Maternal genetic effects can be defined as the causal influence of maternal genotypes on offspring phenotypes over and above that which results from the transmission of genes from mothers to their offspring (Wolf and Wade, 2009). Over the last few years there has been a resurgence of interest in identifying and quantifying maternal genetic effects on offspring phenotypes, both from the perspective of variance component estimation (Balbona et al., 2021; Bates et al., 2018; Eaves et al., 2014; Eilertsen et al., 2021; Kim et al., 2021; Kong et al., 2018; Qiao et al., 2020; Tubbs et al., 2020) and estimating the causal effect of specific maternal environmental exposures on offspring outcomes through Mendelian randomization based approaches (Evans et al., 2019; Lawlor et al., 2017; Moen et al., 2020; Tyrrell et al., 2016; Warrington et al., 2019; Zhang et al., 2015). Indeed, following the advent of transgenerational genome-wide association studies, maternal genetic effects are beginning to be identified at individual genetic loci (Warrington et al., 2019; Beaumont et al., 2018), a trend that is set to continue as the sample sizes of such studies increase further. Given the increasing number of variants identified in GWAS that exhibit robust maternal genetic effects, a natural question to ask is whether these loci exert their effects on offspring phenotypes through intrauterine mechanisms, the postnatal environment, or both. Indeed, resolving maternal effects into prenatal and postnatal sources of variation could be a valuable first step in eventually elucidating the underlying mechanisms behind these associations (Armstrong-Carter et al., 2020), directing investigators to where they should focus their attention, and in the case of disease-related phenotypes, yielding potentially important information regarding the optimal timing of interventions. For example, the demonstration of maternal prenatal effects on offspring IQ/educational attainment, suggest that if the mediating factors that were responsible could be identified, then improvements in the prenatal care of mothers and their unborn babies targeting these factors, could yield useful increases in offspring IQ/educational attainment.

In this manuscript, we develop a simple method for partitioning maternal genetic effects into prenatal and postnatal components that leverages information provided by adopted individuals. We assume that an adopted individual’s phenotype is influenced by prenatal intrauterine factors as proxied by their biological mother’s genome, and postnatal influences as proxied by their adoptive mother’s and father’s genomes. In contrast, we assume that the phenotype of individuals who have not been adopted are influenced by prenatal intrauterine factors resulting from their biological mother’s genome, and postnatal factors as proxied by their biological mother’s and father’s genomes. This model leads to different expectations for the covariance between an individual’s phenotype and their own and their relatives’ genotypes depending on whether they have been adopted or not.

One of the challenges in applying this kind of framework to real life situations is the paucity of cohorts containing large numbers of adoptive families (Horn, 1983; Rhea et al., 2013; Scarr and Weinberg, 1983). Restricted numbers of adoptive families will consequently limit the statistical power to partition maternal genetic effects- particularly at single genetic variants which tend to have very small effect sizes. It is important to realize, however, that adopted ‘singletons’ (i.e. adopted individuals whose adoptive and biological parents have not been included in the study) provide important information on partitioning maternal genetic effects into prenatal and postnatal contributions regardless of whether information on their parents has been gathered. The intuition behind this surprising fact is that the covariance between an adopted individual’s genotype and phenotype is a function of prenatal (but not postnatal) maternal genetic effects (Figure 1, G5 to G7). In contrast, the covariance between a non-adopted individual and their own phenotype includes contributions from prenatal and postnatal genetic effects (Figure 1, G1 to G4). Thus, the difference between the genotype-phenotype covariance in adopted and non-adopted singleton individuals provides important information on the likely size of postnatal genetic effects. This is potentially important given that >7000 individuals in the UK Biobank study (Sudlow et al., 2015) report being adopted, and hence this publicly available dataset may represent a powerful resource for identifying prenatal maternal effects and partitioning maternal genetic effects into prenatal and postnatal sources of variation.

Figure 1

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In this manuscript, we introduce a new structural equation model (SEM) to estimate prenatal and postnatal maternal genetic effects on offspring phenotypes. The SEM framework allows us to model the contributions from missing individuals as latent variables, facilitating the inclusion of information from adopted singleton individuals whose adoptive and/or biological parents are not available for analysis. We estimate the asymptotic power of our model to partition maternal genetic effects into prenatal and postnatal components and confirm these results by simulation. We code our routines into a freely available R shiny app web utility that researchers can use to perform their own power calculations. Finally, we apply our methods to birth weight and educational attainment data in the UK Biobank- two phenotypes known to be affected by maternal genetic effects. We investigate the effect of inadvertently including adopted singleton individuals whose adoptive parents are genetically related to them in our model, adjust our model to correct for this misspecification and quantify the effect of this adjustment on type 1 error rates and power. We discuss the implications of model misspecification for using the UK Biobank resource in this context in future studies more broadly.

Genetic studies of adopted individuals have a venerable history in the field of behavior genetics (Leahy, 1935; Richardson, 1913). Traditionally, adoption studies were used to determine the relative importance of genes and the environment on trait variability- the basic idea being that similarity between adopted individuals and their biological parents reflects the effect of genes, whereas the similarity between adopted individuals and their adoptive parents reflects the effect of shared environmental influences. Indeed, large studies of adopted individuals played a valuable role in resolving the ‘Nature vs Nurture’ debate of last century, complementing similar findings from twin studies and other pedigree-based designs (Horn, 1983; Rhea et al., 2013; Scarr and Weinberg, 1983).

In this manuscript, we developed a new statistical model that uses the information provided by adopted individuals to partition maternal genetic effects into prenatal and postnatal influences on offspring phenotypes. Our method uses SEM to model the expected covariance between genotypes and phenotypes in adopted and non-adopted individuals. We have specifically designed our method to capitalize on the large number of adopted ‘singleton’ individuals present in large-scale population-based resources like the UK Biobank, exploiting the fact that the expected covariance between offspring genotype and phenotype differs between adopted and non-adopted individuals as a function of postnatal maternal genetic (and other) effects. Whilst our method does not provide explicit mechanistic insight into the reason for the existence of maternal genetic effects (merely a simple partitioning into prenatal and postnatal components, and estimation as to the relative importance of each), follow up studies could help elucidate these mechanisms by for example examining the association between maternal PRS and different prenatal and postnatal maternal phenotypes, and performing mediation and Mendelian randomization analyses (Evans et al., 2019; Armstrong-Carter et al., 2020).

