Abstract
Recently, methods have been introduced using polygenic scores (PGS) to estimate the effects of genetic nurture, the environmentally-mediated effects of parental genotypes on the phenotype of their child above and beyond the effects of the alleles which are transmitted to the child. We introduce a simplified model for estimating genetic nurture effects and show, through simulation and analytical derivation, that our method provides unbiased estimates and offers an increase in power to detect genetic nurture of up to 1/3 greater than that of previous methods. Subsequently, we apply this method to data from the Avon Longitudinal Study of Parents and Children to estimate the effects of maternal genetic nurture on childhood body mass index (BMI) trajectories. Through mixed modeling, we observe a statistically significant age-dependent effect of maternal PGS on child BMI, such that the influence of maternal genetic nurture appears to increase throughout development.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Allegrini AG, Selzam S, Rimfeld K, von Stumm S, Pingault JB, Plomin R (2019) Genomic prediction of cognitive traits in childhood and adolescence. Mol Psychiatry 24(6):819–827. https://doi.org/10.1038/s41380-019-0394-4
Bates D, Mächler M, Bolker B, Walker S (2015) Fitting linear mixed-effects models using lme4. J Stat Softw 67(1):1–48. https://doi.org/10.18637/jss.v067.i01
Bates TC, Maher BS, Medland SE, McAloney K, Wright MJ, Hansell NK 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(2):73–83. https://doi.org/10.1017/thg.2018.11
Bergin JE, Neale MC, Eaves LJ, Martin NG, Heath AC, Maes HH (2012) Genetic and environmental transmission of body mass index fluctuation. Behav Genet 42(6):867–874. https://doi.org/10.1007/s10519-012-9567-5
Boyd A, Golding J, Macleod J, Lawlor DA, Fraser A, Henderson J et al (2013) Cohort profile: the ‘Children of the 90s’—the index offspring of the Avon Longitudinal Study of Parents and Children. Int J Epidemiol 42(1):111–127. https://doi.org/10.1093/ije/dys064
Branigan AR, McCallum KJ, Freese J (2013) Variation in the heritability of educational attainment: an international meta-analysis. Soc Forces 92(1):109–140. https://doi.org/10.1093/sf/sot076
Cavalli-Sforza LL, Feldman MW (1973) Cultural versus biological inheritance: phenotypic transmission from parents to children (a theory of the effect of parental phenotypes on children’s phenotypes). Am J Hum Genet 25(6):618–637. http://www.ncbi.nlm.nih.gov/pubmed/4797967
Chang CC, Chow CC, Tellier LC, Vattikuti S, Purcell SM, Lee JJ (2015) Second-generation PLINK: rising to the challenge of larger and richer datasets. GigaScience 4(1):7. https://doi.org/10.1186/s13742-015-0047-8
Eaves L (1976) The effect of cultural transmission on continuous variation. Heredity 37(1):41–57. https://doi.org/10.1038/hdy.1976.64
Eaves LJ, St Pourcain B, 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. https://doi.org/10.1007/s10519-014-9666-6
Elks CE, 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 meta-regression. Front Endocrinol 3(FEB):29. https://doi.org/10.3389/fendo.2012.00029
Evans DM, Moen GH, Hwang LD, 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. https://doi.org/10.1093/ije/dyz019
Felix JF, Bradfield JP, Monnereau C, van der Valk RJP, Stergiakouli E, Chesi A et al (2016) Genome-wide association analysis identifies three new susceptibility loci for childhood body mass index. Hum Mol Genet 25(2):389–403. https://doi.org/10.1093/hmg/ddv472
Fraser A, Macdonald-Wallis C, Tilling K, Boyd A, Golding J, Davey Smith G, Lawlor DA (2013) Cohort Profile: The Avon Longitudinal Study of Parents and Children: ALSPAC mothers cohort. Int J Epidemiol 42(1):97–110. https://doi.org/10.1093/ije/dys066
