Search tips
Search criteria 


Logo of amjepidLink to Publisher's site
Am J Epidemiol. 2012 December 15; 176(12): 1101–1109.
Published online 2012 November 6. doi:  10.1093/aje/kws190
PMCID: PMC3571234

Diabetes and Obesity-Related Genes and the Risk of Neural Tube Defects in the National Birth Defects Prevention Study


Few studies have evaluated genetic susceptibility related to diabetes and obesity as a risk factor for neural tube defects (NTDs). The authors investigated 23 single nucleotide polymorphisms among 9 genes (ADRB3, ENPP1, FTO, LEP, PPARG, PPARGC1A, SLC2A2, TCF7L2, and UCP2) associated with type 2 diabetes or obesity. Samples were obtained from 737 NTD case-parent triads included in the National Birth Defects Prevention Study during 1999–2007. Log-linear models were used to evaluate maternal and offspring genetic effects. After application of the false discovery rate, there were 5 significant maternal genetic effects. The less common alleles at the 4 FTO single nucleotide polymorphisms showed a reduction of NTD risk (for rs1421085, relative risk (RR) = 0.73 (95% confidence interval (CI): 0.62, 0.87); for rs8050136, RR = 0.79 (95% CI: 0.67, 0.93); for rs9939609, RR = 0.79 (95% CI: 0.67, 0.94); and for rs17187449, RR = 0.80 (95% CI: 0.68, 0.95)). Additionally, maternal LEP rs2071045 (RR = 1.31, 95% CI: 1.08, 1.60) and offspring UCP2 rs660339 (RR = 1.32, 95% CI: 1.06, 1.64) were associated with NTD risk. Furthermore, the maternal genotype for TCF7L2 rs3814573 suggested an increased NTD risk among obese women. These findings indicate that maternal genetic variants associated with glucose homeostasis may modify the risk of having an NTD-affected pregnancy.

Keywords: case-parent triads, diabetes, genetics, neural tube defects, obesity

Neural tube defects (NTDs) are among the most common, most costly, and most deadly of all human congenital anomalies whose etiologies remain largely unknown (1, 2). NTDs include a range of malformations (e.g., spina bifida, anencephaly), which further complicates the identification of risk factors. Two well-established risk factors for NTDs are maternal pregestational diabetes and prepregnancy obesity (312). Although mechanisms underlying these risks remain unclear, there is evidence that infants born to obese mothers and infants born to diabetic mothers may share some common underlying pathogenic exposures, including alteration of glucose homeostasis and hyperglycemia (1318).

Glucose is monitored and regulated by the pancreas and is an essential fuel for oxidative metabolism. During early organogenesis, there is high demand for glucose, since the embryo is dependent on uninterrupted anaerobic glycolysis before the chorioallantoic placenta is developed. Evidence suggests that the early embryo does not have pancreatic function until the development of β cells, which occurs after the seventh week of gestation (19). Thus, at the time of neural tube closure (approximately the fourth week of gestation), mothers with poorly regulated glucose levels are likely to have an altered in utero environment, which cannot be managed by the developing embryo, leading to abnormal organogenesis (2022).

Several genes related to glucose homeostasis have been previously identified in human and animal studies. Furthermore, genes related to glucose homeostasis have been associated with type 2 diabetes and obesity risk in genome-wide association studies (23, 24). A few studies have investigated some of these genes in relation to NTD risk, with positive findings (25, 26); however, these analyses have been limited to a small number of single nucleotide polymorphisms (SNPs) or have not assessed the role of maternal genetic effects. Therefore, our objective in this study was to investigate the roles of several maternal and offspring genes related to glucose homeostasis in the risk of NTDs.


Study population

The study population included NTD case-parent triads (n = 737) from the National Birth Defects Prevention Study (NBDPS), with estimated dates of delivery between January 1, 1999, and December 31, 2007. Details on the NBDPS have been published elsewhere (27). In brief, the NBDPS is a population-based case-control study of major structural birth defects. For the period 1999–2007, case infants with one or more congenital anomalies were ascertained through 10 birth defects surveillance systems throughout the United States (Arkansas, California, Georgia, Iowa, Massachusetts, New Jersey, New York, North Carolina, Texas, and Utah) and included livebirths, stillbirths, and induced abortions (pregnancy terminations). NTDs included in the NBDPS had British Pediatric Association codes for the diagnoses anencephaly (740.0), craniorachischisis (740.1), spina bifida (741.0), and encephalocele (742.0). Abstracted data for all NTD case infants were reviewed by clinical geneticists using specific criteria, including standardized case definitions and confirmatory diagnostic procedures (28). Infants/fetuses with known single gene disorders or chromosomal abnormalities were excluded from the NBDPS. Mothers completed a 1-hour computer-assisted telephone interview in English or Spanish between 6 weeks and 2 years after the estimated date of delivery. The interview included sections on maternal conditions and illnesses, lifestyle and behavioral factors, and multivitamin use.