We are not the first to note that information from adopted individuals could be used to provide valuable information on the effect of the prenatal environment on offspring traits (Loehlin, 2016; Demange et al., 2020). We are the first, however, to our knowledge to develop a model that specifically quantifies prenatal and postnatal maternal genetic effects on offspring phenotypes. Recently, (Domingue and Fletcher, 2020) constructed a PRS to proxy educational attainment using maternal genotypes, and tested the strength of the association between the risk score and offspring educational attainment in children from biological and adoptive families separately (Domingue and Fletcher, 2020). The authors showed that the positive association between the score and educational attainment was stronger in children from biological than adoptive families. Our method differs from Dominique and Fletcher in a number of important respects. First, the focus in their study was on demonstrating indirect parental effects on offspring phenotypes, whereas the focus in our manuscript is specifically on partitioning maternal genetic effects into prenatal and postnatal effects. Second, our method provides point estimates and tests of the significance of prenatal and postnatal maternal genetic effects. Lastly, the method used in Dominique and Fletcher requires both adopted and biological parent-child trios (or alternatively mother-offspring pairs). In contrast, our method at a minimum requires biological parent-child trios (or both biological mother-offspring pairs and biological father-offspring pairs) and adopted singletons only to estimate prenatal and postnatal maternal genetic effects on offspring phenotypes. The inclusion of adoptive mother-adopted offspring pairs and/or biological mother-adopted offspring pairs increases power to partition maternal genetic effects dramatically, but is not a strict requirement of our method if adopted singletons are available.

We are also not the first to have used adopted individuals in the UK Biobank to estimate the contribution of indirect genetic effects to phenotypic variability. (Cheesman et al., 2020) contrasted adopted and non-adopted individuals in the UK Biobank to investigate evidence for indirect genetic effects on educational attainment and the existence of gene-environment correlation (Cheesman et al., 2020). The authors found that a genome-wide PRS for educational attainment was more strongly associated with educational attainment in non-adopted compared to adopted individuals consistent with the existence of indirect genetic effects on educational attainment. Likewise, G-REML estimates of SNP heritability were also lower in adopted individuals compared to non-adopted individuals (potentially due to the absence/lower amount of passive gene-environment correlation in adopted individuals).

In a previous study of cognitive and non-cognitive effects on educational attainment (Demange et al., 2020), used three different sorts of study design to estimate the contribution of indirect genetic effects on educational attainment (Demange et al., 2020). They estimated indirect genetic effects by comparing between family and within family genetic effects in sibling pairs (Fulker et al., 1999), by estimating the effect of non-transmitted parental alleles on offspring phenotype in parent-offspring trios, and by computing the difference in PRS associations between adopted and non-adopted individuals. In general, the authors found that estimates of indirect effects using the adoption study design were lower than those produced by the other two models. They interpreted this as being a consequence of the adoption study methodology not capturing the impact of prenatal maternal genetic effects (and also potentially being more robust to assortative mating and population stratification) in contrast to the two other study designs. The present study explicitly capitalizes on differences between adopted and non-adopted individuals in their pattern of genotype-trait covariances to estimate the relative size of prenatal and postnatal maternal genetic effects and to provide formal statistical tests for their presence. Indeed, both (Demange et al., 2020) and our findings are consistent with the existence of prenatal effects on educational attainment (although we believe that at least part of this finding is an artefact and due to unmodelled biological relationships between some adopted children and their adoptive parents in the UK Biobank).

Our power calculations show that in principle the number of adopted individuals in the UK Biobank is unlikely to be sufficient to resolve effects at individual genetic loci but may be large enough to detect evidence for prenatal/postnatal maternal genetic effects in the case of PRS. It is worth noting that, whilst primarily a function of the number of adoptive families in the analysis, the power to partition maternal genetic effects was also influenced by the number of biological families analysed. Intuitively, this is because the other family structures contribute to more precise estimation of for example paternal genetic effects, which in turn provides information on the total size of the maternal genetic component. This observation is important, because the number of adopted individuals in an analysis is likely to be severely constrained, whereas power can sometimes be improved (at least up to a threshold determined by the total number of adoptive families/individuals in the analysis) by increasing the sample size of other relative types that may be easier to ascertain. Regardless, the most efficient way to increase the power to detect prenatal and/or postnatal maternal genetic effects is to genotype adoptive mother-adopted child pairs and/or biological mother-adopted child pairs.

Our empirical analyses in the UK Biobank showed the expected pattern of maternal genetic effects on offspring birth weight (i.e. strong evidence for prenatal but not postnatal maternal genetic effects) but not on offspring educational attainment. Parental educational attainment is known to causally affect offspring educational attainment (Bates et al., 2018; Kong et al., 2018; Hwang et al., 2020). Whilst it is possible, even probable, that some loci may affect offspring educational attainment through prenatal maternal pathways, it is not credible that such mechanisms would lead to larger effect sizes than those from postnatal pathways, or that fathers would exhibit strong evidence of postnatal effects but not mothers. Rather we hypothesize it is possible that our model could have been misspecified in that substantial numbers of adopted individuals in the UK Biobank may have in fact been raised by their biological relatives. This can be thought of as (unintentional) reintroduction of passive gene-environment correlation into the study. In other words, adopted children are brought up by their genetic relatives, who in turn provide the environment in which they are raised. This induces a correlation between adopted individuals’ PRS and their environment. Sensitivity analyses that excluded adopted individuals with known breastfeeding information suggested that including adopted individuals who were raised by genetically related adoptive mothers may lead to inflated prenatal maternal genetic effect estimates. Our simulations with adoptive parents who were siblings or cousins of the biological parents further confirmed that unmodelled relatedness can increase evidence for prenatal maternal genetic effects at the expense of postnatal effects, particularly when paternal genetic and postnatal maternal effects are present, as would be the case with educational attainment (this contrasts with birth weight where we do not expect any paternal or postnatal genetic effects and for which we obtained sensible results). Ideally, one would have detailed information on any genetic relationship between adoptive and biological parents and model these relationships in the SEM (see below).

Our model relies on several strong assumptions, some of which are shown explicitly in Figure 1 and others which are not (see Supplementary file 6 for a summary of some of the major assumptions/limitations of our model). Those assumptions explicitly encoded in Figure 1 include that the total maternal genetic effect can be decomposed into the sum of prenatal and postnatal components, that genetic effects are homogenous across biological and adoptive families, the absence of genotype x environment interaction, the absence of gene-environment correlation (including evocative gene-environment correlation and correlation between the prenatal maternal environment and maternal PRS), any correlation between the genetic scores in spouses is the same in biological and adoptive families, and that fathers do not exert prenatal effects on offspring traits. Assumptions that are not explicitly shown in Figure 1 include that adopted (and adoptive) individuals are not systematically different from other individuals, adopted individuals are placed randomly within the population (including with individuals not genetically related to themselves), that adoption happened soon after birth and that the adopted individuals were raised in one adoptive family, and adopted individuals do not maintain contact with their biological parents. We also ignore any complexities due to for example single mothers raising offspring independently of their fathers etc.

