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Logo of nihpaAbout Author manuscriptsSubmit a manuscriptHHS Public Access; Author Manuscript; Accepted for publication in peer reviewed journal;
Int J Pediatr Obes. Author manuscript; available in PMC 2010 August 25.
Published in final edited form as:
Int J Pediatr Obes. 2010 April; 5(2): 177–184.
doi:  10.3109/17477160903111714
PMCID: PMC2928065

Association of maternally inherited GNAS alleles with African–American male birth weight



Human birth weight variation has a significant genetic component and important clinical consequences. We performed a survey of single nucleotide polymorphisms (SNPs) in 14 candidate genes to identify associations with birth weight variation.


SNP variation was surveyed in 221 healthy African–American mother-newborn pairs. Genes were selected based on previous association with obesity-related traits, significant differences in circulating protein levels in low birth weight pregnancies or association with newborn size in model organisms or growth disorders in humans. Association was tested via multiple linear regression with adjustment for significant covariables.


Under a dominant model SNP rs7754561 of ENPPI was significantly associated with birth weight. Among imprinted loci, maternal genotypes for SNP rs6026576 of GNAS were significantly associated with birth weight (additive and dominant models). This association was restricted to male offspring. Analyses that distinguished between alleles of paternal and maternal origin demonstrated that only maternally-transmitted alleles were associated with birth weight and that this association was restricted to male newborns.


The effect of only maternally-transmitted alleles of GNAS may be a consequence of the complex splicing and imprinting pattern of the GNAS gene, although the reason this effect is observed only among male newborns is unclear.

Keywords: African-American, birth weight, genetic association, epigenetics and disease


Being small for gestational age (SGA) is a major risk factor for illness. Term, low birth weight infants are at least five times more likely to die in the first year (1) and are second only to premature infants in their rates of morbidity and mortality (2,3). As adults, individuals born small for gestational age are at elevated risk of pregnancy-induced hypertension (4), gestational diabetes (5), essential hypertension (6), non-insulin dependent diabetes (7,8), and cardiovascular disease (9,10).

Birth weight variation has a substantial genetic component. A man born SGA is 3.5 times more likely to have an SGA child, and a woman born SGA is 4.7 times more likely. If both parents were SGA, the risk is 16.3 times greater (11). Estimated heritability for birth weight is 42–45% (12,13), with a maternal genetic component of 27% and a paternal component of about 18%.

The maternally- and paternally-inherited alleles of most genes are expressed at approximately equal levels during fetal development. However, some genes are imprinted, meaning that only the maternal or paternal allele is expressed in some tissues or at some developmental stages, and a substantial number of these genes affect the rate of fetal growth. For example, the maternal alleles of the insulin-like growth factor 2 (IGF2) and insulin genes are silenced, and Beckwith-Wiedemann overgrowth syndrome is characterized by large fetal size and biallelic expression of IGF2 due to loss of imprinting (14). Imprinting results in parent-of-origin effects in which the phenotypic consequences of an allele are dependent upon its parental origin.

We performed a candidate gene study to identify single nucleotide polymorphisms (SNPs) associated with birth weight variation. Genes and specific SNPs were selected according to three criteria. First, SNPs (i.e., rs7566605 in INSIG2, rs7903146 in TCF7L2, and multiple SNPs in GHRL and GHSR) (1518) previously found to be associated with birth weight or obesity-related traits were genotyped. Second, haplotype tagging SNPs were genotyped for genes whose circulating protein levels significantly differ in low birth weight newborns (i.e., adiponectin, leptin, endothelin) (1921). Third, haplotype tagging SNPs were genotyped in mothers and newborns for imprinted genes that have been associated with significant changes in newborn size in model organisms (i.e., GRB10 and MEST) (22,23) or in human growth disorders (i.e., GNAS) (2426).

Materials and methods


Informed consent was obtained from healthy African-American women (N=221; Table I) with uncomplicated pregnancies during routine prenatal care or upon admission for delivery at the University of Mississippi Medical Center (Jackson, MS) and University of Tennessee Health Science Center (Memphis, TN) from 2004–08. Only subjects who delivered healthy newborns were retained in this study. Inclusion criteria were: singleton pregnancy, >36 weeks gestation, 18–35 years old. Exclusion criteria eliminated many additional factors influencing birth weight and included, among others: diabetes (type 1, type 2, gestational), hypertension/vascular disease, preeclampsia, autoimmune disease, infectious disease (i.e., hepatitis, HIV), sickle-cell disease, uterine infection, illicit drug use, smoking, and birth defects. The Institutional Review Boards of both institutions approved this study.