Candidate genes and SNPs

Candidate genes and SNPs were selected if 1) they were identified as being associated with type 2 diabetes or obesity in multiple genome-wide association studies (i.e., TCF7L2 and FTO) (23, 29) or 2) there was evidence from candidate gene studies coupled with biologic plausibility supported by studies using animal models (e.g., ADRB3, ENPP1, UCP2, LEP, SLC2A2, PPARG, and PPARGC1A) (3035). The selection criteria for each candidate gene and SNP are presented in Web Table 1, which appears on the Journal's website (

Table 1.
Characteristics of Neural Tube Defect Case-Parent Triads (n = 737), National Birth Defects Prevention Study, 1999–2007

DNA samples and genotyping analysis

Buccal swabs were collected from mothers, fathers, and infants as part of the NBDPS (36). DNA was extracted from buccal cells. A standard quality control procedure was applied to each sample before it was submitted to the NBDPS sample repository (36). To assure genotyping proficiency, high quality, and high concordance among all NBDPS laboratories, annual evaluations are conducted to confirm the performance of each laboratory (see Web Appendix). Our laboratory at the Dell Pediatric Research Institute (University of Texas at Austin) has passed all of these evaluations with a score of 100%. SNPs were assayed using TaqMan (Life Technologies Corporation, Carlsbad, California), and genotypes were read and distinguished on the ABI PRISM 7900HT Sequence Detection System (Life Technologies Corporation).

Statistical analysis

The characteristics of case subjects and their parents were summarized using counts and proportions. For each analyzed variant, samples for which a genotype could not be assigned and triads with genotype combinations incompatible with Mendelian inheritance were determined. For each sample, the number of genotyping failures (i.e., genotypes that could not be assigned) was determined. These analyses were performed using Intercooled Stata, version 10.1 (StataCorp LP, College Station, Texas).

Log-linear models were used to assess the association between NTDs and both the offspring and maternal genotypes for each variant (37, 38). To more fully adjust for the effect not being directly assessed, a log-additive model of inheritance was assumed for the genotype being assessed (e.g., the maternal genotype) and an unrestricted model of inheritance was used for the other genotype (e.g., the offspring genotype). This approach provides a 1-degree-of-freedom test for the effect under study. Genotype relative risks and 95% confidence intervals were estimated. In addition, P values for offspring and maternal genetic effects were determined using likelihood ratio tests to compare the log-linear model including terms for both the offspring and maternal genotypes with reduced models that included terms for only the offspring genotype or only the maternal genotype. These analyses were carried out using the LEM program, which allows for the inclusion of incompletely genotyped triads (39, 40).

Analyses were conducted using data from all complete and incomplete triads (i.e., the full group) and stratified according to maternal prepregnancy obesity status (i.e., body mass index (weight (kg)/height (m)2) ≥30 vs. <30), as several of these variants have been associated with obesity. We did not formally assess statistical interactions because of sample size considerations. Additionally, analyses were conducted in 3 subgroups: 1) triads with spina bifida only; 2) triads in which mothers did not have pregestational diabetes; and 3) triads in which mothers did not have pregestational or gestational diabetes. These subgroups were assessed to determine whether the results obtained using data from all triads were influenced by heterogeneity within the full group. We did not stratify on maternal pregestational and gestational diabetes due to small numbers. Because of concerns about population stratification bias when assessing maternal genetic effects, the analyses of the full NTD group were repeated among non-Hispanic whites (37). Finally, due to the number of comparisons, the Benjamini and Hochberg method (the false discovery rate) was used to calculate a “corrected” P value (Q value) accounting for multiple tests in the full group (41).


