Abstract
This study uses instrumental variable (IV) models with genetic instruments to assess the effects of maternal smoking on the child’s risk of orofacial clefts (OFC), a common birth defect. The study uses genotypic variants in neurotransmitter and detoxification genes relateded to smoking as instruments for cigarette smoking before and during pregnancy. Conditional maximum likelihood and two-stage IV probit models are used to estimate the IV model. The data are from a population-level sample of affected and unaffected children in Norway. The selected genetic instruments generally fit the IV assumptions but may be considered “weak” in predicting cigarette smoking. We find that smoking before and during pregnancy increases OFC risk substantially under the IV model (by about 4–5 times at the sample average smoking rate). This effect is greater than that found with classical analytic models. This may be because the usual models are not able to consider self-selection into smoking based on unobserved confounders, or it may to some degree reflect limitations of the instruments. Inference based on weak-instrument robust confidence bounds is consistent with standard inference. Genetic instruments may provide a valuable approach to estimate the “causal” effects of risk behaviors with genetic-predisposing factors (such as smoking) on health and socioeconomic outcomes.
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Notes
The variant rs1051730 is also related to quitting and intensity of smoking during pregnancy (Freathy et al. 2009).
Cleft lip only and cleft lip with palate are commonly grouped in the literature given their similarities in development and recurrence risks.
The specific questions on smoking and other survey questions can be found on http://www.niehs.nih.gov/research/atniehs/labs/epi/studies/ncl/question.cfm.). We do not have direct data on changes in smoking amounts within these periods, but these may be partly reflected in the responses to average smoking intensity as described above. We focus on estimating the overall average effects of cigarette quantity per day for the days when the women smoked in these periods.
About 27% of the mothers in the study sample reported that the pregnancy was unplanned.
These SNPs pass the Hardy–Weinberg Equilibrium (HWE) test at P = 0.34–0.9. The HWE test evaluates whether the genotypic proportions at each SNP express deviations from an equilibrium/constant distribution due to non-random mating and other factors that may result in such deviations. Higher P values suggest more confidence in not rejecting the random mating hypothesis and in lack of deviation from the HWE equilibrium.
We include one indicator because we find that the effects of the two indicators for each SNP are in the same direction and generally insignificant from each other, except for SNP rs1435252, for which these two effects are significantly different at P < 0.05. Therefore, we keep two indicators for SNP rs1435252. Another advantage of reducing the number of instruments is that in the presence of “weak instruments”, which is the case for these instruments as described below, the bias in the IV estimate increases with the number of instruments (Hahn and Hausman 2003).
The Norway sample had also been genotyped independently of the CIDR project for several other variants in GABAB2 as well as DDC, and CHRNA4, which are genes that have also been implicated in smoking behaviors (Ma et al. 2005; (Li et al. 2005). However, these variants had insignificant effects on smoking during the first pregnancy trimester. Supplementary Table S2 reports the results for the relationship between these additional variants and first trimester smoking.
Some studies report that maternal alcohol consumption, mainly excessive consumption, increases the risk of OFC (e.g. (Grewal et al. 2008; Romitti et al. 1999). However, other studies report insignificant (e.g. (Bille et al. 2007; Meyer et al. 2003). Some studies also report that maternal obesity increases OFC risk (e.g. (Stothard et al. 2009; Villamor et al. 2008) but others do not find significant effects (Shaw et al. 2000). Food insecurity may also increase cleft palate risk (Carmichael et al. 2007). Low socioeconomic resources may increase OFC risk (Clark et al. 2003; Durning et al. (2007); Yang et al. 2008). We include pregnancy intendedness as it reflects maternal preferences for risk taking and may proxy for some unobserved confounders.
This approach averages the predicted probabilities from Eq. 3 across all observations i (so that the predicted effects are averaged across ei).