Table I
Characteristics of the mothers and newborns (N = 221 pairs).


DNA was extracted from maternal and umbilical cord blood. Haplotype tagging SNPs were selected using SNPbrowser software v3.5 (Applied Biosystems, Inc.) and data from the International HapMap Project (27) with the criteria r2 ≥ 0.8 with untyped SNPs and minor allele frequency >20% in the Yoruban population (or Caucasian, if Yoruban data unavailable). SNPs defining principally intronic haplotypes were not genotyped. SNPs were genotyped using TaqMan reagents (Applied Biosystems Inc.). To estimate the proportion of the genome of African descent, 12 unlinked ancestry-informative SNPs (Table II) were genotyped with minor allele frequency differences >80% between Yoruban and Caucasian populations.

Table II
Single nucleotide polymorphisms surveyed in this study.

Statistical analyses

Potential confounders and effect modifiers

Association between birth weight and SNPs (0, 1, or 2 copies of the minor allele) was tested by multiple linear regression with adjustment for confounding variables and possible effect modifiers. Before inclusion of genetic data, a non-genetic model was constructed. Variables considered as confounders or effect modifiers included gestational age (nearest week), maternal age, parity, newborn gender, maternal body mass index, and pregnancy weight gain. Each variable was plotted against birth weight to determine how best to present it. The relationship between maternal body mass index (MBMI) and birth weight is nonlinear with birth weight increasing up to an MBMI of 29 and then slightly decreasing beyond an MBMI of 29. Therefore, MBMI was represented as a linear spline of two variables, MBMI and MBMI29 (0 up to MBMI=29, MBMI–29 beyond MBMI=29).

SNP alleles

In a recently admixed population, differences in subpopulation-specific allele frequencies can produce false positive associations, particularly when the distribution of the outcome (e.g., birth weight) differs across subpopulations (28,29). To genomic ancestry in each subject using the program adjust for admixture between African and European Admixmap (30). These estimates were based on populations, we estimated the proportion of African European and African allele frequencies (31) and both the candidate gene and ancestry-informative SNPs. These estimates were included as a covariable in regression analyses.

A priori it is unknown if an additive, recessive, or dominant genetic model best fits the data. Explicitly testing each model at each SNP requires a very stringent threshold of significance under a Bonferroni-type correction. However, permutation can be used to estimate P values without a loss of power (32). We tested all three genetic models and calculated P values by permuting the dependent variable (birth weight) 100 000 times. The proportion of permuted data sets that produced a test statistic for the significance of the SNP variable equal to or more extreme than the observed test statistic provided an empirical P value (33).

Imprinting sometimes exhibits gender-specific effects on phenotype (34). Therefore, for GNAS SNP rs6026576, which is complexly imprinted and significantly associated with birth weight, regression was performed as described above, but with the addition of a gender x genotype interaction term. Also, separate analyses were performed for paternally-transmitted and maternally-transmitted alleles to investigate parent-of-origin effects. Based on maternal and newborn genotypes, the parental origin of alleles was unambiguously inferred for 168 of 221 mother-newborn pairs. To make inferences for an additional 15 pairs, haplotypes composed of rs6026576 and rs2057291 were computed (35).


Estimated proportions of African genomic ancestry ranged from 0.48–0.95 among mothers and 0.61–0.93 among newborns (Table I). There was a negative correlation between birth weight and newborn proportion of African genomic ancestry (Spearman rank correlation, ρ = −0.16, p = 0.02). Nevertheless, there were no instances in which the significance of a SNP (p < 0.05; Table II) was substantially altered due to adjustment for admixture (unadjusted results not shown). The best non-genetic regression model included the proportion of African genomic ancestry (negative association), newborn gender (males larger), gestational age (positive association), and maternal body mass index (MBMI and MBMI29).

