Abstract
Objective
To examine associations in a preterm population between rs9883204 in ADCY5 and rs900400 near LEKR1 and CCNL1 with birth weight. Both markers were associated with birth weight in a term population in a recent genome-wide association (GWA) study by Freathy et al.
Study design
A meta-analysis of mother and infant samples was performed for associations of rs900400 and rs9883204 with birth weight in 393 families from the U.S., 265 families from Argentina and 735 mother-infant pairs from Denmark. Z scores adjusted for infant sex and gestational age were generated for each population separately and regressed on allele counts. Association evidence was combined across sites by inverse-variance weighted meta-analysis.
Results
Each additional C allele of rs900400 (LEKR1/CCNL1) in infants was marginally associated with a 0.069 standard deviation (SD) lower birth weight (95% CI = −0.159 – 0.022, P = 0.068). This result was slightly more pronounced after adjusting for smoking (P = 0.036). There were no significant associations identified with rs9883204 or in maternal samples.
Conclusions
These results indicate the potential importance of this marker on birth weight irrespective of gestational age.
Keywords: Genetic, association, single nucleotide polymorphism
Birth weight is a complex trait, the extremes of which are associated with high rates of perinatal morbidity and mortality.1–3 Low (<2,500 grams) and very low (<1,500 grams) birth weight infants are at an increased risk for developing neonatal complications such as bronchopulmonary dysplasia (BPD), intraventricular hemorrhage (IVH) and necrotizing enterocolitis (NEC).3 In addition, there is evidence that low birth weight is associated with disease in adulthood, including type 2 diabetes, cardiovascular disease and hypertension.4–9 Many factors influence birth weight, the greatest of which is gestational age. Other environmental contributors include maternal age, race/ethnicity, body mass index (BMI), parity, education and smoking.10,11 Twin and family studies demonstrate that genetic factors also play a role in birth weight.12–15 The heritability of birth weight increases with decreasing gestational age and is ~38% at 25 weeks gestation compared with 15% at 42 weeks.16 Therefore, more of the variation in birth weight is due to genetic factors at earlier gestational ages. However, to date, most genetic association studies of birth weight have examined individuals born at term. Identifying genetic associations with birth weight in preterm infants will better inform us on the biology underlying low birth weight and may lead to the development of predictive and preventive treatment for infants at risk for being born low birth weight for their gestational age.
Previous studies found numerous genes associated with birth weight, the majority of which are also associated with type 2 diabetes, including transcription factor 7-like 2 (TCF7L2), peroxisome proliferator-activated receptor-gamma (PPARG) and insulin-like growth factor binding protein-3 (IGFBP3).17–19 However, most of these associations have failed to replicate in independent populations.20,21 Recently, a genome-wide association (GWAS) study of birth weight was performed on 10,623 European term (>37 weeks) gestations from several large pregnancy and birth cohorts.22 This study found two fetal genetic associations with birth weight, one intergenic marker between the leucine, glutamate and lysine rich 1 (LEKR1) and cyclin L1 (CCNL1) genes and the other marker within the adenylate cyclase 5 (ADCY5) gene. These loci contribute to 0.1% and 0.3% of the variation in birth weight, respectively. Both replicated in a similar population of 27,591 individuals. Specifically, the C alleles of rs900400 (LEKR1/CCNL1) and rs9883204 (ADCY5) were associated with lower birth weight (P = 2×10−35 and P = 7×10−15, respectively). We examined these associations with birth weight in a diverse collection of Caucasian preterm (23–36 weeks gestation) populations, including samples from four sites in the United States, two sites in Argentina, and one site in Denmark. We were able to test these associations using meta-analysis as well as a family-based approach.