Participation in the NBDPS for the period 1999–2007 was 74% among NTD case mothers, yielding 1,553 families available for analysis. Among those, 759 (49%) provided buccal swabs (1,787 individuals). Genotyping was performed on DNA samples derived from these 759 families. Based on quality control checks, 18 families (2% of families) were excluded for being inconsistent with Mendelian inheritance at more than 2 genotypes. Additionally, 47 subjects were excluded for failure at more than 11 genotypes (>50%), resulting in 4 more triads' being excluded and leaving a total of 737 case-parent triads (97% of the original sample). Of those, 317 were complete triads, 313 were dyads, and 107 were monads with only 1 person in the family. After these quality control measures were applied, at least 95% of the samples for each variant were available for analyses; therefore, the genotypes were considered of sufficiently high quality.

The distributions of key characteristics among NTD case-parent triads are presented in Table 1. Spina bifida was the most common phenotype among case subjects (n = 449; 60.9%). Furthermore, a majority of case mothers were non-Hispanic white (n = 439; 59.8%). Among case mothers, 176 were obese (25.4%), 13 had prepregnancy diabetes (1.8%), and 29 had gestational diabetes (4.2%). The only characteristics presented in Table 1 that were significantly different between interviewed case mothers who provided buccal swabs and those who did not were race/ethnicity and education (data not shown).

Table 2 shows estimated relative risks (heterozygote vs. common homozygote) and 95% confidence intervals for the association between offspring and maternal genotypes and NTDs, as well as the likelihood ratio test P values and false discovery rate Q values for the model comparisons for each variant. Offspring genotypes for ADRB3, ENPP1, FTO, LEP, PPARG, PPARGC1A, SLC2A2, or TCF7L2 were not associated with NTD risk. However, the offspring genotype for UCP2 rs660339 was associated with NTD risk (relative risk (RR) = 1.32, 95% confidence interval (CI): 1.06, 1.64).

Table 2.
Log-Linear Results for the Association Between Diabetes and Obesity-Related Genes and the Risk of Neural Tube Defects, National Birth Defects Prevention Study, 1999–2007

There was no statistical evidence of associations between maternal genotypes for ADRB3, ENPP1, PPARG, PPARGC1A, SLC2A2, or UCP2 and the risk of NTDs in offspring (Table 2). However, the less common alleles of all FTO genotypes (rs1421085, rs8050136, rs9939609, and rs17817449) were negatively associated with NTD risk among mothers. In contrast, the less common alleles for LEP rs2071045 and TCF7L2 rs3814573 were associated with an elevated risk among mothers (RR = 1.31 (95% CI: 1.08, 1.60) and RR = 1.22 (95% CI: 1.04, 1.44), respectively). Results were similar (e.g., the estimated relative risks were similar) when analyses were restricted to 1) spina bifida cases only, 2) mothers without pregestational diabetes, 3) mothers without pregestational or gestational diabetes, and 4) non-Hispanic whites; therefore, only results for the full group are presented.

When analyses were stratified on the basis of maternal body mass index (Tables 3 and and4),4), the effect of TCF7L2 rs3814573 was stronger among obese women (RR = 1.64, 95% CI: 1.15, 2.33) than among nonobese women (RR = 1.11, 95% CI: 0.92, 1.35). Additionally, none of the FTO genotypes were significantly associated with NTD risk in obese women, whereas these variants were associated with NTD risk in nonobese women. Offspring genetic effects also appeared to differ by maternal prepregnancy obesity (Tables 3 and and4).4). For instance, the association between NTD risk and UCP2 rs660339 was stronger for the offspring of obese women (RR = 1.74, 95% CI: 1.14, 2.64) than for the offspring of nonobese women (RR = 1.19, 95% CI: 0.91, 1.55). The offspring genetic effect of LEP rs3828942 also differed on the basis of maternal prepregnancy obesity, whereby the less common allele was associated with a reduced risk in offspring of obese women (RR = 0.66, 95% CI: 0.44, 0.98) and an increased risk in offspring of nonobese women (RR = 1.31, 95% CI: 1.00, 1.72). This was also true for LEP rs12706831, whereby the less common allele was associated with a reduced risk in offspring of obese women (RR = 0.69, 95% CI: 0.45, 1.06) and an increased risk in offspring of nonobese women (RR = 1.31, 95% CI: 1.01, 1.69).

Table 3.
Log-Linear Results Among Obese Mothers for the Association Between Diabetes and Obesity-Related Genes and the Risk of Neural Tube Defects, National Birth Defects Prevention Study, 1999–2007
Table 4.
Log-Linear Results Among Nonobese Mothers for the Association Between Diabetes and Obesity-Related Genes and the Risk of Neural Tube Defects, National Birth Defects Prevention Study, 1999–2007

Because of the number of comparisons, we applied the false discovery rate in the full group. Although none of the offspring genetic effects remained statistically significant, 5 of the 6 significant maternal genetic effects in Table 2 (the 4 FTO genotypes and LEP rs2071045) remained significant at P < 0.05.


