Modeling Parent-Specific Genetic Nurture in Families with Missing Parental Genotypes: Application to Birthweight and BMI

Abstract

Disaggregation and estimation of genetic effects from offspring and parents has long been of interest to statistical geneticists. Recently, technical and methodological advances have made the genome-wide and loci-specific estimation of direct offspring and parental genetic nurture effects more possible. However, unbiased estimation using these methods requires datasets where both parents and at least one child have been genotyped, which are relatively scarce. Our group has recently developed a method and accompanying software (IMPISH; Hwang et al. in PLoS Genet 16:e1009154, 2020) which is able to impute missing parental genotypes from observed data on sibships and estimate their effects on an offspring phenotype conditional on the effects of genetic transmission. However, this method is unable to disentangle maternal and paternal effects, which may differ in magnitude and direction. Here, we introduce an extension to the original IMPISH routine which takes advantage of all available nuclear families to impute parent-specific missing genotypes and obtain asymptotically unbiased estimates of genetic effects on offspring phenotypes. We apply this this method to data from related individuals in the UK Biobank, showing concordance with previous estimates of maternal genetic effects on offspring birthweight. We also conduct the first GWAS jointly estimating offspring-, maternal-, and paternal-specific genetic effects on body-mass index.

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References

  1. Adan RAH, Vanderschuren LJMJ, la Fleur SE (2008) Anti-obesity drugs and neural circuits of feeding. Trends Pharmacol Sci 29:208–217

    Article  Google Scholar 

  2. Arnold M, Raffler J, Pfeufer A et al (2015) SNiPA: an interactive, genetic variant-centered annotation browser. Bioinformatics 31:1334–1336. https://doi.org/10.1093/bioinformatics/btu779

    Article  PubMed  Google Scholar 

  3. Bates D, Mächler M, Bolker B, Walker S (2015) Fitting linear mixed-effects models using lme4. J Stat Softw 67:1–48. https://doi.org/10.18637/jss.v067.i01

    Article  Google Scholar 

  4. Bates TC, Maher BS, Medland SE et al (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 21:73–83. https://doi.org/10.1017/thg.2018.11

    Article  PubMed  Google Scholar 

  5. Beaumont RN, Warrington NM, Cavadino A et al (2018) Genome-wide association study of offspring birth weight in 86 577 women identifies five novel loci and highlights maternal genetic effects that are independent of fetal genetics. Hum Mol Genet 18:742–756. https://doi.org/10.1093/hmg/ddx429

    Article  Google Scholar 

  6. Bergin JE, Neale MC, Eaves LJ et al (2012) Genetic and environmental transmission of body mass index fluctuation. Behav Genet 42:867–874. https://doi.org/10.1007/s10519-012-9567-5

    Article  PubMed  PubMed Central  Google Scholar 

  7. Bycroft C, Freeman C, Petkova D et al (2018) The UK Biobank resource with deep phenotyping and genomic data. Nature 562:203–209. https://doi.org/10.1038/s41586-018-0579-z

    Article  PubMed  PubMed Central  Google Scholar 

  8. Chang CC, Chow CC, Tellier LC et al (2015) Second-generation PLINK: rising to the challenge of larger and richer datasets. Gigascience 4:7. https://doi.org/10.1186/s13742-015-0047-8

    Article  PubMed  PubMed Central  Google Scholar 

  9. Csardi G, Nepusz T (2006) The igraph software package for complex network research. InterJ Complex Syst 1695:1–9

    Google Scholar 

  10. Eilertsen EM, Jami ES, McAdams T et al (2020) Direct and indirect effects of maternal, paternal, and offspring genotypes: Trio-GCTA. bioRxiv. https://doi.org/10.1101/2020.05.15.097840

    Article  Google Scholar 

  11. Hwang L-D, Tubbs JD, Luong J et al (2020) Estimating indirect parental genetic effects on offspring phenotypes using virtual parental genotypes derived from sibling and half sibling pairs. PLOS Genet 16:e1009154. https://doi.org/10.1371/journal.pgen.1009154

    Article  PubMed  PubMed Central  Google Scholar 

  12. Kong A, Thorleifsson G, Frigge ML et al (2018) The nature of nurture: effects of parental genotypes. Science 359:424–428. https://doi.org/10.1126/science.aan6877

    Article  PubMed  Google Scholar 

  13. Kong L, Nilsson IAK, Gissler M, Lavebratt C (2019) Associations of maternal diabetes and body mass index with offspring birth weight and prematurity. JAMA Pediatr 173:371–378. https://doi.org/10.1001/jamapediatrics.2018.5541

    Article  PubMed  PubMed Central  Google Scholar 

  14. Kong A, Benonisdottir S, Young A (2020) Family analysis with mendelian imputations. bioRxiv. https://doi.org/10.1101/2020.07.02.185181

    Article  PubMed  PubMed Central  Google Scholar 

  15. Manichaikul A, Mychaleckyj JC, Rich SS et al (2010) Robust relationship inference in genome-wide association studies. Bioinformatics 26:2867–2873. https://doi.org/10.1093/bioinformatics/btq559

    Article  PubMed  PubMed Central  Google Scholar 

  16. McAdams TA, Neiderhiser JM, Rijsdijk FV et al (2014) Accounting for genetic and environmental confounds in associations between parent and child characteristics: a systematic review of children-of-twins studies. Psychol Bull 140:1138–1173. https://doi.org/10.1037/a0036416

    Article  PubMed  Google Scholar 

  17. Meister B (2007) Neurotransmitters in key neurons of the hypothalamus that regulate feeding behavior and body weight. Physiol Behav 92:263–271. https://doi.org/10.1016/j.physbeh.2007.05.021