Methods
The US and Argentina study samples were collected at four sites in the United States (the University of Iowa in Iowa City, IA, Magee-Womens Hospital in Pittsburgh, PA, University of Rochester Medical Center in Rochester, NY, and Wake Forest University in Wake Forest, NC) as well as two centers in Argentina (Instituto de Maternidad y Ginecología Nuestra Señora de las Mercedes in Tucumán and Hospital Provincial de Rosario in Rosario). Signed informed consent (IRBs 199911068, 200411759 and 200506792) for samples collected from Iowa, Wake Forest, Rochester, Pittsburgh and Argentina were obtained from all families for enrollment. DNA was extracted from cord blood or buccal swabs collected from the infants and venous blood, saliva samples, or buccal swabs collected from the parents and other relatives. Demographic information and phenotype data were collected through an interview with the mother and/or medical chart review. This study is a secondary analysis of data, which has been described previously in relation to genetic factors associated with spontaneous preterm birth23,24. Because the underlying genetic etiologies may differ between indicated and spontaneous preterm birth individuals with the following outcomes were excluded from the study: multiple gestations, use of assisted reproductive technology, fetal demise, elective termination of pregnancy, congenital anomalies, uterine structure anomalies, maternal autoimmune disorder, placental abruptions caused by hypertensive or traumatic clinical techniques, placenta previa and delivery occurring as a result of obstetric intervention due to maternal or fetal indications. This information was missing or limited for the samples from Argentina and for 172 of the samples from Iowa. Gestational age was defined by best obstetrical estimate using menstrual dating and obstetrical ultrasound corroboration. A total of 658 families including 393 families from the U.S. (241 from Iowa City, 95 from Wake Forest, 38 from Pittsburgh and 19 from Rochester) and 265 families from Argentina (245 from Tucumán and 20 from Rosario) with complete birth weight and gestational age information were included in the analysis.
The Danish study samples came from a genome-wide association study (GWAS) of prematurity and its complications. In this GWAS, 1,000 mother-baby case pairs (20–36 weeks gestation) and 1,000 mother-baby control pairs (40 weeks gestation) were selected from the Danish National Birth Cohort (DNBC), which is a population-based cohort of 101,042 pregnancies, recruited in the years 1996–2002.25 These samples were selected to study the primary outcome of spontaneous preterm birth; therefore, individuals were only considered for inclusion in this study if there was no evidence of placental abnormalities, preeclampsia/eclampsia, congenital abnormalities, and stillbirth. Gestational age was determined by a consensus algorithm taking up to 6 different gestational age variables (self-reported and from health registers) into account. The study protocol was approved by the Danish Scientific Ethical Committee and the Danish Data Protection Agency. Blood samples from mothers were obtained at recruitment (~week 8 of pregnancy) and infant samples were obtained at birth from umbilical cord blood. Samples were stored in a biobank at Statens Serum Institut (SSI) as buffy coats and/or on filter paper, and DNA was extracted for the samples used in the GWAS. All participating women in the DNBC underwent thorough phenotype characterization based on information from four computer-assisted telephone interviews conducted during pregnancy (two interviews) and after delivery (two interviews). Additional phenotype information was obtained from Danish health registers. Preterm singleton births occurring after spontaneous labor were included for these analyses. A total of 735 mother-infant pairs with genotype data for mother and/or infant were included in the analysis.
Genotyping
DNA samples from Iowa, Pittsburgh, Wake Forest, Rochester and Argentina were genotyped for the single nucleotide polymorphism (SNP) markers ADCY5 rs9883204 and LEKR1/CCNL1 rs900400 using Applied Biosystems (Foster City, CA, USA) Taqman® chemistry under standard conditions. Allele determination was done in the endpoint analysis mode on an Applied Biosystems 7900 HT Sequence Detection System machine with SDS 2.3 software. Genotypes were entered into Progeny (South Bend, IN, USA), a laboratory database, which was used to create datasets for analysis.
GWAS genotyping of the Danish study sample was performed using the Illumina Human 660W-Quadv1_A chip as part of the Gene Environment Association Studies (GENEVA) consortium. Because this array does not include rs900400 and rs9883204, allele dosages for these SNPs were imputed using the MACH software package26,27 and the HapMap CEU sample as the reference panel. Imputation was done separately for mothers and infants; in both cases imputation quality was excellent for both SNPs (R-squared estimates > 0.95).