We evaluated the risk of NTDs associated with maternal and offspring genetic effects of 23 SNPs for 9 diabetes and obesity-related genes. There were significant associations between maternal variants in FTO, TCF7L2, and LEP genes and the risk of NTDs in offspring after applying the false discovery rate. Additionally, an offspring variant in the UCP2 gene was associated with NTD risk, although this association did not remain significant after applying the false discovery rate.

The fat mass and obesity-associated gene (FTO) has been identified as a risk factor for obesity through several genome-wide association studies and has been confirmed in multiple populations (29, 4244). Minor alleles of 4 common intronic SNPs—rs1421085 (C), rs8050136 (A), rs9939609 (A), and rs17817449 (G)—are associated with increased body mass, increased obesity risk, and increased FTO protein (α-ketoglutarate-dependent dioxygenase) expression (43, 4549). The FTO protein is believed to play a role in controlling feeding behavior and energy expenditure. However, biologic mechanisms by which FTO contributes to common obesity remain unknown, partly because of discrepancies between animal studies and observations in humans (5052). Minor alleles in the maternal genotypes of these 4 SNPs were significantly associated with a decreased NTD risk in offspring. Three variants (rs8050136, rs9939609, and rs17817449) are located in a 7-kilobase region in intron 1 of the FTO gene and are in strong linkage disequilibrium (47); however, rs1421085 is 12.5 kilobases away from this region and is not in linkage disequilibrium with the other variants. Because the minor alleles of these SNPs were negatively associated with NTD risk among nonobese mothers in our population, the FTO genotypes may be associated with NTDs through mechanisms other than maternal obesity (e.g., an ancestral survival advantage related to fat accumulation) (4, 912, 53).

The T-cell factor 7-like 2 gene (TCF7L2) harbors the variants with the strongest association with type 2 diabetes risk identified to date (23). In recent years, it has become clear that TCF7L2 is not only a key determinant of β-cell mass in the pancreas but is also essential for maintaining the secretory function in mature β cells and glucose homeostasis (45, 5457). SNP rs3814573 was found to be associated with both type 2 diabetes risk and age of onset in Mexican Americans (58). In our study, we observed a maternal effect of the rs3814573 T allele and increased NTD risk. When we stratified by maternal obesity status, the relative risk was higher among obese women than among nonobese women. This finding suggests that the maternal genetic effect of the TCF7L2 rs3814573 genotype on NTD risk may be different between obese women and nonobese women. The estimated relative risk remained the same when the analysis was restricted to nondiabetic women (data not shown).

Leptin is a hormone that is produced and secreted by white adipose tissue and has profound effects on eating behavior, metabolic rate, endocrine axes, and glucose homeostasis. Leptin deficiency in both mice and humans causes morbid obesity and diabetes, and replacement treatment leads to decreased food intake, normalized glucose homeostasis, and increased energy expenditure (59). In a previous study, Shaw et al. (60) reported a modest increase in spina bifida risk among infants carrying 2 genetic markers adjacent to the LEP gene irrespective of maternal body mass index. In this study, we observed a modest increase in NTD risk among women who carried the risk allele of SNP rs2071045. There appeared to be differences in effect between obese and nonobese women for selected LEP SNPs. For instance, we observed a positive association between offspring genotypes of LEP rs3828942 and rs12706831 and NTD risk among nonobese women, whereas in obese women, the minor allele was protective. Our findings add evidence in support of the hypothesis that leptin/leptin receptor signaling is involved in maternal obesity-related NTD risk (25, 26, 60).

Uncoupling proteins are mitochondrial membrane transporters that play an important role in the pathogenesis of various metabolic disorders, including obesity and diabetes (61, 62). The nonsynonymous variant of the uncoupling protein 2 gene (UCP2), Ala55Val (rs660339), has been associated with body fat distribution and obesity (63, 64). Our colleagues previously reported a 2-fold increase in NTD risk among infants who carried the risk allele at rs660339 in a California population (30). However, this association was not observed in an Irish population (65). Our current data revealed a modest increase in NTD risk among infants who carried the risk allele, which is consistent with our previous finding. Additionally, the offspring genetic effect was greater among obese women than among nonobese women.