    Article  PubMed  Google Scholar 

  18. Moen GH, Hemani G, Warrington NM, Evans DM (2019) Calculating power to detect maternal and offspring genetic effects in genetic association studies. Behav Genet 49:327–339. https://doi.org/10.1007/s10519-018-9944-9

    Article  PubMed  Google Scholar 

  19. Ornoy A (2011) Prenatal origin of obesity and their complications: gestational diabetes, maternal overweight and the paradoxical effects of fetal growth restriction and macrosomia. Reprod Toxicol 32:205–212. https://doi.org/10.1016/j.reprotox.2011.05.002

    Article  PubMed  Google Scholar 

  20. R Core Team (2019) R: a language and environment for statistical computing. R Core Team, Vienna

    Google Scholar 

  21. Stamatakis AM, Van Swieten M, Basiri ML et al (2016) Lateral hypothalamic area glutamatergic neurons and their projections to the lateral habenula regulate feeding and reward. J Neurosci 36:302–311. https://doi.org/10.1523/JNEUROSCI.1202-15.2016

    Article  PubMed  PubMed Central  Google Scholar 

  22. Straker L, Mountain J, Jacques A et al (2017) Cohort profile: the Western Australian pregnancy cohort (RAINE) study-generation 2. Int J Epidemiol 46:1384J-1385J. https://doi.org/10.1093/ije/dyw308

    Article  Google Scholar 

  23. Tanaka M, Delorey TM, Delgado-Escueta AV, Olsen RW (2010) GABRB3, epilepsy, and neurodevelopment. Epilepsia 51:77. https://doi.org/10.1111/j.1528-1167.2010.02863.x

    Article  Google Scholar 

  24. Tubbs JD, Porsch RM, Cherny SS, Sham PC (2020a) The genes we inherit and those we don’t: maternal genetic nurture and child BMI trajectories. Behav Genet. https://doi.org/10.1007/s10519-020-10008-w

    Article  PubMed  Google Scholar 

  25. Tubbs JD, Zhang YD, Sham PC (2020b) Intermediate confounding in trio relationships: the importance of complete data in effect size estimation. Genet Epidemiol 44:395–399. https://doi.org/10.1002/gepi.22294

    Article  PubMed  Google Scholar 

  26. Tyrrell JS, Yaghootkar H, Freathy RM et al (2013) Parental diabetes and birthweight in 236 030 individuals in the UK Biobank study. Int J Epidemiol 42:1714–1723. https://doi.org/10.1093/ije/dyt220

    Article  PubMed  PubMed Central  Google Scholar 

  27. Warrington NM, Freathy RM, Neale MC, Evans DM (2018) Using structural equation modelling to jointly estimate maternal and fetal effects on birthweight in the UK Biobank. Int J Epidemiol 47:1229–1241. https://doi.org/10.1093/ije/dyy015

    Article  PubMed  PubMed Central  Google Scholar 

  28. Warrington NM, Beaumont RN, Horikoshi M et al (2019) Maternal and fetal genetic effects on birth weight and their relevance to cardio-metabolic risk factors. Nat Genet 51:804–814. https://doi.org/10.1038/s41588-019-0403-1

    Article  PubMed  PubMed Central  Google Scholar 

  29. Wright J, Small N, Raynor P et al (2013) Cohort profile: the born in bradford multi-ethnic family cohort study. Int J Epidemiol 42:978–991. https://doi.org/10.1093/ije/dys112

    Article  PubMed  Google Scholar 

  30. Xue A, Wu Y, Zhu Z et al (2018) Genome-wide association analyses identify 143 risk variants and putative regulatory mechanisms for type 2 diabetes. Nat Commun. https://doi.org/10.1038/s41467-018-04951-w

    Article  PubMed  PubMed Central  Google Scholar 

  31. Yengo L, Sidorenko J, Kemper KE et al (2018) Meta-analysis of genome-wide association studies for height and body mass index in ∼700000 individuals of European ancestry. Hum Mol Genet 27:3641–3649. https://doi.org/10.1093/hmg/ddy271

    Article  PubMed  PubMed Central  Google Scholar 

  32. Young A, Nehzati SM, Lee C et al (2020) Mendelian imputation of parental genotypes for genome-wide estimation of direct and indirect genetic effects. bioRxiv. https://doi.org/10.1101/2020.07.02.185199

    Article  PubMed  PubMed Central  Google Scholar 

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Funding

D.M.E. is funded by an Australian National Health and Medical Research Council Senior Research Fellowship (APP1137714) and NHMRC project Grants (GNT1125200, GNT1157714, GNT1183074).

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Correspondence to Pak C. Sham.

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Conflict of interest

Justin D. Tubbs, Liang-Dar Hwang, Justin Luong, David M. Evans and Pak C. Sham declare no conflicts of interest.

Ethical approval

This research has been conducted using the UK Biobank Resource under project ID number 28732. UK Biobank received ethical approval from the NHS National Research Ethics Service North West (11/NW/0382).

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Informed consent was obtained by UK Biobank researchers from all participants.

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Edited by Elizabeth Prom-Wormley.

Justin D. Tubbs and Liang-Dar Hwang are joint first authors.

David M. Evans and Pak C. Sham are joint senior authors.

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Tubbs, J.D., Hwang, LD., Luong, J. et al. Modeling Parent-Specific Genetic Nurture in Families with Missing Parental Genotypes: Application to Birthweight and BMI. Behav Genet (2021). https://doi.org/10.1007/s10519-020-10040-w

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Keywords

  • Statistical genetics
  • Imputation
  • Parental genetic effect
  • Genetic nurture
  • Birthweight
  • BMI