Examining Prenatal Dietary Factors in Association with Child Autism-Related Traits Using a Bayesian Mixture Approach: Results from 2 United States Cohorts

Background

Prior work has suggested relationships between prenatal intake of certain nutrients and autism.

Objectives

We examined a broad set of prenatal nutrients and foods using a Bayesian modeling approach.

Methods

Participants were drawn from the Early Autism Risks Longitudinal Investigation (n = 127), a cohort following women with a child with autism through a subsequent pregnancy. Participants were also drawn from the Nurses' Health Study II (NHSII, n = 713), a cohort of United States female nurses, for comparison analyses. In both studies, information on prospectively reported prenatal diet was drawn from food frequency questionnaires, and child autism-related traits were measured by the Social Responsiveness Scale (SRS). Bayesian kernel machine regression was used to examine the combined effects of several nutrients with neurodevelopmental relevance, including polyunsaturated fatty acids (PUFAs), iron, zinc, vitamin D, folate, and other methyl donors, and separately, key food sources of these, in association with child SRS scores in crude and adjusted models.

Results

In adjusted analyses, the overall mixture effects of nutrients in Early Autism Risks Longitudinal Investigation and foods in both cohorts on SRS scores were not observed, though there was some suggestion of decreasing SRS scores with increasing overall nutrient mixture in NHSII. No associations were observed with folate within the context of this mixture, but holding other nutrients fixed, n-6 PUFAs were associated with lower SRS scores in NHSII. In both cohorts, lower SRS scores were observed with higher intake of some groupings of vegetables, though for differing types of vegetables across cohorts, and some vegetable groups were associated with higher SRS scores in NHSII.

Conclusions

Our work extends prior research and suggests the need to further consider prenatal dietary factors from a combined effects perspective. In addition, findings here point to potential differences in nutrient associations based on a family history of autism, which suggests the need to consider gene interactions in future work.

Examining gene-environment interactions in comorbid depressive and disruptive behavior disorders using a Bayesian approach

OBJECTIVE

The objective of this study was to apply a Bayesian statistical analytic approach that minimizes multiple testing problems to explore the combined effects of chronic low familial support and variants in 12 candidate genes on risk for a common and debilitating childhood mental health condition.

METHOD

Bayesian mixture modeling was used to examine gene by environment interactions among genetic variants and environmental factors (family support) associated in previous studies with the occurrence of comorbid depression and disruptive behavior disorders youth, using a sample of 255 children.

RESULTS

One main effect, variants in the oxytocin receptor (OXTR, rs53576) was associated with increased risk for comorbid disorders. Two significant gene × environment and one signification gene × gene interactions emerged. Variants in the nicotinic acetylcholine receptor α5 subunit (CHRNA5, rs16969968) and in the glucocorticoid receptor chaperone protein FK506 binding protein 5 (FKBP5, rs4713902) interacted with chronic low family support in association with child mental health status. One gene × gene interaction, 5-HTTLPR variant of the serotonin transporter (SERT/SLC6A4) in combination with μ opioid receptor (OPRM1, rs1799971) was associated with comorbid depression and conduct problems.

CONCLUSIONS

Results indicate that Bayesian modeling is a feasible strategy for conducting behavioral genetics research. This approach, combined with an optimized genetic selection strategy (Vrieze et al., 2012), revealed genetic variants involved in stress regulation (FKBP5, SERT × OPMR), social bonding (OXTR), and nicotine responsivity (CHRNA5) in predicting comorbid status.