A genetic association study of maternal and fetal candidate genes that predispose to preterm prelabor rupture of membranes (PROM)

Abstract

Objective

To determine whether maternal/fetal SNPs in candidate genes are associated with preterm prelabor rupture of membranes (pPROM).

Study Design

A case-control study was conducted in patients with pPROM (225 mothers and 155 fetuses) and 599 mothers and 628 fetuses with a normal pregnancy; 190 candidate genes and 775 SNPs were studied. Single locus/haplotype association analyses were performed; FDR was used to correct for multiple testing (q*=0.15)].

Results

1) A SNP in TIMP2 in mothers was significantly associated with pPROM(OR=2.12 95% CI [1.47-3.07], p = 0.000068), and this association remained significant after correction for multiple comparisons; 2) Haplotypes for COL4A3 in the mother were associated with pPROM (global p = 0.003); 3) Multilocus analysis identified a three locus model, which included maternal SNPs in COL1A2, DEFA5, and EDN1.

Conclusion

DNA variants in a maternal gene involved in extracellular matrix metabolism doubled the risk of pPROM.

INTRODUCTION

Preterm prelabor rupture of membranes (pPROM) complicates approximately 3 to 4.5% of all pregnancies in the US and it is responsible for about 30% of preterm births.^1-15 A genetic predisposition to preterm birth has been suggested^16-18 based upon: 1) demonstration of familial aggregation;^19-26 2) substantiation with segregation studies;²⁷ 3) identification of disease-susceptibility genes;^28-30 and 4) racial disparity in rates of pPROM and preterm birth.^16;31-54

Genetic factors are known to predispose to pPROM. First, patients with Ehlers-Danlos Syndrome, a rare Mendelian connective tissue disorder with mutations either in collagen genes or genes involved in collagen processing, have a substantial genetic predisposition to preterm delivery preceded by spontaneous rupture of the membranes.^55;56 Although Ehlers-Danlos Syndrome is a Mendelian disorder and pPROM is not, the shared aspects of the phenotypes are indicative of related and perhaps common etiology, i.e., genetic predisposition. Further supporting a genetic role with fetal effect is that pregnant women without Ehlers-Danlos Syndrome but with an Ehlers-Danlos Syndrome fetus, present with pPROM more than twice as often (50%) than in affected women (20%) with or without an affected fetus.⁵⁶ Similarly, patients with Marfan syndrome, a disorder involving mutations of the fibrillin-1 gene leading to abnormalities of collagen structure and hyaluronic acid synthesis, have a 6% rate of pPROM,⁵⁷ which is higher than the general population, supporting the view that a genetic factor that predisposes to Marfan plays a role in the risk for pPROM.⁵⁸ These two syndromes only explain a small fraction of pPROM risk, but their existence demonstrates the principle of a genetic role in pPROM.

Polymorphisms in several genes have been studied in pPROM.^59-100 Some genes include matrix metalloproteinase genes (MMP1, MMP8 and MMP9) and SERPINH1 [heat-shock protein 47 (Hsp47)], all of which affect extracellular matrix protein degradation in fetal membranes. Variants in these genes could be associated with membrane weakening and rupture, although a direct functional link has yet to be established. Functional studies have also demonstrated a role for some of the variants in the expression and activity of these molecules involved in extracellular matrix metabolism.^{61-63;76;86;91;92;94}

The objective of this genetic association study was to determine if either maternal or fetal carriage of DNA variants predispose to pPROM. Seven hundred seventy five single nucleotide polymorphisms (SNPs) from 190 candidate genes that have been implicated in the mechanisms of disease responsible for spontaneous preterm labor, pPROM, small-for-gestational age (SGA), and preeclampsia, were analyzed. The study was conducted in a Hispanic population at a single site from Chile and with extreme care to phenotypic characterization.