GBD 2015 Obesity Collaborators, Afshin A, Forouzanfar MH, Reitsma MB, Sur P, Estep K et al (2017) Health effects of overweight and obesity in 195 countries over 25 years. N Engl J Med 377(1):13–27. https://doi.org/10.1056/NEJMoa1614362
Hemani G, Yang J, Vinkhuyzen A, Powell JE, Willemsen G, Hottenga JJ, Visscher PM (2013) Inference of the genetic architecture underlying BMI and height with the use of 20,240 sibling pairs. Am J Hum Genet 93(5):865–875. https://doi.org/10.1016/j.ajhg.2013.10.005
Jaeger BC, Edwards LJ, Das K, Sen PK (2017) An R2 statistic for fixed effects in the generalized linear mixed model. J Appl Stat 44(6):1086–1105. https://doi.org/10.1080/02664763.2016.1193725
Kong A, Thorleifsson G, Frigge ML, Vilhjalmsson BJ, Young AI, Thorgeirsson TE et al (2018) The nature of nurture: effects of parental genotypes. Science 359(6374):424–428. https://doi.org/10.1126/science.aan6877
Kuznetsova A, Brockhoff PB, Christensen RHB (2017) {{lmerTest}} package: tests in linear mixed effects models. J Stat Softw 82(13):1–26. https://doi.org/10.18637/jss.v082.i13
Lawlor D, Richmond R, Warrington N, McMahon G, Smith GD, Bowden J, Evans DM (2017) Using Mendelian randomization to determine causal effects of maternal pregnancy (intrauterine) exposures on offspring outcomes: sources of bias and methods for assessing them. Wellcome Open Res 2:11. https://doi.org/10.12688/wellcomeopenres.10567.1
Maes HHM, Neale MC, Eaves LJ (1997) Genetic and environmental factors in relative body weight and human adiposity. Behav Genet 27(4):325–351. https://doi.org/10.1023/A:1025635913927
Mak TSH, Porsch RM, Choi SW, Zhou X, Sham PC (2017) Polygenic scores via penalized regression on summary statistics. Genet Epidemiol 41:469–480. https://doi.org/10.1002/gepi.22050
Min J, Chiu DT, Wang Y (2013) Variation in the heritability of body mass index based on diverse twin studies: a systematic review. Obes Rev Off J Int Assoc Study Obes 14(11):871–882. https://doi.org/10.1111/obr.12065
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(3):327–339. https://doi.org/10.1007/s10519-018-9944-9
Nakagawa S, Schielzeth H (2013) A general and simple method for obtaining R2 from generalized linear mixed-effects models. Methods Ecol Evol 4(2):133–142. https://doi.org/10.1111/j.2041-210x.2012.00261.x
Nan C, Guo B, Warner C, Fowler T, Barrett T, Boomsma D et al (2012) Heritability of body mass index in pre-adolescence, young adulthood and late adulthood. Eur J Epidemiol 27:247–253. https://doi.org/10.1007/s10654-012-9678-6
Nance WE, Corey LA (1976) Genetic models for the analysis of data from the families of identical twins. Genetics 83(4):811–826. http://www.genetics.org/content/83/4/811
R Core Team (2019) R: a language and environment for statistical computing. https://www.r-project.org/
Robinson MR, Kleinman A, Graff M, Vinkhuyzen AAE, Couper D, Miller MB et al (2017) Genetic evidence of assortative mating in humans. Nat Hum Behav 1(1):1–13. https://doi.org/10.1038/s41562-016-0016
Silventoinen K, Kaprio J, Lahelma E, Viken RJ, Rose RJ (2003) Assortative mating by body height and BMI: Finnish twins and their spouses. Am J Hum Biol 15(5):620–627. https://doi.org/10.1002/ajhb.10183
Silventoinen K, Rokholm B, Kaprio J, Sørensen TIA (2010) The genetic and environmental influences on childhood obesity: a systematic review of twin and adoption studies. Int J Obes 34(1):29–40. https://doi.org/10.1038/ijo.2009.177
