The Interplay between Maternal Smoking and Genes in Offspring Birth Weight

Abstract

It is well-established that both the child's genetic endowments as well as maternal smoking during pregnancy impact offspring birth weight. In this paper we move beyond the nature versus nurture debate by investigating the interaction between genetic endowments and this critical prenatal environmental exposure (maternal smoking) in determining birth weight. We draw on longitudinal data from the Avon Longitudinal Study of Parents and Children (ALSPAC) study and replicate our results using data from the UK Biobank. Genetic endowments of the children are proxied with a polygenic score that is constructed based on the results of the most recent genome wide association study of birth weight. We instrument the maternal decision to smoke during pregnancy with a genetic variant (rs1051730) located in the nicotine receptor gene CHRNA3. This genetic variant is associated with the number of cigarettes consumed daily, and we present evidence that this is plausibly the only channel through which the maternal genetic variant affects the child's birth weight. Additionally, we deal with the misreporting of maternal smoking by using measures of cotinine, a biomarker of nicotine, collected from the mother's urine during their pregnancy. We confirm earlier findings that genetic endowments as well as maternal smoking during pregnancy significantly affects the child's birth weight. However, we do not find evidence of meaningful interactions between genetic endowments and an adverse fetal environment, suggesting that the child's genetic predisposition cannot cushion the damaging effects of maternal smoking.

Gene-Environment Interplay in the Social Sciences

“…This suggests the SNP influences offspring birth weight through maternal smoking, since the association is non-existent among non-smokers. Recent studies have corroborated this finding in two large independent samples (N = 2, 841 and N = 256, 702) (Yang et al, 2020;Pereira et al, 2020). Furthermore, Fletcher (2012) showed how variation in the nicotinic acetylcholine receptor (CHRNA6) moderates the influence of tobacco taxation policy on multiple measures of tobacco use, providing evidence for a gene-policy interaction.…”

“…Positive associations between the SNPs and lung cancer and chronic obstructive pulmonary disease were additionally found (e.g., Bierut, 2010;Thorgeirsson et al, 2008), and both polymorphisms are highly associated with nicotine dependency, with an R 2 of 0.8% (Thorgeirsson et al, 2008). Recent work has suggested an R 2 of 0.4%-0.5% of the SNP rs1051730 for women during pregnancy (Pereira et al, 2020).…”

“…While it is unclear whether this genetic component should be perceived as a fair or unfair source of advantage, it is clear that genetic components matter for the realization of outcomes that society considers important. If one adopts the view that genetic components are unfair sources of advantage, then genetic components and their interactions with socioeconomic indicators could all be included in the share of inequality of opportunity (e.g., Pereira et al, 2020).…”

Genetic predispositions moderate the effectiveness of tobacco excise taxes

“…First, we enrich the literature analyzing smoking behavior and responses to tobacco excise taxes [8][9][10][11][12][13][14][15]20] by overcoming limitations of earlier studies analysing G×E interactions on smoking [21,22]. Second, we contribute to an emerging literature on G×E interactions exploiting exogenous variation in environments that addresses how the environment moderates the effect of genetic variants, and vice versa [28][29][30][31][32][33]. These studies stress that the analysis of exogenous variation in environments is key to overcome bias from gene-environment correlation when estimating G×E interactions.…”

The moderating impact of the genetic predisposition to smoking behaviour on the response to tobacco excise taxes

“…First, we enrich the literature analysing smoking behaviour and responses to tobacco excise taxes (Anderson & Mellor, 2008; Barsky et al, 1997; Boardman, 2009; Clark & Etilé, 2002; Jones, 1994; Kandel et al, 2004; Lahiri & Song, 2000; Nesson, 2017; Yen, 2005) by overcoming limitations of earlier studies analysing G × E interactions on smoking (Fletcher, 2012; Fontana, 2015). Second, we contribute to an emerging literature on gene-environment interactions (G × E) exploiting exogenous variations in environments that addresses how the environment moderates the effect of genetic variants, and vice versa (Barcellos et al, 2018; Conley & Rauscher, 2013; Schmitz & Conley, 2016; Pereira et al, 2020; Schmitz & Conley, 2017). These studies stress that the analysis of exogenous variation in environments is key to overcome bias from gene-environment correlation when estimating G × E interactions.…”