An important strength of our study was the use of data from the NBDPS, the largest population-based study of birth defects to be conducted to date, which provided us with a unique opportunity to examine both maternal and offspring genetic effects on NTD risk. We employed a case-parent triad design, which is immune to confounding by race/ethnicity (i.e., population stratification) in the assessment of offspring genotypes (37). Additionally, we restricted our analyses to non-Hispanic whites to limit population stratification in the assessment of maternal genetic effects. The log-linear modeling approach to analyses also allowed us to include data from incomplete triads (i.e., genotype data were missing for one or two individuals) (40, 66). An additional strength of the NBDPS is the extensive and standardized case review employed by clinical geneticists, which maximizes homogeneity among case groups. The main weakness of this study was the limited proportion of families with biologic samples available because of the generally low participation rates for contributing biologic samples (49%). Additionally, we did not conduct haplotype association analyses, because the SNPs selected for this study were primarily those identified as being associated with diabetes and/or obesity risk or because they were functional variants in these candidate genes, rather than haplotype tagging SNPs. The low percentage of families on which the genetic findings were based could limit our ability to generalize these results. However, we do not think that the demographic differences between persons who were included in this study and those who were not included were associated with genotypes.

In conclusion, our findings suggest that genetic variants associated with glucose metabolism may modify a woman's risk of having an NTD-affected pregnancy. The maternal effects of FTO, TCF7L2, and LEP genes may also provide evidence regarding the molecular mechanisms underlying the development NTDs. Replication of these findings in other populations and investigation of additional genes is warranted. Furthermore, since maternal obesity and diabetes are also risk factors for other malformations (5, 8, 67), assessing the association between these variants and other birth defects will broaden our understanding of diabetes and obesity-related teratogenicity.

Supplementary Material

Web Material:


Author affiliations: Human Genetics Center, Division of Epidemiology, Human Genetics and Environmental Sciences, School of Public Health, University of Texas, Houston, Texas (Philip J. Lupo, A. J. Agopian, Laura E. Mitchell); Birth Defects Epidemiology and Surveillance Branch, Texas Department of State Health Services, Austin, Texas (Mark A. Canfield); Dell Pediatric Research Institute, Department of Nutritional Sciences, University of Texas at Austin, Austin, Texas (Claudia Chapa, Wei Lu, Richard H. Finnell, Huiping Zhu); Department of Pediatrics, School of Medicine, Stanford University, Stanford, California (Gary M. Shaw); Division of Epidemiology, Human Genetics and Environmental Sciences, School of Public Health, University of Texas, Houston, Texas (D. Kim Waller); and Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina (Andrew F. Olshan).

This study was supported by the National Institute of Child Health and Human Development (grant R21 HD 058912 to H. Zhu). It was also supported by Centers for Disease Control and Prevention (CDC) Centers for Excellence Award U50/CCU925286 (California), CDC Centers for Excellence Award U01/DD000494 (Texas), and National Institutes of Health grant R01 NS 050249.

The authors thank the Maternal, Child and Adolescent Health Division of the California Department of Public Health for providing California surveillance data for this study. The authors also thank Adrian Guzman and Consuelo Vega for technical assistance.

The findings and conclusions in this report are those of the authors and do not necessarily represent the official position of the CDC or the California Department of Public Health.

Conflict of interest: none declared.