In models that include only smoking and no covariates, the OR as defined in Eq. 4 that is obtained from a probit function is identical to that from a logistic function, which is the standard regression model used in case–control or case–cohort designs. For example, both logistic regression and probit regression of OFC on smoking participation (yes/no) in the first trimester provide an OR estimate of 1.54. When the model includes additional covariates, the odds ratio from the probit model is expected to differ slightly from the logistic regression OR, because unlike the logistic regression OR, the probit OR is dependent on the covariate values. However, the ORs from the two models are still expected to be very close to each other. For example, when adding the covariates described above into the model, the OR for first trimester smoking participation is 1.379 and 1.370 in the probit and logistic regressions, respectively.
For the bootstraps, we set the maximum number of iterations for each IV-probit CML regression to 10. Using the full study sample, The CML model converged at five iterations with cigarettes before pregnancy and six iterations for first trimester cigarettes.
The “threshold” for weak instruments varies with several parameters including sample size, number of instruments, and other parameters. However, F-statistics below 10 are generally considered to suggest weak-instruments in linear models (Staiger and Stock 1997). There are no rules of thumb for instrument strengths in the CML model, but the instruments may also be weak in that model.
The weak-instrument robust confidence bounds are only available for the Newey’s IV probit estimator and are not available for the other IV probit models (CML IV-Probit or the two-stage IV probit with residual substitution).
We interpret the smoking effects using odds ratios based on the estimated cigarette number regression coefficients by simulating the effects of smoking the average number of cigarettes among smokers relative to not smoking. The average cigarette numbers (9 cigarettes per day before pregnancy and 6 cigarettes per day during the first trimester) used in this simulation are conditional on being a smoker. Note that these effects may not be directly compared to odds ratios for smoking participation (yes/no) effects. One reason is that cigarette number is likely to be measured with some error, which attenuates the effect towards zero. We do not use binary smoking status or categorical indicators for cigarette number because of the limited instrument effects on these measures. Therefore, these effects should not be contrasted with odds ratio effects reported in previous studies using this sample (Lie et al. 2008). For instance, adjusting for the model covariates described above, we estimate an odds ratio of any smoking during the first trimester of 1.38 (95% CI: 1.02; 1.88) for OFC and 1.55 (95% CI: 1.1, 2.17) for cleft lip without palate.
Table S5 in the Supplementary Material reports the full regression results for the OFC function excluding cleft palate only. Table S6 reports the full regression results for the cigarette function.
The regression coefficients cannot be directly compared between these models as they require transformation to obtain the variable effects.
Recent studies have provided several additional candidates for smoking, with confirmed associations for the gene encoding cholinergic receptor, nicotinic, alpha 5 (CHRNA5 on chr 15q24) in multiple, independent studies (Hung et al. 2008; Liu et al. 2010; Saccone et al. (2010); Thorgeirsson et al. 2008, 2010). Additional susceptibility loci have been identified through recent genome-wide association studies, including acetylcholine receptor genes CHRNB3 and CHRNA6 on chr 8p11, egl nine homolog 2 (EGLN2) on chr 9q13, brain-derived neurotrophic factor (BDNF) on chr 11p13, CHRNA3 on chr 15q24, and cytochrome P450 genes CYP2A6 and CYP2B6 on chr 19q13 (2010; McKay et al. 2008; Thorgeirsson et al. 2010).
Unlike for the local average treatment effect of two stage least squares (2SLS), this is not a strict but rather an intuitive interpretation of the effects in the CML and other IV probit models.
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This research was supported by NIH/NIDCR grant 1 R03 DE018394 and in part by NIH/NIDCR grant 1 R01 DE020895-01 and the Intramural Research Program of the NIH, National Institutes of Environmental Health.
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Wehby, G.L., Jugessur, A., Murray, J.C. et al. Genes as instruments for studying risk behavior effects: an application to maternal smoking and orofacial clefts. Health Serv Outcomes Res Method 11, 54–78 (2011). https://doi.org/10.1007/s10742-011-0071-9
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DOI: https://doi.org/10.1007/s10742-011-0071-9