Forty-five SNPs were surveyed among fourteen candidate genes. Only two SNPs (Table II) exhibited clearly significant association with birth weight: ENPPI SNP rs7754561 among newborns (dominant model, p = 0.002) and GNAS SNP rs6026576 among mothers (additive and dominant models, P < 0.005). When maternal rs6026576 genotype and a newborn gender x maternal rs6026576 genotype interaction term were included in the same regression analysis, both the direct genotypic effect (p < 0.001) and interaction term (p = 0.02) were significant. Among males, but not females, there was a negative linear relationship between birth weight and the mother’s number of A alleles at rs6026576 (Table III). Analysis of maternally-transmitted alleles revealed a significant (p = 0.015) association with birth weight, which was restricted to male newborns (p = 0.001; Table IV). Paternally-transmitted alleles were not associated with birth weight. Newborn genotype at rs6026576 had a marginally significant (additive, p = 0.04) association with birth weight but a nonsignificant interaction with gender (p>0.05).

Table III
Birth weight in grams according to newborn gender and genotype at rs6026576.
Table IV
Grams birth weight (N) according to parental transmission of alleles at SNP rs6026576.


ENPP1 and leptin

The ectonucleotide pyrophosphatase phosphodiesterase gene (ENPP1) directly inhibits conformational changes in the insulin receptor and affects the insulin sensitivity of cells. We genotyped two of the three SNPs Meyre et al. (36) found to be associated with obesity. Some researchers have replicated the K121Q (rs1044498) association (3739). However, similarly to researchers who could not replicate that association (40,41), we found no relationship between birth weight and rs1044498. SNP rs7754561 associated with obesity in French and Austrian cohorts (36), but not in British, German, Polish or African–American cohorts (38,41). We found rs7754561 to be significantly (p = 0.0016) associated with birth weight, but unlike Meyre et al. the AA homozygotes have the highest birth weight (3 748 g vs. 3 293 g and 3 206 g for AG and GG). However, the association of the A allele with higher birth weight is biologically plausible because that allele is associated with lower production of ENPP1, which is expected to result in increased sensitivity to insulin, uptake of glucose, and promotion of fetal growth. Further, rs7754561 is present only in a long transcript of ENPP1 that is restricted to pancreatic beta cells, adipocytes, and liver, which are highly responsive to insulin and key to maintenance of glucose levels. Nevertheless, this association is tentative because we observed only six AA homozygotes and their birth weights could be biased.

We previously showed that the relationship between leptin rs7799039 genotypes and both birth size and umbilical cord leptin levels are significantly different between smaller male and female newborns (20). In this larger sample of African-Americans, regression on birth weight revealed a suggestive (p = 0.078) gender x genotype interaction whose significance increased among the smaller newborns (p = 0.032 for the smaller 50% of newborns), which is consistent with our earlier results.

Several of the SNPs surveyed have previously been associated with obesity-related traits. Some of these previous associations may have been spurious or due to processes unrelated to fetal growth, in which case our negative results are correct. On the other hand, our study involved 221 mother-newborn pairs and would not detect very small effects (e.g., 18 g shift due to rs7903146 of TCF7L2 [18]) that will only be significant in a much larger cohort.


The GNAS (guanine nucleotide-binding protein, alpha-stimulating) locus encodes four different transcripts distinguished by four different first exons that all splice onto a common set of 12 downstream exons (exons 2–13; Figure 1) and exhibit a complex pattern of imprinting (42). NESP55 (neuroendocrine-specific protein of 55 kDa) is a chromogranin-like protein encoded entirely by its first exon that is unrelated to the other GNAS transcripts and transcribed only from the maternal allele. Another first exon encodes 551 extra amino acids and generates the Gsα isoform XLαs, which is expressed from only the paternal allele. A promoter for an antisense transcript (NESPAS) expressed from only the paternal allele is just upstream of the XLαs promoter. The third of the four alternative first exons of GNAS (termed exon A/B or 1a in human) produces a paternal-specific transcript that does not appear to be translated.

Figure 1
Schematic of the GNAS locus. The approximate locations of the eight SNPs (designated by their dbSNP rs numbers) genotyped in this locus are shown. There are four primary transcripts produced from the sense strand of the GNAS gene. Each is distinguished ...