Statistical Analysis
We chose a similar approach to the GWAS by Freathy et al.22 and generated Z scores for the individual study populations using a uniform model. Within each study population, birth weight was regressed on centered gestational age, centered gestational age squared, and infant sex. These covariates were highly significant for most of the larger sites (Table I; available at www.jpeds.com). Residuals from the regressions were standardized to Z scores for each study population, and the data was cleaned by iteratively removing outliers more than 3 standard deviations (SD) away from the population mean and recalculation of the Z scores. The association between each SNP and birth weight was assessed for each study population using linear regression of birth-weight Z score against C allele count. In a subset of the study populations additional regression analyses adjusting for smoking could be performed. Meta-analysis was performed using the fixed-effect inverse-variance method yielding pooled beta coefficients, standard errors and Z scores.28 One-sided tests were performed based on the negative effect of the C alleles for the two SNPs reported by Freathy et al.22 under the assumption that the direction of effect would be the same for preterm infants as was reported for the term infants. Analyses were performed with R (http://www.r-project.org/). Quantitative disequilibrium tests (QTDT), adjusting for gestational age and sex, were also performed on the parent-infant trios to determine if the transmission of parental alleles to the offspring was associated with birth weight. QTDT is based on the same principles as the transmission disequilibrium test (TDT). QTDT builds upon the methods developed for linkage-disequilibrium mapping and uses variance components to construct tests that utilize information from all available offspring.29 Permutation tests were used to correct for the non-normal distribution of birth weight. These tests are performed by randomly permuting the observed pattern of allelic transmission, defined by the vector wi. Under the hypothesis of no linkage disequilibrium and no segregation, both vectors wi and −wi are equally likely to occur. Therefore, with K families the number of different possible permutations of the data is 2K. Models were adjusted for gestational age, sex, smoking and study site. QTDT analyses were performed in the U.S. and Argentina samples (trios were not available for the Danish site) separately as well as combined. Markers were tested for deviations from Hardy-Weinberg equilibrium (HWE) in each population for maternal and infant samples separately.
Table I.
Infant and maternal models of birth weight regressed on centered gestational age, centered gestational age squared and infant sex.
| Covariate | Estimate | SE | P-value | Adjusted r2 |
|---|---|---|---|---|
| Infant Models | ||||
| Iowa (N=205) | <2×10−16 | 0.88 | ||
| Intercept | 2039 | 30.83 | <2×10−16 | |
| centered gestational age | 31.94 | 1.26 | <2×10−16 | |
| centered gestational age squared | 0.15 | 0.03 | 2.4×10−7 | |
| infant sex | 134.8 | 35.45 | 1.8×10−4 | |
| Pittsburgh (N=34) | <2×10−16 | 0.91 | ||
| Intercept | 2011 | 75.21 | <2×10−16 | |
| centered gestational age | 27.52 | 2.91 | 1.7×10−10 | |
| centered gestational age squared | 0.09 | 0.05 | 0.07 | |
| infant sex | 109.10 | 74.74 | 0.15 | |
| Rochester (N=17) | 1.1×10−7 | 0.91 | ||
| Intercept | 2231.23 | 74.63 | 2.3×10−13 | |
| centered gestational age | 26.89 | 4.48 | 4.4×10−5 | |
| centered gestational age squared | 0.01 | 0.09 | 0.88 | |
| infant sex | −11.81 | 99.13 | 0.91 | |
| Wake Forest (N=91) | <2×10−16 | 0.86 | ||
| Intercept | 2062.57 | 49.11 | <2×10−16 | |
| centered gestational age | 33.02 | 1.84 | <2×10−16 | |
| centered gestational age squared | 0.15 | 0.05 | 5.2×10−3 | |
| infant sex | 177.47 | 55.86 | 2.1×10−3 | |
| Rosario (N=18) | 0.13 | 0.18 | ||
| Intercept | 2038.66 | 155.85 | 3.1×10−9 | |
| centered gestational age | 28.12 | 15.85 | 0.10 | |
| centered gestational age squared | −0.07 | 1.18 | 0.96 | |
| infant sex | 16.24 | 183.58 | 0.93 | |
| Tucumán (N=199) | <2×10−16 | 0.71 | ||
| Intercept | 1811 | 30.83 | <2×10−16 | |