1. Campbell LR, Dayton DH, Sohal GS. Neural tube defects: a review of human and animal studies on the etiology of neural tube defects. Teratology. 1986;34(2):171–187. [PubMed]
2. Ouyang L, Grosse SD, Armour BS, et al. Health care expenditures of children and adults with spina bifida in a privately insured U.S. population. Birth Defects Res A Clin Mol Teratol. 2007;79(7):552–558. [PubMed]
3. Shaw GM, Todoroff K, Finnell RH, et al. Spina bifida phenotypes in infants or fetuses of obese mothers. Teratology. 2000;61(5):376–381. [PubMed]
4. Shaw GM, Velie EM, Schaffer D. Risk of neural tube defect-affected pregnancies among obese women. JAMA. 1996;275(14):1093–1096. [PubMed]
5. Watkins ML, Rasmussen SA, Honein MA, et al. Maternal obesity and risk for birth defects. Pediatrics. 2003;111(5):1152–1158. [PubMed]
6. Watkins ML, Scanlon KS, Mulinare J, et al. Is maternal obesity a risk factor for anencephaly and spina bifida? Epidemiology. 1996;7(5):507–512. [PubMed]
7. Soler NG, Walsh CH, Malins JM. Congenital malformations in infants of diabetic mothers. Q J Med. 1976;45(178):303–313. [PubMed]
8. Waller DK, Shaw GM, Rasmussen SA, et al. Prepregnancy obesity as a risk factor for structural birth defects. National Birth Defects Prevention Study. Arch Pediatr Adolesc Med. 2007;161(8):745–750. [PubMed]
9. Waller DK, Mills JL, Simpson JL, et al. Are obese women at higher risk for producing malformed offspring? Am J Obstet Gynecol. 1994;170(2):541–548. [PubMed]
10. Hendricks KA, Nuno OM, Suarez L, et al. Effects of hyperinsulinemia and obesity on risk of neural tube defects among Mexican Americans. Epidemiology. 2001;12(6):630–635. [PubMed]
11. Werler MM, Louik C, Shapiro S, et al. Prepregnant weight in relation to risk of neural tube defects. JAMA. 1996;275(14):1089–1092. [PubMed]
12. Källén K. Maternal smoking, body mass index, and neural tube defects. Am J Epidemiol. 1998;147(12):1103–1111. [PubMed]
13. Cabrera RM, Hill DS, Etheredge AJ, et al. Investigations into the etiology of neural tube defects. Birth Defects Res C Embryo Today. 2004;72(4):330–344. [PubMed]
14. Andreasen KR, Andersen ML, Schantz AL. Obesity and pregnancy. Acta Obstet Gynecol Scand. 2004;83(11):1022–1029. [PubMed]
15. Carmichael SL, Rasmussen SA, Shaw GM. Prepregnancy obesity: a complex risk factor for selected birth defects. Birth Defects Res A Clin Mol Teratol. 2010;88(10):804–810. [PubMed]
16. King JC. Maternal obesity, metabolism, and pregnancy outcomes. Annu Rev Nutr. 2006;26:271–291. [PubMed]
17. Ray JG, Thompson MD, Vermeulen MJ, et al. Metabolic syndrome features and risk of neural tube defects. BMC Pregnancy Childbirth. 2007;7:21. doi:10.1186/1471-2393-7-21. [PMC free article] [PubMed]
18. Scialli AR. Teratology Public Affairs Committee position paper: maternal obesity and pregnancy. Birth Defects Res A Clin Mol Teratol. 2006;76(2):73–77. [PubMed]
19. Jovanovic-Peterson L, Peterson CM. Abnormal metabolism and the risk for birth defects with emphasis on diabetes. Ann N Y Acad Sci. 1993;678:228–243. [PubMed]
20. Trocino RA, Akazawa S, Takino H, et al. Cellular-tissue localization and regulation of the GLUT-1 protein in both the embryo and the visceral yolk sac from normal and experimental diabetic rats during the early postimplantation period. Endocrinology. 1994;134(2):869–878. [PubMed]
21. Maeda Y, Akazawa S, Akazawa M, et al. Glucose transporter gene expression in rat conceptus during early organogenesis and exposure to insulin-induced hypoglycemic serum. Acta Diabetol. 1993;30(2):73–78. [PubMed]
22. Takao Y, Akazawa S, Matsumoto K, et al. Glucose transporter gene expression in rat conceptus during high glucose culture. Diabetologia. 1993;36(8):696–706. [PubMed]
23. Zeggini E, McCarthy MI. TCF7L2: the biggest story in diabetes genetics since HLA? Diabetologia. 2007;50(1):1–4. [PMC free article] [PubMed]
24. Tung YC, Yeo GS. From GWAS to biology: lessons from FTO. Ann N Y Acad Sci. 2011;1220(1):162–171. [PubMed]
25. Carter TC, Pangilinan F, Troendle JF, et al. Evaluation of 64 candidate single nucleotide polymorphisms as risk factors for neural tube defects in a large Irish study population. Am J Med Genet A. 2011;155A(1):14–21. [PMC free article] [PubMed]