The first exon of the main transcript of the GNAS locus, Gsα-subunit (Gsα), is closest to the shared exons 2–13 and is a G protein expressed in all tissues that serves primarily to couple cell membrane receptors to the stimulation of adenylyl cyclase and production of cyclic AMP. Inactivating mutations of Gsα result in Albright hereditary osteodystrophy (AHO), characterized by short stature, subcutaneous ossification, brachydactyly and neurobehavioral abnormalities. When inherited via the father, patients develop only AHO, accompanied by a mild increase in BMI. If inherited via the mother, AHO is accompanied by resistance to multiple hormones (gonadotropins and parathyroid, thyroid stimulating, and growth hormone releasing hormones) and a significant increase in BMI (43) and is called pseudohypoparathyroidism type 1a (PHP1a [42]). Because those hormones signal via Gsα, which is paternally imprinted in renal proximal tubules, thyroid, pituitary, and ovary, maternal inheritance of a defective GNAS allele results in greatly reduced Gsα production and activity in those tissues. About 68% of patients with PHP1a exhibit GH deficiency and reduced IGF-I levels (44,45). Disruption of the GNAS locus can also result in severe pre- and post-natal growth retardation (2426). However, Wang et al. (46) found no association between birth weight and GNAS polymorphisms, but their study was limited by their inability to incorporate the possibility of imprinting effects, the lack of paternal and fetal genetic data, and the use of a single SNP (rs7121) in a recombination hotspot to represent the entire GNAS locus.

Maternal genotypes for GNAS SNP rs6026576 exhibited a significant (p=0.0044 additive, p=0.0019 dominant) association with newborn birth weight. We investigated the possibility that the association of rs6026576 with birth weight may differ in male and female newborns in a manner consistent with gender-specific effects of imprinting observed in mice (34). Among males, but not females, we found a positive linear trend (p=0.001) between birth weight and the number of G alleles. To further investigate parent-of-origin effects due to the complex imprinting of the GNAS locus, separate regression analyses (Table IV) were performed for each newborn gender and for the newborn rs6026576 allele of paternal or maternal origin. Alleles of paternal origin showed no association (p>0.2) with birth weight in either male or female newborns. However, among male newborns, alleles of maternal origin exhibited a significant (p=0.001; N=99) association with birth weight.

Our results indicate that maternally-transmitted alleles at rs6026576 of the GNAS gene are significantly associated with the birth weight of male offspring. The location of this SNP (Figure 1) and its association with birth weight only when inherited from the mother suggest that this SNP (or another in linkage disequilibrium) exerts its effect via the maternal-specific transcripts of Gsα in the pituitary and thyroid (4749). Patients with PHP1a, who maternally-inherit a Gsα defect, are heavier as children and adults but not at birth (43). Alleles of rs6026576 are not expected to be inactivating, as in AHO, but might affect Gsα expression levels or splicing. Presently, there are no data on the functional effects of rs6026576 alleles. However, this SNP is in very high linkage disequilibrium (D′=1 in Caucasians and Africans [27]) with rs6123837 (A/G), which has been demonstrated to have a functional effect on binding of upstream stimulatory factor 1 and, as part of a broader haplotype, expression of Gsα (50,51) and activity of adenylyl cyclase. Parent-of-origin effects have not been studied for rs6123837, but it is possible that the effects we observe for rs6026576 are due to linkage disequilibrium with functional polymorphisms in the promoter and/or first intron of Gsα. If so, this suggests that alterations in Gs expression can affect body mass at all stages of development. While rs6026576 is not in a CpG island and is not part of a CpG itself that might become methylated, the highly correlated functional SNP rs6123837 resides within a CpG-rich region and is part of a CpG that may experience allele-specific methylation as part of the partial imprinting of Gsα and exhibit parent-of-origin specific effects. This might explain our finding of an effect due only to maternally-inherited alleles. Unfortunately, it is still unclear why the association is restricted to male newborns, and the possibility cannot be excluded that a larger sample size of both genders would either reveal a similar trend among female newborns or reduce the apparent magnitude of the effect in males. Additionally, we have suggested that our observed effect is due to imprinting of the paternal allele in thyroid and pituitary, but the possibility cannot be excluded that Gsα exhibits some imprinting in the placenta that would indirectly affect fetal growth.