MATERIALS AND METHODS

Study Design

This was a case-control study that included patients with pPROM and their neonates (mothers: 225 and fetuses: 155) who delivered preterm (21-36 weeks of gestation) as well as controls and their neonates (mothers: 599 and fetuses: 628). A patient was considered to have preterm PROM if she met the following criteria: 1) gestational age below 37 weeks; 2) a history of leaking of fluid reported by the mother; 3) sterile speculum examination demonstrating pooling of fluid and a nitrazine test which was positive; 4) a ferning test was considered confirmatory but not necessary for the diagnosis of preterm PROM; and 5) the term “prelabor” rupture of membranes was used in our manuscript to indicate that the leaking of fluid was required to have occurred at least one hour prior to the onset of regular contractions. These criteria have been used by other investigators.⁵ The control group included women who delivered a neonate of appropriate weight for gestational age¹⁰¹ at term (37–42 weeks of gestation) without complications of pregnancy including preterm labor with term delivery, preeclampsia, eclampsia, HELLP syndrome, term PROM, SGA, large-for-gestational age neonates, fetal demise, placental abruption, placenta previa, or chorioamnionitis. Clinical chorioamnionitis was diagnosed according to the criteria proposed by Gibbs et al.¹⁰² including maternal temperature of ≥37.8°C and two or more of the following criteria: uterine tenderness, malodorous vaginal discharge, maternal leukocytosis (≥15000 cells/mm³), maternal tachycardia (>100 beats/min) and fetal tachycardia (>160 beats/min).

Patients of Hispanic origin were recruited at the Sotero del Rio Hospital, in Puente Alto, Chile. All eligible mothers were enrolled in a research protocol, which requested permission to collect DNA from the mother and her neonate for research purposes. The exclusion criteria, beside those explained above for controls, included: 1) known major fetal chromosomal and/or structural anomalies; 2) multiple pregnancy; 3) serious medical illness (chronic renal failure, congestive heart failure, connective tissue disorders, etc.); 4) refusal to provide written informed consent; and 5) a clinical emergency, which prevented counseling of the patient about participation in the study, such as fetal distress or maternal hemorrhage. A blood sample was obtained from the mother at the time of enrollment in the protocol, and from the umbilical cord (fetal blood) after delivery. Demographic and clinical characteristics of the mothers were obtained from a data collection form administered by trained medical and paramedical personnel. The collection of samples and their utilization for research purposes was approved by the Institutional Review Boards of the Sotero del Rio Hospital, Santiago, Chile (an affiliate of the Pontificia Catholic University of Santiago, Chile), and the Eunice Kennedy Shriver National Institute of Child Health and Human Development, NIH, DHHS.

Genotyping

Candidate genes were selected for analysis based on biological plausibility for a role in pPROM and other pregnancy complications including spontaneous preterm labor with intact membranes, SGA, and preeclampsia. Genes involved in processes such as the control of the immune response (pattern recognition receptors, cytokines, chemokines and their respective receptors), uteroplacental ischemia, or angiogenesis were considered appropriate candidates for this study. A complete list of the 190 genes and all SNPs genotyped are included in the supplemental materials (Supplemental Table 1).

SNP discovery within the candidate genes was performed by DNA sequencing at Genaissance Pharmaceuticals, Inc. (New Haven, CT, USA) using its Index Repository that includes a total of 93 subjects with Native American, Hispanic/Latino, European, Asian, and African-American ancestry.¹⁰³ The protocol for this has been previously described.³⁰ SNPs selected for genotyping were intended to capture at least 90% of the haplotypic diversity of each gene covering variation in the coding regions,¹⁰⁴ 100 bases at each end of the introns, 1000 bases upstream of the start codon, and 100 bases downstream of the stop codon.