Whilst we expect some of these assumptions to hold (i.e. it is reasonable to expect the absence of substantial prenatal paternal genetic effects for many traits), we expect that others will show varying degrees of violation with differing consequences for the parameter estimates obtained under the misspecified model. For example, it is well appreciated that adopted individuals and adoptive families may differ in several respects to the general population. For example, some studies have reported that adoptive families are better educated and have higher socio-economic status than the population average (Kendler et al., 2015). We argue that violation of this assumption is likely to exert more serious consequences on traditional adoption studies which model the phenotypic correlation between biological, adopted relatives. In contrast, in our design it is more important that genetic effect sizes are homogenous across adopted and non-adopted individuals (i.e. no genotype by environment interaction), and at least currently, there is limited evidence for large genotype by environment interactions at individual genetic loci. Our model also allows for differences in the residual variance across biological and adoptive families (Cheesman et al., 2020). This may be important if for example, adopted children are selectively placed in a reduced range of environments compared to their non-adopted counterparts.

We argue that of greater consequence for the validity of our model is that any genetic relationship between adoptive and biological parents is accurately modelled and included in the SEM. Through simulation, we have shown that the consequences of model misspecification depend upon which biological and adoptive parents are related, the nature of this relationship, and the proportion of adopted individuals in the sample who have had their relationship misspecified. Our simulations also showed that correctly modelling this relationship returns asymptotically unbiased effect estimates and correct type I error rates. Clearly, knowing these cryptic relationships in the UK Biobank would allow us to properly model them and better estimate prenatal and postnatal maternal genetic effects using this resource. We emphasize that accurately modelling these relationships does not require that actual genotypes for adoptive and/or biological parents be obtained (although this would be advantageous in terms of statistical power) as our SEM allows us to model these relationships in terms of latent variables. Indeed, as large-scale resources like the UK Biobank become more common, we expect that the number of adopted individuals who have GWAS will also increase, and consequently models like the one espoused in this manuscript will become increasingly useful. High quality phenotypic information on these adopted individuals and their adoptive parents including whether they share any biological relationship will be critical to making the most of these resources.

We have also not modelled the complex effects of assortative mating in our SEM other than including covariance terms between maternal and paternal genotypes and assuming equal genetic variances in parents and their offspring under random mating (i.e. this is equivalent to assuming one round of assortative mating in the parental generation). Positive assortment induces a number of complications when attempting to decompose the offspring phenotypic variance into its constituent sources of variation, including increasing homozygosity and the genetic variance at loci for the trait undergoing assortment relative to that expected under Hardy-Weinberg equilibrium, and inducing correlations between assorting loci across the rest of the genome both within and between individuals in the same family. Spousal correlations for phenotypes (educational attainment: r=0.285; birth weight: r=0.076) and PRS (educational attainment: r=0.117; birth weight: r=0.068) in complete parent-offspring trios in the UK Biobank suggest that assortative mating is likely present for educational attainment, and potentially to a lesser degree for (phenotypes correlated with) birth weight. Recent work by Balbona et al., 2021 and Kim et al., 2021 have shown how PRS of transmitted and non-transmitted alleles in parent-offspring trios can be used to estimate direct and indirect genetic effects on offspring phenotype and the variation attributable to the environmental influence of parents on offspring under phenotypic assortment (Balbona et al., 2021; Kim et al., 2021). It is possible that our basic model could be extended in a similar fashion to incorporate the effect of assortment and estimate some of these effects also.

Also relevant to this discussion is a series of data simulations performed by Demange et al., 2020 who found that estimates of indirect genetic effects obtained in adoption designs (i.e. which estimate the size of indirect genetic effects by taking the difference between a PRS-trait association in adopted individuals and a PRS-trait association in non-adopted individuals) were less biased by assortative mating and population stratification than estimates of indirect genetic effects obtained in biologically related parent-offspring trio designs (i.e. where estimates of indirect genetic effects are obtained by regressing offspring phenotype on parental genotype conditional on offspring genotype). In the case of population stratification, the intuition behind this observation is that the contaminating effects of substructure are present in both adopted and non-adopted individuals. This contamination cancels out when the PRS-trait association in adopted individuals is subtracted from the PRS-trait association in non-adopted individuals. However, this is not the case in parent-offspring trios, where estimates of indirect genetic effects remain contaminated.

In the context of our SEM, we expect that unmodelled population stratification will bias estimates of maternal prenatal and postnatal effects away from the null since population stratification will increase the covariance between maternal genotype and offspring phenotype, as well as the covariance between offspring genotype and phenotype in adopted and non-adopted individuals. Because we have limited our empirical analyses to individuals of white European ancestry and residualized the phenotypes for the first five genetic principal components prior to analysis in the SEM, any influence due to population stratification is likely to have been minor in our empirical analyses in the UK Biobank. The influence of assortative mating on our model is more difficult to predict, but our limited exploration of this issue by simulation (data not shown) suggests that the effect of assortative mating on parameter estimates from our SEM is likely to be minor.

Finally, it has not been lost on us, that our framework may provide a basis for correcting Mendelian randomization (MR) studies of intrauterine/early life environmental exposures for some of the contaminating effects of horizontal genetic pleiotropy (Figure 4). The presence of latent horizontal pleiotropy is one of the major threats to the validity of causal inference from MR studies (Hemani et al., 2018). As we have shown, the inclusion of adopted individuals alongside biological families in genetic studies permits the partitioning of maternal genetic effects into prenatal and postnatal components. Consequently, if the focus of an MR study is on estimating the causal effect of a prenatal maternal exposure on an offspring outcome, the covariance between adoptive mother’s genotype and offspring (outcome) phenotype should provide an estimate of genetic effects due to postnatal horizontal genetic pleiotropy. These estimates could then be included in an MR model to correct causal estimates for the effect of postnatal pleiotropy (Figure 4). It is important to realize, however, that this procedure will not correct for the effect of prenatal/early life horizontal pleiotropy as these paths will be present in both biological and adopted individuals and so this approach is not a panacea for dealing with latent pleiotropy in MR studies.

Figure 4

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In conclusion, we present a simple extension to the basic adoption design that includes measured genotypes and enables partitioning of maternal genetic effects into prenatal and postnatal sources of variation. We show in principle that adopted singleton individuals in the UK Biobank combined with biologically related parent-offspring trios and pairs is sufficient to partition maternal genetic effects into prenatal and postnatal sources of variation. However, power calculations suggest that such a partitioning would currently only be realistic for PRS explaining substantial proportions of the variance in offspring phenotype, and that much larger numbers of individuals would be required to achieve such a partitioning at individual loci. In addition, failure to correctly model cryptic relationships between adoptive and biological parents may produce biased estimates of maternal genetic effects and increased type I error rates. Accurate modelling of cryptic relationships is sufficient to bring type I error rate under control and produce unbiased effect estimates. We suggest that there is a possibility that many individuals in the UK Biobank who report being adopted could have been raised by their biological relatives. We conclude that there would be considerable value in following up adopted individuals in the UK Biobank to determine whether they were raised by biological relatives, and if so, to precisely ascertain the nature of the relationship. These adopted individuals could then be used in informative statistical genetics models like the one described in the present manuscript to further elucidate the genetic architecture of complex traits and diseases.