We thank three anonymous reviewers for their helpful suggestions that improved the manuscript. This work was supported by grants to RMA from the National Institute of Child Health and Human Development (HD055462), the Children’s Foundation Research Center of Memphis, the University of Tennessee Health Science Center’s Clinical Translational Science Institute, and the Accredo Foundation. Support was also provided to GS from The Urban Child Institute to support the Conditions Affecting Neurocognitive Development and Learning in Early Childhood (CANDLE) study. Additional support was provided by a grant from the National Center for Research Resources (M01RR00211; University of Tennessee Health Science Center General Clinical Research Center). We gratefully acknowledge the laboratory expertise of Jeanette Peeples and the subject recruitment and sample collection by CANDLE staff.


Declaration of interest: The authors alone are responsible for the content and writing of the paper.

Conflict of Interest statement

The authors declare no conflicts of interests, including patents, business relationships, or financial connections to funding sources. Sources of research funding: US National Institute of Child Health and Human Development (HD055462), Children’s Foundation Research Center of Memphis, University of Tennessee Health Science Center’s Clinical Translational Science Institute, Accredo Foundation, and The Urban Child Institute.


1. McIntire DD, Bloom SL, Casey BM, et al. Birth weight in relation to morbidity and mortality among newborn infants. N Engl J Med. 1999;340:1234–8. [PubMed]
2. O’Keeffe MJ, O’Callaghan M, Williams GM, et al. Learning, cognitive, and attentional problems in adolescents born small for gestational age. Pediatrics. 2003;112:301–7. [PubMed]
3. Jelliffe-Pawlowski LL, Hansen RL. Neurodevelopmental Outcome at 8 Months and 4 Years among Infants Born Full-Term Small-for-Gestational-Age. J Perinatol. 2004;24:505–14. [PubMed]
4. Innes KE, Byers TE, Marshall JA, et al. Association of a woman’s own birth weight with her subsequent risk for pregnancy-induced hypertension. Am J Epidemiol. 2003;158:861–70. [PubMed]
5. Innes KE, Byers TE, Marshall JA, et al. Association of a woman’s own birth weight with subsequent risk for gestational diabetes. JAMA. 2002;287:2534–41. [PubMed]
6. Frontini MG, Srinivasan SR, Xu J, et al. Low birth weight and longitudinal trends of cardiovascular risk factor variables from childhood to adolescence: the Bogalusa heart study. BMC Pediatr. 2004;4:22. [PMC free article] [PubMed]
7. Rich-Edwards JW, Colditz GA, Stampfer MJ, et al. Birth-weight and the risk for type 2 diabetes mellitus in adult women. Ann Intern Med. 1999;130:278–84. [PubMed]
8. Lithell HO, McKeigue PM, Berglund L, et al. Relation of size at birth to non-insulin dependent diabetes and insulin concentrations in men aged 50–60 years. BMJ. 1996;312:406–10. [PMC free article] [PubMed]
9. Lawlor DA, Ronalds G, Clark H, et al. Birth weight is inversely associated with incident coronary heart disease and stroke among individuals born in the 1950s: findings from the Aberdeen Children of the 1950s prospective cohort study. Circulation. 2005;112:1414–8. [PubMed]
10. Huxley R, Neil A, Collins R. Unravelling the fetal origins hypothesis: is there really an inverse association between birthweight and subsequent blood pressure? Lancet. 2002;360:659–65. [PubMed]
11. Jaquet D, Swaminathan S, Alexander GR, et al. Significant paternal contribution to the risk of small for gestational age. BJOG. 2005;112:153–9. [PubMed]
12. Clausson B, Lichtenstein P, Cnattingius S. Genetic influence on birthweight and gestational length determined by studies in offspring of twins. BJOG. 2000;107:375–81. [PubMed]