Template DNA for genotyping was obtained by whole-genome amplification¹⁰⁵ of genomic DNA¹⁰⁶ isolated from blood using an automated DNA isolation protocol (BioRobot 9604, Qiagen, Valencia, CA, USA). Genotyping was carried out using the MassARRAY TM System (Sequenom, Inc., San Diego, CA, USA) at the high-throughput genotyping facility at Genaissance. Each genotyping assay involved PCR amplification from template DNA in a target region defined by specific primers for the respective polymorphic sites, purification of the amplicon, annealing of the indicated extension primer to one strand of the amplicon adjacent to the polymorphic site, extending the primer by one nucleotide using the MassEXTEND TM reaction (Sequenom, Inc., San Diego, CA, USA), and detection of the allele-specific extension product by mass spectrometry.¹⁰⁷

Quality Control

SNPs were verified for Mendelian consistency and genotyping efficiency of both SNPs and samples as described elsewhere.³⁰ Briefly, we considered the number of Mendelian inconsistencies between mother and fetus to identify potential relationship errors (e.g. sample mix-ups or mislabeling). In the case of multiple inconsistencies in a given pair, the pair was excluded from further analysis (10 pairs in controls and 5 pairs in cases). Tests for deviations from HWE were performed for mothers and fetuses separately and again separately for diagnostic subgroups. Because it is currently unclear how to unequivocally distinguish between deviations from HWE due to genotyping error, and deviations from HWE due to biological causes, such as location at or near a disease susceptibility locus, we noted SNPs that deviate from HWE, but we did not remove them from the analysis.^108-113 If necessary, we could follow-up these observations with additional testing. Therefore, in the case of deviations from HWE we tagged the SNPs but proceeded with the analyses.

Finally, we tested for population stratification in cases and controls using STRUCTURE,¹¹⁴ which indicated that case and control Chilean samples both cluster with HapMap European samples (data not shown).

Statistical analysis

Continuous demographic and clinical characteristics of cases and controls [gestational age, birth weight, maternal age, and body mass index (BMI)] were tested for normality using Shapiro-Wilks test. All measurements deviated significantly from normality; therefore, Mann-Whitney two-sample rank sum tests were used for case-control comparisons. χ² tests were used to test for differences in parity, Apgar scores at 1 and 5 minutes, smoking, and differences in fetal gender between cases and controls. Stata 10.0 statistical software (StataCorp, College Station, TX, USA) was used for all analyses.

Single locus tests of association

Statistical tests for single locus association and for deviations from HWE were calculated using PLINK software.¹¹⁵ Statistical significance for deviations from HWE in cases and controls was determined using Fisher’s exact test. Single locus tests of association were performed with logistic regression using an additive genotypic model where the minor allele was coded as the risk allele. Standard summary statistics, odds ratios (OR) and confidence intervals (CI) were reported for these tests of association. Prior to performing single locus and haplotype analyses, rare SNPs in our data set (allele frequency less than 0.01) were removed (21 SNPs in maternal samples and 44 in fetal samples) as were redundant SNPs (those in strong linkage disequilibrium (LD). LD based SNP pruning was performed using PLINK software, with a cutoff of r² = 0.8. Fifty-two maternal SNPs and 59 fetal SNPs were removed because they were in LD with other SNPs in the data set. Of the 775 SNPs that passed quality control, we analyzed 702 maternal and 665 fetal SNPs. We also excluded a small number of X chromosome SNPs (seven total) for fetal data, as neonates included in the study were both male and female, and power is greatly reduced in the male and female samples analyzed separately. These criteria accounted for the difference in the number of SNPs tested in mothers and fetuses.

Multiple testing corrections

A false discovery rate (FDR) correction was performed to adjust for multiple comparisons using a q* of 0.15 in single locus tests of association in maternal and fetal analyses separately.¹¹⁶ The q* indicates the expected proportion of results that are identified as interesting that are actually false. This is in contrast to α (typically set to 0.05), which indicates the probability of obtaining even one false positive result among all tests for which the null hypothesis is rejected. FDR is used to measure global error, that is, the expected number of false rejections of the null hypothesis among the total number of rejections. The critical significance level was calculated by ranking the results by p values and then multiplying this rank by q* divided by the total number of tests using the step-up approach of Benjamini and Hochberg.¹¹⁷ The threshold q* = 0.15 is deliberately generous, for the purposes of discovery, in which false acceptance of the null is more problematic than false rejection.