Human genotype and phenotype data on which the results of this study were based were accessed from the UK Biobank (http://www.ukbiobank.ac.uk/) with accession ID 53641. The genotype and phenotype data are available upon application from the UK Biobank (http://www.ukbiobank.ac.uk/). R code for performing the analyses described in this manuscript is available in the Supplementary Materials.

1. 1. Sudlow C
   2. Gallacher J
   3. Allen N
   4. Beral V
   5. Burton P
   6. Danesh J
   7. Downey P
   8. Elliott P
   9. Green J
   10. Landray M
   11. Lui B
   12. Matthews P
   13. Ong G
   14. Pell J
   15. Silman A
   16. Young A
   17. Sprosen T
   18. Peakman T
   19. Collins R

   (2015) UK Biobank

   ID 53641. UK Biobank.

   http://www.ukbiobank.ac.uk/

1. 1. Armstrong-Carter E
   2. Trejo S
   3. Hill LJB
   4. Crossley KL
   5. Mason D
   6. Domingue BW

   (2020) The earliest origins of genetic nurture the prenatal environment mediates the association between maternal genetics and child development

   Psychological Science 31:781–791.

   https://doi.org/10.1177/0956797620917209

   • PubMed
   • Google Scholar
2. 1. Balbona JV
   2. Kim Y
   3. Keller MC

   (2021) Estimation of parental effects using polygenic scores

   Behavior Genetics 51:264–278.

   https://doi.org/10.1007/s10519-020-10032-w

   • PubMed
   • Google Scholar
3. 1. Bates TC
   2. Maher BS
   3. Medland SE
   4. McAloney K
   5. Wright MJ
   6. Hansell NK
   7. Kendler KS
   8. Martin NG
   9. 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 Research and Human Genetics 21:73–83.

   https://doi.org/10.1017/thg.2018.11

   • PubMed
   • Google Scholar
4. 1. Beaumont RN
   2. Warrington NM
   3. Cavadino A
   4. Tyrrell J
   5. Nodzenski M
   6. Horikoshi M
   7. Geller F
   8. Myhre R
   9. Richmond RC
   10. Paternoster L
   11. Bradfield JP
   12. Kreiner-Møller E
   13. Huikari V
   14. Metrustry S
   15. Lunetta KL
   16. Painter JN
   17. Hottenga JJ
   18. Allard C
   19. Barton SJ
   20. Espinosa A
   21. Marsh JA
   22. Potter C
   23. Zhang G
   24. Ang W
   25. Berry DJ
   26. Bouchard L
   27. Das S
   28. Hakonarson H
   29. Heikkinen J
   30. Helgeland Ø
   31. Hocher B
   32. Hofman A
   33. Inskip HM
   34. Jones SE
   35. Kogevinas M
   36. Lind PA
   37. Marullo L
   38. Medland SE
   39. Murray A
   40. Murray JC
   41. Njølstad PR
   42. Nohr EA
   43. Reichetzeder C
   44. Ring SM
   45. Ruth KS
   46. Santa-Marina L
   47. Scholtens DM
   48. Sebert S
   49. Sengpiel V
   50. Tuke MA
   51. Vaudel M
   52. Weedon MN
   53. Willemsen G
   54. Wood AR
   55. Yaghootkar H
   56. Muglia LJ
   57. Bartels M
   58. Relton CL
   59. Pennell CE
   60. Chatzi L
   61. Estivill X
   62. Holloway JW
   63. Boomsma DI
   64. Montgomery GW
   65. Murabito JM
   66. Spector TD
   67. Power C
   68. Järvelin MR
   69. Bisgaard H
   70. Grant SFA
   71. Sørensen TIA
   72. Jaddoe VW
   73. Jacobsson B
   74. Melbye M
   75. McCarthy MI
   76. Hattersley AT
   77. Hayes MG
   78. Frayling TM
   79. Hivert MF
   80. Felix JF
   81. Hyppönen E
   82. Lowe WL
   83. Evans DM
   84. Lawlor DA
   85. Feenstra B
   86. Freathy RM

   (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

   Human Molecular Genetics 27:742–756.

   https://doi.org/10.1093/hmg/ddx429

   • PubMed
   • Google Scholar
5. 1. Boker S
   2. Neale M
   3. Maes H
   4. Wilde M
   5. Spiegel M
   6. Brick T
   7. Spies J
   8. Estabrook R
   9. Kenny S
   10. Bates T
   11. Mehta P
   12. Fox J

   (2011) OpenMx: an open source extended structural equation modeling framework

   Psychometrika 76:306–317.

   https://doi.org/10.1007/s11336-010-9200-6

   • PubMed
   • Google Scholar
6. 1. Cheesman R
   2. Hunjan A
   3. Coleman JRI
   4. Ahmadzadeh Y
   5. Plomin R
   6. McAdams TA
   7. Eley TC
   8. Breen G

   (2020) Comparison of adopted and nonadopted individuals reveals gene-environment interplay for education in the uk biobank

   Psychological Science 31:582–591.

   https://doi.org/10.1177/0956797620904450

   • PubMed
   • Google Scholar
7. Preprint

   1. Demange PA
   2. Hottenga JJ
   3. Malanchini M

   (2020) Parental Influences on Offspring Education: Indirect Genetic Effects of Non-Cognitive Skills

   bioRxiv.

   https://doi.org/10.1101/2020.09.15.296236

   • Google Scholar
8. 1. Domingue BW
   2. Fletcher J

   (2020) Separating measured genetic and environmental effects: evidence linking parental genotype and adopted child outcomes

   Behavior Genetics 50:301–309.

   https://doi.org/10.1007/s10519-020-10000-4

   • PubMed
   • Google Scholar
9. 1. Eaves LJ
   2. Pourcain BS
   3. Smith GD
   4. York TP
   5. Evans DM

   (2014) Resolving the effects of maternal and offspring genotype on dyadic outcomes in genome wide complex trait analysis (“M-GCTA”)

   Behavior Genetics 44:445–455.

   https://doi.org/10.1007/s10519-014-9666-6

   • PubMed
   • Google Scholar
10. 1. Eilertsen EM
    2. Jami ES
    3. McAdams TA
    4. Hannigan LJ
    5. Havdahl AS
    6. Magnus P
    7. Evans DM
    8. Ystrom E

    (2021) Direct and indirect effects of maternal, paternal, and offspring genotypes: trio-GCTA

    Behavior Genetics 51:154–161.