| centered gestational age | 24.94 | 1.51 | <2×10−16 | |
| centered gestational age squared | 0.10 | 0.04 | 0.01 | |
| infant sex | 78.81 | 41.10 | 0.06 | |
| Denmark (N=644) | <2×10−16 | 0.68 | ||
| Intercept | 2165 | 23.88 | <2×10−16 | |
| centered gestational age | 32.96 | 1.00 | <2×10−16 | |
| centered gestational age squared | 0.15 | 0.03 | 5.5×10−6 | |
| infant sex | 76.68 | 25.26 | 2.5×10−3 | |
| Maternal Models | ||||
| Iowa (N=219) | <2×10−16 | 0.89 | ||
| Intercept | 2019 | 30.02 | <2×10−16 | |
| centered gestational age | 32.43 | 1.16 | <2×10−16 | |
| centered gestational age squared | 0.16 | 0.03 | 1.5×10−8 | |
| infant sex | 133.4 | 34.27 | 1.3×10−4 | |
| Pittsburgh (N=34) | <2×10−16 | 0.91 | ||
| Intercept | 1991 | 68.48 | <2×10−16 | |
| centered gestational age | 25.41 | 3.07 | 3.1×10−9 | |
| centered gestational age squared | 0.06 | 0.05 | 0.23 | |
| infant sex | 71.07 | 67.23 | 0.30 | |
| Rochester (N=16) | 1.3×10−5 | 0.84 | ||
| Intercept | 2134.42 | 98.35 | 5.4×10−11 | |
| centered gestational age | 30.33 | 5.35 | 1.1×10−4 | |
| centered gestational age squared | 0.10 | 0.13 | 0.45 | |
| infant sex | 91.17 | 137.99 | 0.52 | |
| Wake Forest (N=91) | <2×10−16 | 0.86 | ||
| Intercept | 2065 | 50.90 | <2×10−16 | |
| centered gestational age | 32.66 | 1.88 | <2×10−16 | |
| centered gestational age squared | 0.14 | 0.05 | 9.0×10−3 | |
| infant sex | 162.0 | 56.73 | 5.4×10−3 | |
| Rosario (N=17) | 0.03 | 0.37 | ||
| Intercept | 1960.29 | 128.37 | 1.1×10−9 | |
| centered gestational age | 30.57 | 11.19 | 0.02 | |
| centered gestational age squared | −0.60 | 0.92 | 0.52 | |
| infant sex | 132.01 | 144.54 | 0.38 | |
| Tucumán (N=224) | <2×10−16 | 0.69 | ||
| Intercept | 1789 | 30.59 | <2×10−16 | |
| centered gestational age | 25.70 | 1.42 | <2×10−16 | |
| centered gestational age squared | 0.13 | 0.04 | 2.9×10−3 | |
| infant sex | 82.52 | 39.54 | 0.04 | |
| Denmark (N=669) | <2×10−16 | 0.74 | ||
| Intercept | 2177 | 23.15 | <2×10−16 | |
| centered gestational age | 32.03 | 0.98 | <2×10−16 | |
| centered gestational age squared | 0.12 | 0.02 | 3.3×10−7 | |
| infant sex | 76.41 | 24.82 | 2.2×10−3 | |
Power calculations
Power to detect association of rs9883204 at alpha = 0.05 in the combined U.S., Denmark, and Argentina sample was approximately 67%, assuming an effect size of −0.09 phenotype Z score per C allele (i.e. the effect observed for rs9883204 in the discovery set reported in Freathy et al.) for the one-sided test under the additive model. An effect size of −0.11 phenotype Z score per C allele is required to achieve approximately 80% power. For rs900400, the power to detect association in the combined U.S., Denmark, and Argentina sample was 72%, assuming an effect size of −0.09 phenotype Z score per C allele (i.e. the effect observed for rs900400 in the discovery set reported in Freathy et al.22). An effect size of −0.10 phenotype Z score per C allele is required to achieve approximately 80% power. These estimates of power were calculated assuming a single test of genetic association, rather than the meta-analysis of seven independent tests as reported herein, and therefore may be anti-conservative. Power calculations were performed using the program Quanto (http://hydra.usc.edu/gxe/).
Results
Table II lists clinical characteristics and genotype distributions for the different sites. There were substantial differences in birth weight and gestational ages due to the different designs of the studies. The rs900400 marker deviated from HWE in both sites from Argentina (Rosario, P = 0.03; Tucumán, P = 3.7×10−7) but in none of the sites from the US or Denmark. These deviations were not observed when examining mother samples or rs9883204 in infants or mothers. Mendelian errors were checked and genotypes were excluded if the error could not be resolved. The deviation from HWE in the Argentina samples was characterized by a deficiency of heterozygotes, most likely a result of poor genotyping quality due to a combination of sample quality and technical efficiency of this marker in the Argentina samples. Therefore, the samples from both Argentina sites were excluded in the analyses of rs900400 in infants.