Haplotype tests of association

Haplotype analyses were performed on genes with at least one significantly associated SNP (p < 0.01) and at least two SNPs in the same gene. Haplotype frequencies, as well as haplotype-based association analyses for pPROM with two- and three-marker sliding windows, were calculated using PLINK software. Only haplotypes that had a frequency of ≥ 0.05 were analyzed, and only SNPs that had less than 5% missing data were used. The strongest associated haplotype windows are reported and those that demonstrated marginal significance with an omnibus test (p ≤ 0.05) were analyzed for haplotype-specific effects. We present the calculation of OR for each haplotype (using the most common haplotype as referent), as well as determination of case and control haplotype frequencies. Standard summary statistics for pairwise LD were calculated using Haploview.^118;119 Haplotype blocks were assigned using the confidence interval algorithm created by Gabriel et al.¹²⁰

Histologic chorioamnionitis analysis

A systematic histologic examination of all placentas available was performed based on diagnostic criteria previously described.¹²¹ All statistically significant single locus and haplotype associations were further analyzed for allele and haplotype differences between patients with pPROM with histologic chorioamnionitis alone or with funisitis (n=85) and term controls (delivered >37 weeks without histologic chorioamnionitis or funisitis, n=488). The purpose of these analyses was to further evaluate whether histologic chorioamnionitis was driving the observed associations.

Multi-locus analysis

Exploratory multi-locus analyses were performed using Multifactor Dimensionality Reduction (MDR) to identify interactions among maternal, fetal, and maternal/fetal SNPs. MDR has been previously described by Ritchie et al.¹²² and is available as open source software at www.epistasis.org. Briefly, MDR is a non-parametric (does not assume any statistical model) and model free (no assumption mode of genetic inheritance) unique tool for identifying gene-gene interactions. MDR collapses all of the genetic data into two categories (high and low risk) by comparing all single locus and all multi-locus combinations, and then categorizing each genotype into either high-risk or low-risk on the basis of the ratio of cases to controls that have that genotype. MDR ultimately selects one genetic model, either single or multi-locus, that most successfully predicts phenotype or disease status. Analyses were performed: 1) separately for maternal and fetal data (tag SNPs only); and 2) combined for available maternal and fetal paired DNA samples. In our case, we analyzed 672 fetal and 702 maternal SNPs for a total of 1374 in the combined analysis. The different number in maternal and fetal samples was due to different QC results for the two, and the fact that we did not want to remove possible interactions among genes in mothers and fetuses. Data were analyzed for two- and three-way interactions with 10-fold cross-validation and average balanced accuracy as the metrics for evaluating a model.¹²³ Several filtering steps and parameters were explored and are described on Supplemental Table 2. The MDR algorithm was implemented with the full array of tag SNPs as well as after filtering, using the Tuned ReliefF (TuRF) approach as described in detail by Moore and White.¹²⁴ TuRF is a modification of ReliefF. Briefly, ReliefF is a method that estimates the quality of attributes (e.g. SNPs) through a nearest neighbor algorithm that selects neighbors from the same and different classes based on the values of the SNPs (in this case genotypes).¹²⁵ TuRF is a modification of ReliefF method that systematically removes SNPs that poorly differentiate cases and controls.¹²⁴ The motivation behind this algorithm is that the ReliefF estimates of the true associating SNPs will improve as the non-associating SNPs are removed from the dataset. In addition, SNPs were filtered based on results of the single SNP analyses and only SNPs that had a marginal p value of ≤ 0.1 were included, or only those with a p value < 0.05 were analyzed separately. Permutation testing with 1,000 permutations was used to determine statistical significance of all MDR models, addressing potential multiple testing issues.

MDR as described above is ideal for a balanced data set where the number of cases and controls are the same or close to the same. However, computational methods have been developed since the initial development of MDR to test for prediction accuracies in an imbalanced data set, such as ours.¹²³ The method, termed balanced accuracy, corrects for imbalanced data by taking an average of the sensitivity and specificity and is defined as the arithmetic mean of sensitivity and specificity. We tested for balanced accuracy in this manuscript.

Bioinformatics Tools

The SNPper (http://snpper.chip.org) database using dbSNP Build 125 was used to determine marker positions (bp), marker function, and identify amino acid changes.