    https://doi.org/10.1007/s10519-020-10036-6

    • PubMed
    • Google Scholar
11. 1. Evans DM
    2. Moen GH
    3. Hwang LD
    4. Lawlor DA
    5. Warrington NM

    (2019) Elucidating the role of maternal environmental exposures on offspring health and disease using two-sample Mendelian randomization

    International Journal of Epidemiology 48:861–875.

    https://doi.org/10.1093/ije/dyz019

    • PubMed
    • Google Scholar
12. 1. Fulker DW
    2. Cherny SS
    3. Sham PC
    4. Hewitt JK

    (1999) Combined linkage and association sib-pair analysis for quantitative traits

    American Journal of Human Genetics 64:259–267.

    https://doi.org/10.1086/302193

    • PubMed
    • Google Scholar
13. 1. Hemani G
    2. Bowden J
    3. Davey Smith G

    (2018) Evaluating the potential role of pleiotropy in Mendelian randomization studies

    Human Molecular Genetics 27:R195–R208.

    https://doi.org/10.1093/hmg/ddy163

    • PubMed
    • Google Scholar
14. 1. Horn JM

    (1983)

    The Texas Adoption Project: adopted children and their intellectual resemblance to biological and adoptive parents

    Child Development 54:268–275.

    • PubMed
    • Google Scholar
15. 1. Hwang LD
    2. Tubbs JD
    3. Luong J
    4. Lundberg M
    5. Moen GH
    6. Wang G
    7. Warrington NM
    8. Sham PC
    9. Cuellar-Partida G
    10. Evans DM

    (2020) Estimating indirect parental genetic effects on offspring phenotypes using virtual parental genotypes derived from sibling and half sibling pairs

    PLOS Genetics 16:e1009154.

    https://doi.org/10.1371/journal.pgen.1009154

    • PubMed
    • Google Scholar
16. 1. Kendler KS
    2. Turkheimer E
    3. Ohlsson H
    4. Sundquist J
    5. Sundquist K

    (2015) Family environment and the malleability of cognitive ability: a Swedish national home-reared and adopted-away cosibling control study

    PNAS 112:4612–4617.

    https://doi.org/10.1073/pnas.1417106112

    • PubMed
    • Google Scholar
17. 1. Kim Y
    2. Balbona JV
    3. Keller MC

    (2021) Bias and precision of parameter estimates from models using polygenic scores to estimate environmental and genetic parental influences

    Behavior Genetics 51:279–288.

    https://doi.org/10.1007/s10519-020-10033-9

    • PubMed
    • Google Scholar
18. 1. Kong A
    2. Thorleifsson G
    3. Frigge ML
    4. Vilhjalmsson BJ
    5. Young AI
    6. Thorgeirsson TE
    7. Benonisdottir S
    8. Oddsson A
    9. Halldorsson BV
    10. Masson G
    11. Gudbjartsson DF
    12. Helgason A
    13. Bjornsdottir G
    14. Thorsteinsdottir U
    15. Stefansson K

    (2018) The nature of nurture: Effects of parental genotypes

    Science 359:424–428.

    https://doi.org/10.1126/science.aan6877

    • PubMed
    • Google Scholar
19. 1. Lawlor D
    2. Richmond R
    3. Warrington N
    4. McMahon G
    5. Davey Smith G
    6. Bowden J
    7. 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 Research 2:11.

    https://doi.org/10.12688/wellcomeopenres.10567.1

    • PubMed
    • Google Scholar
20. 1. Leahy AM

    (1935)

    Nature-nurture and intelligence

    Psychology Monographs 17:237–308.

    • Google Scholar
21. 1. Lee JJ
    2. Wedow R
    3. Okbay A
    4. Kong E
    5. Maghzian O
    6. Zacher M
    7. Nguyen-Viet TA
    8. Bowers P
    9. Sidorenko J
    10. Karlsson Linnér R
    11. Fontana MA
    12. Kundu T
    13. Lee C
    14. Li H
    15. Li R
    16. Royer R
    17. Timshel PN
    18. Walters RK
    19. Willoughby EA
    20. Yengo L
    21. Alver M
    22. Bao Y
    23. Clark DW
    24. Day FR
    25. Furlotte NA
    26. Joshi PK
    27. Kemper KE
    28. Kleinman A
    29. Langenberg C
    30. Mägi R
    31. Trampush JW
    32. Verma SS
    33. Wu Y
    34. Lam M
    35. Zhao JH
    36. Zheng Z
    37. Boardman JD
    38. Campbell H
    39. Freese J
    40. Harris KM
    41. Hayward C
    42. Herd P
    43. Kumari M
    44. Lencz T
    45. Luan J
    46. Malhotra AK
    47. Metspalu A
    48. Milani L
    49. Ong KK
    50. Perry JRB
    51. Porteous DJ
    52. Ritchie MD
    53. Smart MC
    54. Smith BH
    55. Tung JY
    56. Wareham NJ
    57. Wilson JF
    58. Beauchamp JP
    59. Conley DC
    60. Esko T
    61. Lehrer SF
    62. Magnusson PKE
    63. Oskarsson S
    64. Pers TH
    65. Robinson MR
    66. Thom K
    67. Watson C
    68. Chabris CF
    69. Meyer MN
    70. Laibson DI
    71. Yang J
    72. Johannesson M
    73. Koellinger PD
    74. Turley P
    75. Visscher PM
    76. Benjamin DJ
    77. Cesarini D

    (2018) Gene discovery and polygenic prediction from a genome-wide association study of educational attainment in 1.1 million individuals

    Nature Genetics 50:1112–1121.

    https://doi.org/10.1038/s41588-018-0147-3

    • PubMed
    • Google Scholar
22. 1. Loehlin JC

    (2016) What can an adoption study tell us about the effect of prenatal environment on a trait?

    Behavior Genetics 46:329–333.

    https://doi.org/10.1007/s10519-015-9730-x

    • PubMed
    • Google Scholar
23. 1. Manichaikul A
    2. Mychaleckyj JC
    3. Rich SS
    4. Daly K
    5. Sale M
    6. Chen WM

    (2010) Robust relationship inference in genome-wide association studies

    Bioinformatics 26:2867–2873.

    https://doi.org/10.1093/bioinformatics/btq559

    • PubMed
    • Google Scholar
24. 1. Moen GH
    2. Hemani G
    3. Warrington NM
    4. Evans DM

    (2019) Calculating power to detect maternal and offspring genetic effects in genetic association studies

    Behavior Genetics 49:327–339.

    https://doi.org/10.1007/s10519-018-9944-9

    • PubMed
    • Google Scholar
25. 1. Moen G-H
    2. Brumpton B
    3. Willer C
    4. Åsvold BO
    5. Birkeland KI
    6. Wang G
    7. Neale MC
    8. Freathy RM
    9. Smith GD
    10. Lawlor DA
    11. Kirkpatrick RM
    12. Warrington NM
    13. Evans DM