13. Svensson AC, Pawitan Y, Cnattingius S, et al. Familial aggregation of small-for-gestational-age births: the importance of fetal genetic effects. Am J Obstet Gynecol. 2006;194:475–9. [PubMed]
14. Morison IM, Reeve AE. Insulin-like growth factor 2 and overgrowth: molecular biology and clinical implications. Mol Med Today. 1998;4:110–5. [PubMed]
15. Baessler A, Hasinoff MJ, Fischer M, et al. Genetic linkage and association of the growth hormone secretagogue receptor (ghrelin receptor) gene in human obesity. Diabetes. 2005;54:259–67. [PMC free article] [PubMed]
16. Herbert A, Gerry NP, McQueen MB, et al. A common genetic variant is associated with adult and childhood obesity. Science. 2006;312:279–83. [PubMed]
17. Cauchi S, Meyre D, Choquet H, et al. TCF7L2 rs7903146 variant does not associate with smallness for gestational age in the French population. BMC Med Genet. 2007;8:37. [PMC free article] [PubMed]
18. Freathy RM, Weedon MN, Bennett A, et al. Type 2 diabetes TCF7L2 risk genotypes alter birth weight: a study of 24,053 individuals. Am J Hum Genet. 2007;80:1150–61. [PubMed]
19. Chan TF, Yuan SS, Chen HS, et al. Correlations between umbilical and maternal serum adiponectin levels and neonatal birthweights. Acta Obstet Gynecol Scand. 2004;83:165–9. [PubMed]
20. Adkins RM, Klauser CK, Magann EF, et al. Site -2548 of the leptin gene is associated with gender-specific trends in newborn size and cord leptin levels. Int J Pediatr Obes. 2007;2:130–7. [PubMed]
21. Schiff E, Weiner E, Zalel Y, et al. Endothelin-1,2 levels in umbilical vein serum of intra-uterine growth retarded fetuses as detected by cordocentesis. Acta Obstet Gynecol Scand. 1994;73:21–4. [PubMed]
22. Charalambous M, Smith FM, Bennett WR, et al. Disruption of the imprinted Grb10 gene leads to disproportionate overgrowth by an Igf2-independent mechanism. Proc Natl Acad Sci USA. 2003;100:8292–7. [PubMed]
23. Lefebvre L, Viville S, Barton SC, et al. Abnormal maternal behaviour and growth retardation associated with loss of the imprinted gene Mest. Nat Genet. 1998;20:163–9. [PubMed]
24. Genevieve D, Sanlaville D, Faivre L, et al. Paternal deletion of the GNAS imprinted locus (including Gnasxl) in two girls presenting with severe pre-and post-natal growth retardation and intractable feeding difficulties. Eur J Hum Genet. 2005;13:1033–9. [PubMed]
25. Plagge A, Gordon E, Dean W, et al. The imprinted signaling protein XL alpha s is required for postnatal adaptation to feeding. Nat Genet. 2004;36:818–26. [PubMed]
26. Aldred MA, Aftimos S, Hall C, et al. Constitutional deletion of chromosome 20q in two patients affected with albright hereditary osteodystrophy. Am J Med Genet. 2002;113:167–72. [PubMed]
27. Frazer KA, Ballinger DG, Cox DR, et al. A second generation human haplotype map of over 3.1 million SNPs. Nature. 2007;449:851–61. [PMC free article] [PubMed]
28. Ewens WJ, Spielman RS. The transmission/disequilibrium test: history, subdivision, and admixture. Am J Hum Genet. 1995;57:455–64. [PubMed]
29. Lander ES, Schork NJ. Genetic dissection of complex traits. Science. 1994;265:2037–48. [PubMed]
30. Hoggart CJ, Parra EJ, Shriver MD, et al. Control of confounding of genetic associations in stratified populations. Am J Hum Genet. 2003;72:1492–504. [PubMed]
31. Project IH. A haplotype map of the human genome. Nature. 2005;437:1299–320. [PMC free article] [PubMed]
32. Lettre G, Lange C, Hirschhorn JN. Genetic model testing and statistical power in population-based association studies of quantitative traits. Genet Epidemiol. 2007;31:358–62. [PubMed]
33. Churchill GA, Doerge RW. Empirical threshold values for quantitative trait mapping. Genetics. 1994;138:963–71. [PubMed]