26. Davidson CM, Northrup H, King TM, et al. Genes in glucose metabolism and association with spina bifida. Reprod Sci. 2008;15(1):51–58. [PMC free article] [PubMed]
27. Yoon PW, Rasmussen SA, Lynberg MC, et al. The National Birth Defects Prevention Study. Public Health Rep. 2001;116(suppl 1):32–40. [PMC free article] [PubMed]
28. Rasmussen SA, Olney RS, Holmes LB, et al. Guidelines for case classification for the National Birth Defects Prevention Study. Birth Defects Res A Clin Mol Teratol. 2003;67(3):193–201. [PubMed]
29. Scott LJ, Mohlke KL, Bonnycastle LL, et al. A genome-wide association study of type 2 diabetes in Finns detects multiple susceptibility variants. Science. 2007;316(5829):1341–1345. [PMC free article] [PubMed]
30. Volcik KA, Shaw GM, Zhu H, et al. Risk factors for neural tube defects: associations between uncoupling protein 2 polymorphisms and spina bifida. Birth Defects Res A Clin Mol Teratol. 2003;67(3):158–161. [PubMed]
31. Pizzuti A, Frittitta L, Argiolas A, et al. A polymorphism (K121Q) of the human glycoprotein PC-1 gene coding region is strongly associated with insulin resistance. Diabetes. 1999;48(9):1881–1884. [PubMed]
32. Ohnuma H, Yamatani K, Daimon M, et al. Impaired neural regulation of insulin secretion related to the leptin receptor gene mutation in Wistar fatty rats. Physiol Behav. 2000;70(5):527–532. [PubMed]
33. Zhang Y, Wat N, Stratton IM, et al. UKPDS 19: heterogeneity in NIDDM: separate contributions of IRS-1 and beta 3-adrenergic-receptor mutations to insulin resistance and obesity respectively with no evidence for glycogen synthase gene mutations. UK Prospective Diabetes Study. Diabetologia. 1996;39(12):1505–1511. [PubMed]
34. Nitz I, Ewert A, Klapper M, et al. Analysis of PGC-1α variants Gly482Ser and Thr612Met concerning their PPARγ2-coactivation function. Biochem Biophys Res Commun. 2007;353(2):481–486. [PubMed]
35. Li R, Thorens B, Loeken MR. Expression of the gene encoding the high-Km glucose transporter 2 by the early postimplantation mouse embryo is essential for neural tube defects associated with diabetic embryopathy. Diabetologia. 2007;50(3):682–689. [PubMed]
36. Rasmussen SA, Lammer EJ, Shaw GM, et al. Integration of DNA sample collection into a multi-site birth defects case-control study. National Birth Defects Prevention Study. Teratology. 2002;66(4):177–184. [PubMed]
37. Weinberg CR, Wilcox AJ, Lie RT. A log-linear approach to case-parent-triad data: assessing effects of disease genes that act either directly or through maternal effects and that may be subject to parental imprinting. Am J Hum Genet. 1998;62(4):969–978. [PubMed]
38. Kistner EO, Weinberg CR. Method for using complete and incomplete trios to identify genes related to a quantitative trait. Genet Epidemiol. 2004;27(1):33–42. [PubMed]
39. Vermunt JK. LEM: A General Program for the Analysis of Categorical Data. Tilberg, the Netherlands: Tilberg University; 1997.
40. Weinberg CR. Allowing for missing parents in genetic studies of case-parent triads. Am J Hum Genet. 1999;64(4):1186–1193. [PubMed]
41. Benjamini Y, Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc B. 1995;57(1):289–300.
42. Dina C, Meyre D, Gallina S, et al. Variation in FTO contributes to childhood obesity and severe adult obesity. Nat Genet. 2007;39(6):724–726. [PubMed]
43. Scuteri A, Sanna S, Chen WM, et al. Genome-wide association scan shows genetic variants in the FTO gene are associated with obesity-related traits. PLoS Genet. 2007;3(7):e115. doi:10.1371/journal.pgen.0030115 doi:10.1371/journal.pgen.0030115. [PubMed]
44. Peng S, Zhu Y, Xu F, et al. FTO gene polymorphisms and obesity risk: a meta-analysis. BMC Med. 2011;9:71. doi:10.1186/1741-7015-9-71 doi:10.1186/1741-7015-9-71. [PMC free article] [PubMed]
45. Frayling TM, Timpson NJ, Weedon MN, et al. A common variant in the FTO gene is associated with body mass index and predisposes to childhood and adult obesity. Science. 2007;316(5826):889–894. [PMC free article] [PubMed]