Spielman RS, McGinnis RE, Ewens WJ (1993) Transmission test for linkage disequilibrium: the insulin gene region and insulin-dependent diabetes mellitus (IDDM). Am J Hum Genet 52(3):506–516
Strand BH, Kuh D, Shah I, Guralnik J, Hardy R (2012) Childhood, adolescent and early adult body mass index in relation to adult mortality: results from the British 1946 birth cohort. J Epidemiol Community Health 66(3):225–232. https://doi.org/10.1136/jech.2010.110155
Tambs K, Moum T, Eaves L, Neale M, Midthjell K, Lund-Larsen PG, Holmen J (1991) Genetic and environmental contributions to the variance of the body mass index in a Norwegian sample of first- and second-degree relatives. Am J Hum Biol Off J Hum Biol Counc 3(3):257–267. https://doi.org/10.1002/ajhb.1310030305
Tubbs JD, Zhang YD, Sham PC (2020) Intermediate confounding in trio relationships: the importance of complete data in effect size estimation. Genet Epidemiol 44(4):395–399. https://doi.org/10.1002/gepi.22294
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(4):1229–1241. https://doi.org/10.1093/ije/dyy015
Warrington NM, Beaumont RN, Horikoshi M, Day FR, Helgeland Ø, Laurin C 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. https://doi.org/10.1038/s41588-019-0403-1
Wolf JB, Wade MJ (2009) What are maternal effects (and what are they not)? Philos Trans R Soc B 364(1520):1107–1115. https://doi.org/10.1098/rstb.2008.0238
Yang J, Bakshi A, Zhu Z, Hemani G, Vinkhuyzen AAE, Lee SH et al (2015) Genetic variance estimation with imputed variants finds negligible missing heritability for human height and body mass index. Nat Genet 47(10):1114–1120. https://doi.org/10.1038/ng.3390
Yanovski JA (2018) Obesity: trends in underweight and obesity: scale of the problem. Nat Rev Endocrinol 14(1):5–6. https://doi.org/10.1038/nrendo.2017.157
Yengo L, Sidorenko J, Kemper KE, Zheng Z, Wood AR, Weedon MN 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(20):3641–3649. https://doi.org/10.1093/hmg/ddy271
Zhang G, Bacelis J, Lengyel C, Teramo K, Hallman M, Helgeland Ø et al (2015) Assessing the causal relationship of maternal height on birth size and gestational age at birth: a Mendelian randomization analysis. PLoS Med. https://doi.org/10.1371/journal.pmed.1001865
Acknowledgements
We are extremely grateful to all the families who took part in this study, 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.
Funding
Data transfer from ALSPAC was supported by funding from the University of Hong Kong Department of Psychiatry. The UK Medical Research Council and Wellcome (Grant Ref. 102215/2/13/2) and the University of Bristol provide core support for ALSPAC, with additional funding for collection of data included in this research by (Grant Refs. 076467/Z/05/Z, WT092830/Z/10/Z, SP/07/008/24066, WT088806, 102215/2/13/2). GWAS data was generated by Sample Logistics and Genotyping Facilities at Wellcome Sanger Institute and LabCorp (Laboratory Corporation of America) using support from 23andMe.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
Justin D. Tubbs, Robert M. Porsch, Stacey S. Cherny and Pak C. Sham declares that they have no conflict of interest to report.
Additional information
Handling Editor: Sarah Medland
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Electronic supplementary material
Below is the link to the electronic supplementary material.
Rights and permissions
About this article
Cite this article
Tubbs, J.D., Porsch, R.M., Cherny, S.S. et al. The Genes We Inherit and Those We Don’t: Maternal Genetic Nurture and Child BMI Trajectories. Behav Genet 50, 310–319 (2020). https://doi.org/10.1007/s10519-020-10008-w
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10519-020-10008-w