34. Hager R, Cheverud JM, Leamy LJ, et al. Sex dependent imprinting effects on complex traits in mice. BMC Evol Biol. 2008;8:303. [PMC free article] [PubMed]
35. Stephens M, Scheet P. Accounting for decay of linkage disequilibrium in haplotype inference and missing-data imputation. Am J Hum Genet. 2005;76:449–62. [PubMed]
36. Meyre D, Bouatia-Naji N, Tounian A, et al. Variants of ENPP1 are associated with childhood and adult obesity and increase the risk of glucose intolerance and type 2 diabetes. Nat Genet. 2005;37:863–7. [PMC free article] [PubMed]
37. Grarup N, Urhammer SA, Ek J, et al. Studies of the relationship between the ENPP1 K121Q polymorphism and type 2 diabetes, insulin resistance and obesity in 7,333 Danish white subjects. Diabetologia. 2006;49:2097–104. [PubMed]
38. Bottcher Y, Korner A, Reinehr T, et al. ENPP1 variants and haplotypes predispose to early onset obesity and impaired glucose and insulin metabolism in German obese children. J Clin Endocrinol Metab. 2006;91:4948–52. [PubMed]
39. Matsuoka N, Patki A, Tiwari HK, et al. Association of K121Q polymorphism in ENPP1 (PC-1) with BMI in Caucasian and African-American adults. Int J Obes (Lond) 2006;30:233–7. [PubMed]
40. Lyon HN, Florez JC, Bersaglieri T, et al. Common variants in the ENPP1 gene are not reproducibly associated with diabetes or obesity. Diabetes. 2006;55:3180–4. [PubMed]
41. Weedon MN, Shields B, Hitman G, et al. No evidence of association of ENPP1 variants with type 2 diabetes or obesity in a study of 8,089 U.K. Caucasians. Diabetes. 2006;55:3175–9. [PubMed]
42. Weinstein LS, Xie T, Zhang QH, et al. Studies of the regulation and function of the Gs alpha gene Gnas using gene targeting technology. Pharmacol Ther. 2007;115:271–91. [PMC free article] [PubMed]
43. Long DN, McGuire S, Levine MA, et al. Body mass index differences in pseudohypoparathyroidism type 1a versus pseudopseudohypoparathyroidism may implicate paternal imprinting of Galpha(s) in the development of human obesity. J Clin Endocrinol Metab. 2007;92:1073–9. [PubMed]
44. Germain-Lee EL, Groman J, Crane JL, et al. Growth hormone deficiency in pseudohypoparathyroidism type 1a: another manifestation of multihormone resistance. J Clin Endocrinol Metab. 2003;88:4059–69. [PubMed]
45. Mantovani G, Maghnie M, Weber G, et al. Growth hormone-releasing hormone resistance in pseudohypoparathyroidism type ia: new evidence for imprinting of the Gs alpha gene. J Clin Endocrinol Metab. 2003;88:4070–4. [PubMed]
46. Wang L, Wang X, Laird N, et al. Polymorphism in maternal LRP8 gene is associated with fetal growth. Am J Hum Genet. 2006;78:770–7. [PubMed]
47. Chen M, Gavrilova O, Liu J, et al. Alternative Gnas gene products have opposite effects on glucose and lipid metabolism. Proc Natl Acad Sci USA. 2005;102:7386–91. [PubMed]
48. Germain-Lee EL, Schwindinger W, Crane JL, et al. A mouse model of albright hereditary osteodystrophy generated by targeted disruption of exon 1 of the Gnas gene. Endocrinology. 2005;146:4697–709. [PubMed]
49. Williamson CM, Ball ST, Nottingham WT, et al. A cis-acting control region is required exclusively for the tissue-specific imprinting of Gnas. Nat Genet. 2004;36:894–9. [PubMed]
50. Frey UH, Adamzik M, Kottenberg-Assenmacher E, et al. A novel functional haplotype in the human GNAS gene alters G{alpha}s expression, responsiveness to {beta}-adrenoceptor stimulation, and peri-operative cardiac performance. Eur Heart J. 2009;30(11):1402–10. [PubMed]
51. Frey UH, Hauner H, Jockel KH, et al. A novel promoter polymorphism in the human gene GNAS affects binding of transcription factor upstream stimulatory factor 1, Galphas protein expression and body weight regulation. Pharmacogenet Genomics. 2008;18:141–51. [PubMed]