Table II.
Clinical characteristics and infant genotype frequencies by collection site
| Variable | Tucumán (n=199) | Rosario (n=18) | Iowa City (n=205) | Pittsburgh (n=34) | Rochester (n=17) | Wake Forest (n=91) | Denmark (n=644) |
|---|---|---|---|---|---|---|---|
| Birth weight (grams) | 1,827 (528) | 2,268 (408) | 1,710 (724) | 1,493 (636) | 1,865(658) | 2,270 (721) | 2,487 (566) |
| Gestational age (weeks) | 33 (3) | 35 (1) | 30 (4) | 29 (4) | 31 (3) | 33 (3) | 34 (2) |
| Infant sex (male) | 99 (49.8%) | 7 (38.9%) | 125 (61.0%) | 25 (73.5%) | 9 (52.9%) | 46 (50.6%) | 344 (53.4%) |
| Smoked during | |||||||
| pregnancy | 20 (20.0%) | NA | 52 (29.7%) | 8 (23.5%) | 3 (17.7%) | 9 (10.5%) | 183 (29.0%) |
| rs900400 | |||||||
| CC | 51 (35.7%) | 3 (25.0%) | 33 (16.8%) | 6 (20.0%) | 1 (6.7%) | 12 (14.3%) | 110 (17.1%) |
| CT | 45 (31.5%) | 2 (16.7%) | 90 (45.9%) | 9 (30.0%) | 5 (33.3%) | 38 (45.2%) | 319 (49.5%) |
| TT | 47 (32.9%) | 7 (58.3%) | 73 (37.2%) | 15 (50.0%) | 9 (60.0%) | 34 (40.5%) | 215 (33.4%) |
| rs9883204 | |||||||
| CC | 87 (49.4%) | 10 (58.8%) | 115 (57.2%) | 25 (75.8%) | 8 (53.3%) | 46 (55.4%) | 314 (48.8%) |
| CT | 67 (38.1%) | 6 (35.3%) | 70 (35.4%) | 7 (21.2%) | 6 (40.0%) | 31 (37.4%) | 284 (44.1%) |
| TT | 22 (12.5%) | 1 (5.9%) | 15 (7.4%) | 1 (3.0%) | 1 (6.7%) | 6 (7.2%) | 46 (7.1%) |
Birth weight and gestational age are represented as means and standard deviations, all other variables are presented in counts and percents.
Meta-analysis of linear regression results for 5 collection sites (excluding Rosario and Tucumán) revealed that each additional C allele of rs900400 (LEKR1/CCNL1) in infants was associated with a 0.069 SD lower birth weight (P = 0.068, one-sided) (Figure 1 and Table III; available at www.jpeds.com). We found no evidence of heterogeneity between the 5 study sites examined (I2 = 0%, 95% CI = 0 – 53%, P > 0.5).30 The observed effect size corresponds to approximately 17 grams lower birth weight per C allele (median collection site SD = 244 g). This result was very similar when using an alternative meta-analysis Z score based on sample size (P = 0.071).28 In the subgroup analysis adjusting for smoking, the effect was slightly more pronounced (Z = −0.085, 95% CI = −0.177 – 0.007, P = 0.036, one-sided). Family-based analyses adjusting for sex, gestational age, smoking and study population validated these results in the combined samples in that the C allele of rs900400 was also associated with lower birth weight (P = 0.024). This result also remained significant when removing the Argentina population (P = 0.040). Meta analysis of the mothers’ data gave a pooled estimate of 0.039-s.d. lower birth per C allele, but this was not statistically significant (P = 0.17) (Figure 2, A, and Table IV; available at www.jpeds.com). No significant association between infant (pooled Z = 0.001, 95% CI = −0.090 – 0.092) or maternal (pooled Z = 0.066, 95% CI = −0.019 – 0.150) genotype at rs9883204 (ADCY5) and birth weight was observed (Figures 1, B, and 2, B).
Figure 1.