46. Wing MR, Ziegler JM, Langefeld CD, et al. Analysis of FTO gene variants with obesity and glucose homeostasis measures in the multiethnic Insulin Resistance Atherosclerosis Study cohort. Int J Obes (Lond) 2011;35(9):1173–1182. [PubMed]
47. Nock NL, Plummer SJ, Thompson CL, et al. FTO polymorphisms are associated with adult body mass index (BMI) and colorectal adenomas in African-Americans. Carcinogenesis. 2011;32(5):748–756. [PMC free article] [PubMed]
48. Chauhan G, Tabassum R, Mahajan A, et al. Common variants of FTO and the risk of obesity and type 2 diabetes in Indians. J Hum Genet. 2011;56(10):720–726. [PubMed]
49. Berulava T, Horsthemke B. The obesity-associated SNPs in intron 1 of the FTO gene affect primary transcript levels. Eur J Hum Genet. 2010;18(9):1054–1056. [PMC free article] [PubMed]
50. Groop L. From fused toes in mice to human obesity. Nat Genet. 2007;39(6):706–707. [PubMed]
51. Fawcett KA, Barroso I. The genetics of obesity: FTO leads the way. Trends Genet. 2010;26(6):266–274. [PMC free article] [PubMed]
52. Meyre D, Proulx K, Kawagoe-Takaki H, et al. Prevalence of loss-of-function FTO mutations in lean and obese individuals. Diabetes. 2010;59(1):311–318. [PMC free article] [PubMed]
53. Mojtabai R. Body mass index and serum folate in childbearing age women. Eur J Epidemiol. 2004;19(11):1029–1036. [PubMed]
54. Hansson O, Zhou Y, Renström E, et al. Molecular function of TCF7L2: consequences of TCF7L2 splicing for molecular function and risk for type 2 diabetes. Curr Diab Rep. 2010;10(6):444–451. [PubMed]
55. Shu L, Sauter NS, Schulthess FT, et al. Transcription factor 7-like 2 regulates beta-cell survival and function in human pancreatic islets. Diabetes. 2008;57(3):645–653. [PubMed]
56. Shu L, Matveyenko AV, Kerr-Conte J, et al. Decreased TCF7L2 protein levels in type 2 diabetes mellitus correlate with downregulation of GIP- and GLP-1 receptors and impaired beta-cell function. Hum Mol Genet. 2009;18(13):2388–2399. [PMC free article] [PubMed]
57. da Silva Xavier G, Loder MK, McDonald A, et al. TCF7L2 regulates late events in insulin secretion from pancreatic islet beta-cells. Diabetes. 2009;58(4):894–905. [PMC free article] [PubMed]
58. Lehman DM, Hunt KJ, Leach RJ, et al. Haplotypes of transcription factor 7-like 2 (TCF7L2) gene and its upstream region are associated with type 2 diabetes and age of onset in Mexican Americans. Diabetes. 2007;56(2):389–393. [PubMed]
59. Gautron L, Elmquist JK. Sixteen years and counting: an update on leptin in energy balance. J Clin Invest. 2011;121(6):2087–2093. [PMC free article] [PubMed]
60. Shaw GM, Barber R, Todoroff K, et al. Microsatellites proximal to leptin and leptin receptor as risk factors for spina bifida. Teratology. 2000;61(3):231–235. [PubMed]
61. Stark R, Roden M. ESCI Award 2006. Mitochondrial function and endocrine diseases. Eur J Clin Invest. 2007;37(4):236–248. [PubMed]
62. Dalgaard LT. Genetic variance in uncoupling protein 2 in relation to obesity, type 2 diabetes, and related metabolic traits: focus on the functional -866G>A promoter variant (rs659366) J Obes. 2011;2011:340241. doi:10.1155/2011/340241 doi:10.1155/2011/340241. [PMC free article] [PubMed]
63. Martinez-Hervas S, Mansego ML, de Marco G, et al. Polymorphisms of the UCP2 gene are associated with body fat distribution and risk of abdominal obesity in Spanish population. Eur J Clin Invest. 2012;42(2):171–178. [PubMed]
64. Kosuge K, Soma M, Nakayama T, et al. Human uncoupling protein 2 and 3 genes are associated with obesity in Japanese. Endocrine. 2008;34(1–3):87–95. [PubMed]
65. Mitchell A, Pangilinan F, Van der Meer J, et al. Uncoupling protein 2 polymorphisms as risk factors for NTDs. Birth Defects Res A Clin Mol Teratol. 2009;85(2):156–160. [PMC free article] [PubMed]
66. Dempster AP Laird NM, Rubin DB. Maximum likelihood from incomplete data via the EM algorithm. J R Stat Soc Ser B. 1977;39(1):1–28.
67. Correa A, Gilboa SM, Besser LM, et al. Diabetes mellitus and birth defects. Am J Obstet Gynecol. 2008;199(3):237.e1–237.e9. [PubMed]

Articles from American Journal of Epidemiology are provided here courtesy of Oxford University Press