    (2020) Mendelian randomization study of maternal influences on birthweight and future cardiometabolic risk in the HUNT cohort

    Nature Communications 11:5404.

    https://doi.org/10.1038/s41467-020-19257-z

    • PubMed
    • Google Scholar
26. 1. Neale MC
    2. Hunter MD
    3. Pritikin JN
    4. Zahery M
    5. Brick TR
    6. Kirkpatrick RM
    7. Estabrook R
    8. Bates TC
    9. Maes HH
    10. Boker SM

    (2016) OpenMx 2.0: extended structural equation and statistical modeling

    Psychometrika 81:535–549.

    https://doi.org/10.1007/s11336-014-9435-8

    • PubMed
    • Google Scholar
27. 1. Okbay A
    2. Beauchamp JP
    3. Fontana MA
    4. Lee JJ
    5. Pers TH
    6. Rietveld CA
    7. Turley P
    8. Chen G-B
    9. Emilsson V
    10. Meddens SFW
    11. Oskarsson S
    12. Pickrell JK
    13. Thom K
    14. Timshel P
    15. de Vlaming R
    16. Abdellaoui A
    17. Ahluwalia TS
    18. Bacelis J
    19. Baumbach C
    20. Bjornsdottir G
    21. Brandsma JH
    22. Pina Concas M
    23. Derringer J
    24. Furlotte NA
    25. Galesloot TE
    26. Girotto G
    27. Gupta R
    28. Hall LM
    29. Harris SE
    30. Hofer E
    31. Horikoshi M
    32. Huffman JE
    33. Kaasik K
    34. Kalafati IP
    35. Karlsson R
    36. Kong A
    37. Lahti J
    38. van der Lee SJ
    39. deLeeuw C
    40. Lind PA
    41. Lindgren K-O
    42. Liu T
    43. Mangino M
    44. Marten J
    45. Mihailov E
    46. Miller MB
    47. van der Most PJ
    48. Oldmeadow C
    49. Payton A
    50. Pervjakova N
    51. Peyrot WJ
    52. Qian Y
    53. Raitakari O
    54. Rueedi R
    55. Salvi E
    56. Schmidt B
    57. Schraut KE
    58. Shi J
    59. Smith AV
    60. Poot RA
    61. St Pourcain B
    62. Teumer A
    63. Thorleifsson G
    64. Verweij N
    65. Vuckovic D
    66. Wellmann J
    67. Westra H-J
    68. Yang J
    69. Zhao W
    70. Zhu Z
    71. Alizadeh BZ
    72. Amin N
    73. Bakshi A
    74. Baumeister SE
    75. Biino G
    76. Bønnelykke K
    77. Boyle PA
    78. Campbell H
    79. Cappuccio FP
    80. Davies G
    81. De Neve J-E
    82. Deloukas P
    83. Demuth I
    84. Ding J
    85. Eibich P
    86. Eisele L
    87. Eklund N
    88. Evans DM
    89. Faul JD
    90. Feitosa MF
    91. Forstner AJ
    92. Gandin I
    93. Gunnarsson B
    94. Halldórsson BV
    95. Harris TB
    96. Heath AC
    97. Hocking LJ
    98. Holliday EG
    99. Homuth G
    100. Horan MA
    101. Hottenga J-J
    102. de Jager PL
    103. Joshi PK
    104. Jugessur A
    105. Kaakinen MA
    106. Kähönen M
    107. Kanoni S
    108. Keltigangas-Järvinen L
    109. Kiemeney LALM
    110. Kolcic I
    111. Koskinen S
    112. Kraja AT
    113. Kroh M
    114. Kutalik Z
    115. Latvala A
    116. Launer LJ
    117. Lebreton MP
    118. Levinson DF
    119. Lichtenstein P
    120. Lichtner P
    121. Liewald DCM
    122. Loukola A
    123. Madden PA
    124. Mägi R
    125. Mäki-Opas T
    126. Marioni RE
    127. Marques-Vidal P
    128. Meddens GA
    129. McMahon G
    130. Meisinger C
    131. Meitinger T
    132. Milaneschi Y
    133. Milani L
    134. Montgomery GW
    135. Myhre R
    136. Nelson CP
    137. Nyholt DR
    138. Ollier WER
    139. Palotie A
    140. Paternoster L
    141. Pedersen NL
    142. Petrovic KE
    143. Porteous DJ
    144. Räikkönen K
    145. Ring SM
    146. Robino A
    147. Rostapshova O
    148. Rudan I
    149. Rustichini A
    150. Salomaa V
    151. Sanders AR
    152. Sarin A-P
    153. Schmidt H
    154. Scott RJ
    155. Smith BH
    156. Smith JA
    157. Staessen JA
    158. Steinhagen-Thiessen E
    159. Strauch K
    160. Terracciano A
    161. Tobin MD
    162. Ulivi S
    163. Vaccargiu S
    164. Quaye L
    165. van Rooij FJA
    166. Venturini C
    167. Vinkhuyzen AAE
    168. Völker U
    169. Völzke H
    170. Vonk JM
    171. Vozzi D
    172. Waage J
    173. Ware EB
    174. Willemsen G
    175. Attia JR
    176. Bennett DA
    177. Berger K
    178. Bertram L
    179. Bisgaard H
    180. Boomsma DI
    181. Borecki IB
    182. Bültmann U
    183. Chabris CF
    184. Cucca F
    185. Cusi D
    186. Deary IJ
    187. Dedoussis GV
    188. van Duijn CM
    189. Eriksson JG
    190. Franke B
    191. Franke L
    192. Gasparini P
    193. Gejman PV
    194. Gieger C
    195. Grabe H-J
    196. Gratten J
    197. Groenen PJF
    198. Gudnason V
    199. van der Harst P
    200. Hayward C
    201. Hinds DA
    202. Hoffmann W
    203. Hyppönen E
    204. Iacono WG
    205. Jacobsson B
    206. Järvelin M-R
    207. Jöckel K-H
    208. Kaprio J
    209. Kardia SLR
    210. Lehtimäki T
    211. Lehrer SF
    212. Magnusson PKE
    213. Martin NG
    214. McGue M
    215. Metspalu A
    216. Pendleton N
    217. Penninx BWJH
    218. Perola M
    219. Pirastu N
    220. Pirastu M
    221. Polasek O
    222. Posthuma D
    223. Power C
    224. Province MA
    225. Samani NJ
    226. Schlessinger D
    227. Schmidt R
    228. Sørensen TIA
    229. Spector TD
    230. Stefansson K
    231. Thorsteinsdottir U
    232. Thurik AR
    233. Timpson NJ
    234. Tiemeier H
    235. Tung JY
    236. Uitterlinden AG
    237. Vitart V
    238. Vollenweider P
    239. Weir DR
    240. Wilson JF
    241. Wright AF
    242. Conley DC
    243. Krueger RF
    244. Davey Smith G
    245. Hofman A
    246. Laibson DI
    247. Medland SE
    248. Meyer MN
    249. Yang J
    250. Johannesson M
    251. Visscher PM
    252. Esko T
    253. Koellinger PD
    254. Cesarini D
    255. Benjamin DJ
    256. LifeLines Cohort Study