Forest plots of the association between birth weight and infant genotype at rs900400 (a) and rs9883204 (b). Argentina samples were excluded from analysis of infant rs900400 and birth weight due to deviations from Hardy-Weinberg equilibrium.
Table III.
Associations between birth weight and infant genotype by collection site.
| Site | rs900400 | rs9883204 | ||||||
|---|---|---|---|---|---|---|---|---|
| Beta | SE | N | p-value | Beta | SE | N | p-value | |
| Iowa City | −0.11 | 0.10 | 196 | 0.13 | −0.14 | 0.11 | 200 | 0.10 |
| Pittsburgh | −0.10 | 0.22 | 30 | 0.33 | 0.78 | 0.32 | 33 | 0.99 |
| Rochester | −0.28 | 0.35 | 15 | 0.21 | −0.11 | 0.41 | 15 | 0.39 |
| Wake Forest | −0.21 | 0.16 | 84 | 0.10 | 0.14 | 0.18 | 83 | 0.78 |
| Rosario* | NA | NA | NA | NA | 0.06 | 0.40 | 17 | 0.56 |
| Tucumán* | NA | NA | NA | NA | 0.10 | 0.11 | 176 | 0.81 |
| Denmark | −0.03 | 0.06 | 644 | 0.31 | −0.03 | 0.06 | 644 | 0.31 |
SE = standard error; N = number of observations per site; p-value is one-sided
Analysis of rs900400 was excluded from the Argentina sites due to a strong deviation from HWE.
Figure 2.
Table IV.
Associations between birth weight and maternal genotype by collection site.
| Site | rs900400 | rs9883204 | ||||||
|---|---|---|---|---|---|---|---|---|
| Beta | SE | N | p-value | Beta | SE | N | p-value | |
| Iowa City | 0.09 | 0.10 | 214 | 0.81 | −0.04 | 0.11 | 215 | 0.37 |
| Pittsburgh | 0.17 | 0.31 | 32 | 0.70 | 0.17 | 0.27 | 33 | 0.74 |
| Rochester | −0.39 | 0.27 | 15 | 0.08 | 0.27 | 0.32 | 16 | 0.80 |
| Wake Forest | −0.19 | 0.16 | 89 | 0.12 | 0.19 | 0.19 | 89 | 0.84 |
| Rosario | −0.59 | 0.45 | 14 | 0.10 | −0.06 | 0.36 | 17 | 0.43 |
| Tucumán | −0.06 | 0.10 | 176 | 0.28 | 0.05 | 0.09 | 209 | 0.71 |
| Denmark | −0.04 | 0.06 | 669 | 0.24 | 0.08 | 0.06 | 669 | 0.91 |
SE = standard error; N = number of observations per site; p-value is one-sided
Discussion
The most significant signal in term infants in the GWAS by Freathy et al.22 was for rs900400, located between LEKR1 and CCNL1 (P = 2×10−35). We found a marginally significant (p=0.068) association between rs900400 and birth weight in a meta-analysis of five independent Caucasian preterm (23–26 weeks gestation) infant populations from two countries. Individually, there was no significant association observed for any one site; however, performing a meta-analysis afforded adequate power to detect an effect, which was one of the strengths of our study. We did not observe any associations with maternal genotype alone, which was also consistent with the findings from the GWAS study. In addition, the C allele of rs900400 was associated (p=0.024) with decreased birth weight when examining the transmission of parental alleles to offspring, confirming that this effect lies with the fetal, not maternal genotype. This is consistent with the literature that paternal birth weight, height and weight are associated with the birth weight of the offspring; suggesting that paternal factors transmitted to the fetus are associated with birth weight.31,32
The rs900400 marker is positioned between LEKR1 (~35 kb) and CCNL1 (~67 kb), and occurs 35 bp from an 8 bp intergenic conserved element of unknown function and 800 bp downstream of a predicted transcript that is otherwise uncharacterized but is within the most strongly conserved haplotype block. There is no known plausible biological role of LEKR1 (a gene of unknown function) or CCNL1 (a regulator of RNA polymerase 2 transcription) on birth weight. Furthermore, it is unknown whether rs900400 affects these or other nearby genes. However, a recent study found that rs900400 was associated with smaller fetal head circumference and femur length in the second trimester of pregnancy and smaller head circumference, abdominal circumference, femur length and estimated fetal weight in the third trimester33. Further studies including re-sequencing and functional studies of these genes and the putative transcript and conserved regions are needed to determine the causal variants responsible for the effects observed with birth weight.
