribeiro & Alfonso Buil & Zoltán Kutalik & Olivier Delaneau, 2022. "Parent-of-Origin inference for biobanks," Nature Communications, Nature, vol. 13(1), pages 1-15, December.
  • Handle: RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-34383-6
    DOI: 10.1038/s41467-022-34383-6
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    References listed on IDEAS

    as
    1. Simone Rubinacci & Olivier Delaneau & Jonathan Marchini, 2020. "Genotype imputation using the Positional Burrows Wheeler Transform," PLOS Genetics, Public Library of Science, vol. 16(11), pages 1-19, November.
    2. Olivier Delaneau & Jean-François Zagury & Matthew R. Robinson & Jonathan L. Marchini & Emmanouil T. Dermitzakis, 2019. "Accurate, scalable and integrative haplotype estimation," Nature Communications, Nature, vol. 10(1), pages 1-10, December.
    3. Olivier Delaneau & Jonathan Marchini, 2014. "Integrating sequence and array data to create an improved 1000 Genomes Project haplotype reference panel," Nature Communications, Nature, vol. 5(1), pages 1-9, September.
    4. Clare Bycroft & Colin Freeman & Desislava Petkova & Gavin Band & Lloyd T. Elliott & Kevin Sharp & Allan Motyer & Damjan Vukcevic & Olivier Delaneau & Jared O’Connell & Adrian Cortes & Samantha Welsh &, 2018. "The UK Biobank resource with deep phenotyping and genomic data," Nature, Nature, vol. 562(7726), pages 203-209, October.
    5. Stefania Benonisdottir & Asmundur Oddsson & Agnar Helgason & Ragnar P. Kristjansson & Gardar Sveinbjornsson & Arna Oskarsdottir & Gudmar Thorleifsson & Olafur B. Davidsson & Gudny A. Arnadottir & Gera, 2016. "Epigenetic and genetic components of height regulation," Nature Communications, Nature, vol. 7(1), pages 1-10, December.
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