    (2016) Genome-wide association study identifies 74 loci associated with educational attainment

    Nature 533:539–542.

    https://doi.org/10.1038/nature17671

    • PubMed
    • Google Scholar
28. 1. Qiao Z
    2. Zheng J
    3. Helgeland Ø
    4. Vaudel M
    5. Johansson S
    6. Njølstad PR
    7. Smith GD
    8. Warrington NM
    9. Evans DM

    (2020) Introducing M-GCTA a software package to estimate maternal (or paternal) genetic effects on offspring phenotypes

    Behavior Genetics 50:51–66.

    https://doi.org/10.1007/s10519-019-09969-4

    • PubMed
    • Google Scholar
29. 1. Rhea SA
    2. Bricker JB
    3. Wadsworth SJ
    4. Corley RP

    (2013) The Colorado Adoption Project

    Twin Research and Human Genetics 16:358–365.

    https://doi.org/10.1017/thg.2012.109

    • PubMed
    • Google Scholar
30. 1. Richardson LF

    (1913)

    The measurement of mental “nature” and the study of adopted children

    Eugenics Review 4:1912–1913.

    • Google Scholar
31. Book

    1. RStudioTeam

    (2015)

    RStudio: Integrated Development for R

    Boston, MA: RStudio Inc.

    • Google Scholar
32. 1. Scarr S
    2. Weinberg RA

    (1983)

    The Minnesota Adoption Studies: genetic differences and malleability

    Child Development 54:260–267.

    • PubMed
    • Google Scholar
33. 1. Sudlow C
    2. Gallacher J
    3. Allen N
    4. Beral V
    5. Burton P
    6. Danesh J
    7. Downey P
    8. Elliott P
    9. Green J
    10. Landray M
    11. Liu B
    12. Matthews P
    13. Ong G
    14. Pell J
    15. Silman A
    16. Young A
    17. Sprosen T
    18. Peakman T
    19. Collins R

    (2015) UK biobank: an open access resource for identifying the causes of a wide range of complex diseases of middle and old age

    PLOS Medicine 12:e1001779.

    https://doi.org/10.1371/journal.pmed.1001779

    • PubMed
    • Google Scholar
34. 1. Tubbs JD
    2. Porsch RM
    3. Cherny SS
    4. Sham PC

    (2020) The genes we inherit and those we don’t: maternal genetic nurture and child BMI trajectories

    Behavior Genetics 50:310–319.

    https://doi.org/10.1007/s10519-020-10008-w

    • PubMed
    • Google Scholar
35. 1. Tyrrell J
    2. Richmond RC
    3. Palmer TM
    4. Feenstra B
    5. Rangarajan J
    6. Metrustry S
    7. Cavadino A
    8. Paternoster L
    9. Armstrong LL
    10. De Silva NMG
    11. Wood AR
    12. Horikoshi M
    13. Geller F
    14. Myhre R
    15. Bradfield JP
    16. Kreiner-Møller E
    17. Huikari V
    18. Painter JN
    19. Hottenga JJ
    20. Allard C
    21. Berry DJ
    22. Bouchard L
    23. Das S
    24. Evans DM
    25. Hakonarson H
    26. Hayes MG
    27. Heikkinen J
    28. Hofman A
    29. Knight B
    30. Lind PA
    31. McCarthy MI
    32. McMahon G
    33. Medland SE
    34. Melbye M
    35. Morris AP
    36. Nodzenski M
    37. Reichetzeder C
    38. Ring SM
    39. Sebert S
    40. Sengpiel V
    41. Sørensen TIA
    42. Willemsen G
    43. de Geus EJC
    44. Martin NG
    45. Spector TD
    46. Power C
    47. Järvelin MR
    48. Bisgaard H
    49. Grant SFA
    50. Nohr EA
    51. Jaddoe VW
    52. Jacobsson B
    53. Murray JC
    54. Hocher B
    55. Hattersley AT
    56. Scholtens DM
    57. Davey Smith G
    58. Hivert MF
    59. Felix JF
    60. Hyppönen E
    61. Lowe WL
    62. Frayling TM
    63. Lawlor DA
    64. Freathy RM

    (2016) Genetic evidence for causal relationships between maternal obesity-related traits and birth weight

    JAMA 315:1129–1140.

    https://doi.org/10.1001/jama.2016.1975

    • PubMed
    • Google Scholar
36. 1. Wang B
    2. Baldwin JR
    3. Schoeler T
    4. Cheesman R
    5. Barkhuizen W
    6. Dudbridge F
    7. Bann D
    8. Morris TT
    9. Pingault J-B

    (2021) Robust genetic nurture effects on education: A systematic review and meta-analysis based on 38,654 families across 8 cohorts

    American Journal of Human Genetics 108:1780–1791.

    https://doi.org/10.1016/j.ajhg.2021.07.010

    • PubMed
    • Google Scholar
37. 1. Warrington NM
    2. Freathy RM
    3. Neale MC
    4. Evans DM

    (2018) Using structural equation modelling to jointly estimate maternal and fetal effects on birthweight in the UK Biobank

    International Journal of Epidemiology 47:1229–1241.