The second most significant signal in the GWAS by Freathy et al.22 was rs9883204 in ADCY5; which was also recently reported to be associated with fetal growth measures (i.e., abdominal circumference, femur length, and estimated fetal weight) in the third trimester of pregnancy and at birth.33 However, this signal did not replicate in our preterm population for infant or maternal samples. One possible explanation is that this signal only mediates birth weight in term infants and does not affect the variation in birth weight in preterm infants. ADCY5 is a member of a family of enzymes that synthesizes cyclic adenosine monophosphate (cAMP), and is associated with type II diabetes, fasting glucose and beta-cell function, all of which implicate its involvement in insulin secretion34. Thus variants in this gene may result in reduced insulin secretion which may impact fetal growth. Typically, premature infants have immature insulin regulation compared with term infants; therefore, it is possible that the impact of ADCY5 on insulin secretion may operate through a different etiologic mechanism in term infants where insulin regulation is not already altered. However, a more plausible explanation for the lack of replication is the lower power to detect small effect sizes for this SNP due to our comparably small sample size, a limitation of our study.
This study suggests rs900400, previously identified to associate with birth weight in term infants, is also associated with birth weight in preterm infants. Although the causal etiology and mechanisms of the effect remain unknown, this study has demonstrated replication of this robust association between fetal genotype and birth weight in a preterm population. It is important to study SNPs associated with low birth weight in a sample of preterm births, as low birth weight is even more likely to lead to adverse outcomes in infants that already face a host of complications due to early gestational age. There is now evidence that this signal may play a significant role in affecting the variation in birth weight irrespective of gestational age. Therefore, it is important that continuing research on this association focuses on discovering the mechanisms and biological pathways impacting fetal growth.
Acknowledgments
Supported by the March of Dimes (grants 1-FY05-126 and 6-FY08-260) and the NIH (grants R01 HD-52953 and R01 HD-57192). K.R.’s postdoctoral fellowship was supported by a NIH/NRSA T-32 training grant (5T32 HL 007638-24). Support for the Danish National Birth Cohort was obtained from the Danish National Research Foundation, the Danish Pharmacists’ Fund, the Egmont Foundation, the March of Dimes Birth Defects Foundation, the Augustinus Foundation, and the Health Fund of the Danish Health Insurance Societies. The generation of GWAS genotype data for the DNBC samples was carried out within the GENEVA consortium with funding provided through the NIH Genes, Environment, and Health Initiative (GEI) (U01HG004423). Assistance with phenotype harmonization, genotype cleaning, and general study coordination was provided by the GENEVA Coordinating Center (U01HG004446). Genotyping was performed at Johns Hopkins University Center for Inherited Disease Research, with support from the NIH GEI (U01HG004438).
We would like to express our thanks to all the participating families at our study sites in the United States and Argentina. We would also like to express our gratitude to the coordinating medical and research staff at the University of Iowa Hospitals and Clinics in Iowa City, IA, Magee-Womens Research Institute in Pittsburgh, PA, Wake Forest University Baptist Medical Center in Winston-Salem, NC, Strong Children’s Research Center in Rochester, NY, Centro de Educación Médica E Investigaciones Clínicas in Buenos Aires, Argentina and the Instituto de Maternidad y Ginecología Nuestra Señora de las Mercedes in San Miguel de Tucumán, Argentina. Special thanks to research coordinators Laura Knosp, Kristi Lanier, Lauren Smith and Karen Debes (funded by NIH grants R01 HD-52953 and R01 HD-57192 and March of Dimes grants 1-FY05-126 and 6-FY08-260).
Abbreviations
- ADCY5
Adenylate cyclase 5
- CCNL1
Cyclin L1
- GWAS
Genome wide association study
- HWE
Hardy Weinberg equilibrium
- LEKR1
Leucine, glutamate and lysine rich 1
- SD
Standard deviation
Footnotes
Funding information available at www.jpeds.com (Appendix). The authors declare no conflicts of interest.
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