Neural Tube Defects and Folate Pathway Genes: Family-Based Association Tests of Gene–Gene and Gene–Environment Interactions

BACKGROUND: Folate metabolism pathway genes have been examined for association with neural tube defects (NTDs) because folic acid supplementation reduces the risk of this debilitating birth defect. Most studies addressed these genes individually, often with different populations providing conflicting results. OBJECTIVES: Our study evaluates several folate pathway genes for association with human NTDs, incorporating an environmental cofactor: maternal folate supplementation. METHODS: In 304 Caucasian American NTD families with myelomeningocele or anencephaly, we examined 28 polymorphisms in 11 genes: folate receptor 1, folate receptor 2, solute carrier family 19 member 1, transcobalamin II, methylenetetrahydrofolate dehydrogenase 1, serine hydroxymethyl-transferase 1, 5,10-methylenetetrahydrofolate reductase (MTHFR), 5-methyltetrahydrofolate-homo-cysteine methyltransferase, 5-methyltetrahydrofolate-homocysteine methyltransferase reductase, betaine-homocysteine methyltransferase (BHMT), and cystathionine-beta-synthase. RESULTS: Only single nucleotide polymorphisms (SNPs) in BHMT were significantly associated in the overall data set; this significance was strongest when mothers took folate-containing nutritional supplements before conception. The BHMT SNP rs3733890 was more significant when the data were stratified by preferential transmission of the MTHFR rs1801133 thermolabile T allele from parent to offspring. Other SNPs in folate pathway genes were marginally significant in some analyses when stratified by maternal supplementation, MTHFR, or BHMT allele transmission. CONCLUSIONS: BHMT rs3733890 is significantly associated in our data set, whereas MTHFR rs1801133 is not a major risk factor. Further investigation of folate and methionine cycle genes will require extensive SNP genotyping and/or resequencing to identify novel variants, inclusion of environmental factors, and investigation of gene–gene interactions in large data sets.