    https://doi.org/10.1093/ije/dyy015

    • PubMed
    • Google Scholar
38. 1. Warrington NM
    2. Beaumont RN
    3. Horikoshi M
    4. Day FR
    5. Helgeland Ø
    6. Laurin C
    7. Bacelis J
    8. Peng S
    9. Hao K
    10. Feenstra B
    11. Wood AR
    12. Mahajan A
    13. Tyrrell J
    14. Robertson NR
    15. Rayner NW
    16. Qiao Z
    17. Moen G-H
    18. Vaudel M
    19. Marsit CJ
    20. Chen J
    21. Nodzenski M
    22. Schnurr TM
    23. Zafarmand MH
    24. Bradfield JP
    25. Grarup N
    26. Kooijman MN
    27. Li-Gao R
    28. Geller F
    29. Ahluwalia TS
    30. Paternoster L
    31. Rueedi R
    32. Huikari V
    33. Hottenga J-J
    34. Lyytikäinen L-P
    35. Cavadino A
    36. Metrustry S
    37. Cousminer DL
    38. Wu Y
    39. Thiering E
    40. Wang CA
    41. Have CT
    42. Vilor-Tejedor N
    43. Joshi PK
    44. Painter JN
    45. Ntalla I
    46. Myhre R
    47. Pitkänen N
    48. van Leeuwen EM
    49. Joro R
    50. Lagou V
    51. Richmond RC
    52. Espinosa A
    53. Barton SJ
    54. Inskip HM
    55. Holloway JW
    56. Santa-Marina L
    57. Estivill X
    58. Ang W
    59. Marsh JA
    60. Reichetzeder C
    61. Marullo L
    62. Hocher B
    63. Lunetta KL
    64. Murabito JM
    65. Relton CL
    66. Kogevinas M
    67. Chatzi L
    68. Allard C
    69. Bouchard L
    70. Hivert M-F
    71. Zhang G
    72. Muglia LJ
    73. Heikkinen J
    74. EGG Consortium
    75. Morgen CS
    76. van Kampen AHC
    77. van Schaik BDC
    78. Mentch FD
    79. Langenberg C
    80. Luan J
    81. Scott RA
    82. Zhao JH
    83. Hemani G
    84. Ring SM
    85. Bennett AJ
    86. Gaulton KJ
    87. Fernandez-Tajes J
    88. van Zuydam NR
    89. Medina-Gomez C
    90. de Haan HG
    91. Rosendaal FR
    92. Kutalik Z
    93. Marques-Vidal P
    94. Das S
    95. Willemsen G
    96. Mbarek H
    97. Müller-Nurasyid M
    98. Standl M
    99. Appel EVR
    100. Fonvig CE
    101. Trier C
    102. van Beijsterveldt CEM
    103. Murcia M
    104. Bustamante M
    105. Bonas-Guarch S
    106. Hougaard DM
    107. Mercader JM
    108. Linneberg A
    109. Schraut KE
    110. Lind PA
    111. Medland SE
    112. Shields BM
    113. Knight BA
    114. Chai J-F
    115. Panoutsopoulou K
    116. Bartels M
    117. Sánchez F
    118. Stokholm J
    119. Torrents D
    120. Vinding RK
    121. Willems SM
    122. Atalay M
    123. Chawes BL
    124. Kovacs P
    125. Prokopenko I
    126. Tuke MA
    127. Yaghootkar H
    128. Ruth KS
    129. Jones SE
    130. Loh P-R
    131. Murray A
    132. Weedon MN
    133. Tönjes A
    134. Stumvoll M
    135. Michaelsen KF
    136. Eloranta A-M
    137. Lakka TA
    138. van Duijn CM
    139. Kiess W
    140. Körner A
    141. Niinikoski H
    142. Pahkala K
    143. Raitakari OT
    144. Jacobsson B
    145. Zeggini E
    146. Dedoussis GV
    147. Teo Y-Y
    148. Saw S-M
    149. Montgomery GW
    150. Campbell H
    151. Wilson JF
    152. Vrijkotte TGM
    153. Vrijheid M
    154. de Geus EJCN
    155. Hayes MG
    156. Kadarmideen HN
    157. Holm J-C
    158. Beilin LJ
    159. Pennell CE
    160. Heinrich J
    161. Adair LS
    162. Borja JB
    163. Mohlke KL
    164. Eriksson JG
    165. Widén EE
    166. Hattersley AT
    167. Spector TD
    168. Kähönen M
    169. Viikari JS
    170. Lehtimäki T
    171. Boomsma DI
    172. Sebert S
    173. Vollenweider P
    174. Sørensen TIA
    175. Bisgaard H
    176. Bønnelykke K
    177. Murray JC
    178. Melbye M
    179. Nohr EA
    180. Mook-Kanamori DO
    181. Rivadeneira F
    182. Hofman A
    183. Felix JF
    184. Jaddoe VWV
    185. Hansen T
    186. Pisinger C
    187. Vaag AA
    188. Pedersen O
    189. Uitterlinden AG
    190. Järvelin M-R
    191. Power C
    192. Hyppönen E
    193. Scholtens DM
    194. Lowe WL Jr
    195. Davey Smith G
    196. Timpson NJ
    197. Morris AP
    198. Wareham NJ
    199. Hakonarson H
    200. Grant SFA
    201. Frayling TM
    202. Lawlor DA
    203. Njølstad PR
    204. Johansson S
    205. Ong KK
    206. McCarthy MI
    207. Perry JRB
    208. Evans DM
    209. Freathy RM

    (2019) Maternal and fetal genetic effects on birth weight and their relevance to cardio-metabolic risk factors

    Nature Genetics 51:804–814.

    https://doi.org/10.1038/s41588-019-0403-1

    • PubMed
    • Google Scholar
39. 1. Wolf JB
    2. Wade MJ

    (2009) What are maternal effects (and what are they not)?

    Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences 364:1107–1115.

    https://doi.org/10.1098/rstb.2008.0238

    • PubMed
    • Google Scholar
40. 1. Zhang G
    2. Bacelis J
    3. Lengyel C
    4. Teramo K
    5. Hallman M
    6. Helgeland Ø
    7. Johansson S
    8. Myhre R
    9. Sengpiel V
    10. Njølstad PR
    11. Jacobsson B
    12. Muglia L

    (2015) Assessing the causal relationship of maternal height on birth size and gestational age at birth: a mendelian randomization analysis

    PLOS Medicine 12:e1001865.

    https://doi.org/10.1371/journal.pmed.1001865

    • PubMed
    • Google Scholar

Author details

1. Liang-Dar Hwang

   Institute for Molecular Bioscience, The University of Queensland, Brisbane, Australia

   Contribution

   Data curation, Software, Formal analysis, Validation, Investigation, Visualization, Methodology, Writing – original draft, Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-5535-2199

2. Gunn-Helen Moen

   1. Institute for Molecular Bioscience, The University of Queensland, Brisbane, Australia
   2. The University of Queensland Diamantina Institute, The University of Queensland, Brisbane, Australia
   3. Institute for Clinical Medicine, Faculty of Medicine, University of Oslo, Oslo, Norway
   4. K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, Norwegian University of Science and Technology, Trondheim, Norway
   5. Population Health Science, Bristol Medical School, University of Bristol, Bristol, United Kingdom

   Contribution

   Data curation, Software, Validation, Writing – review and editing

   Competing interests

   No competing interests declared

3. David M Evans

   1. Institute for Molecular Bioscience, The University of Queensland, Brisbane, Australia
   2. The University of Queensland Diamantina Institute, The University of Queensland, Brisbane, Australia
   3. MRC Integrative Epidemiology Unit, University of Bristol, Bristol, United Kingdom

   Contribution

   Conceptualization, Resources, Data curation, Software, Formal analysis, Supervision, Funding acquisition, Investigation, Visualization, Methodology, Writing – original draft, Project administration, Writing – review and editing

   For correspondence

   d.evans1@uq.edu.au

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-0663-4621

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