Pathway analysis

To examine whether the SNPs found to be putatively associated with pPROM mapped to different biological networks and disease functions, an exploratory analysis was performed using Ingenuity Pathway Analyses (IPA) (Ingenuity Systems, Inc., Redwood City, CA, USA).^126-129 The genes with variants that were associated with pPROM (p<0.05) were entered into IPA analysis and were termed “focus genes.” The IPA measured associations of these molecules with other molecules, their network interactions, and biological functions stored in its knowledge base. The knowledge base is scientist-curated and encompasses relationships between proteins, genes, cells, tissues, xenobiotics, and diseases. Our focus genes served as seeds for the IPA algorithm, which models functional networks by identifying interconnected molecules, including molecules not among the focus genes from the IPA knowledge base. The software illustrates the networks graphically, and calculates a score for each network, which represents the approximate “fit” between the eligible focus molecules and each network. The network score is based on the hypergeometric distribution and is reported as the -log (Fisher’s exact test result). The IPA software was used to calculate the most significant biological processes associated with each network modeled by IPA. The top functions for a network were ascertained in IPA using the right-tailed Fisher’s exact test for over-representation of network molecules in a given process.

RESULTS

Table 1 displays the clinical and demographic characteristics of the study population. Women with pPROM had a lower median gestational age at delivery, a lower birth weight, and different distributions of 1^st minute Apgar score, 5^th minute Apgar score and BMI. These differences were expected (except BMI) by the design of the study. The cases had more male than female newborns. Analyses were adjusted for potential confounders (BMI and fetal gender) in all single locus tests of association.

Table 1.

Demographic and clinical characteristics of the study population

Variable	Cases (n = 225*)		Controls (n = 599*)		p-value
	Median (25th-75th)	Mean (SD)	Median (25th-75th)	Mean (SD)
Parity (number of previous pregnancies)	1 [0-2]	1.39 (1)	1 [0-1]	0.91 (1)	<0.0001
Maternal age (years)	27 [21-34]	28 (8)	24 [20-30]	25 (6)	<0.0001
BMI	24 [22-27]	25 (5)	24 [22-26]	24 (4)	0.018
Smoking	15%		14%		0.910
Clinical chorioamnionitis	11%		0%		-
Gestational age at delivery (weeks)	32 [28-34]	31 (4)	40 [39-41]	40 (1)	<0.0001
Birth weight (grams)	1730 [1200-2200]	1676 (642)	3440 [3230-3650]	3449 (287)	<0.0001
Fetal gender (% male)	61%		51%		0.015
1st Minute Apgar score	8 [4-9]	6 (3)	9 [9-9]	8 (1)	<0.0001
5th Minute Apgar score	9 [7-9]	7 (3)	9 [9-9]	9 (0.3)	<0.0001

BMI: body mass index; SD: standard deviation

^*Maternal samples: 225 cases and 599 controls; fetal samples: 155 cases and 628 controls

Single Locus Tests of Association

Summary information for the SNPs with the most significant associations (p < 0.01) with pPROM in maternal and fetal DNA is provided on Table 2 (Table 3 for unadjusted analyses). There was one significant deviation from HWE in maternal controls at Prostaglandin E receptor 1, subtype EP1 (PTGER1) SNP rs3745459 (p = 9 × 10⁻⁵) that also significantly deviated from HWE in cases (p = 4 × 10⁻⁵). There were also significant deviations from HWE in controls in the corticotropin-releasing hormone receptor-1 gene (CRHR1), SNP rs28364026 (in fetal samples p = 3 × 10⁻⁸; in maternal samples p = 4 × 10⁻⁶), but not in cases. Therefore, although these two SNPs were analyzed and were found to associate with pPROM, these results should be interpreted with caution.

Table 2.

Gene summary information strongest associations (p < 0.01)

Population	Gene Name	Gene Code	rs#	Chromosome	Position (bp)	Function
Maternal	Tissue inhibitor of metalloproteinase 2	TIMP2	rs2277698	17	74378612	Coding Exon (S101S)
	Angiogenin, ribonuclease, RNase family 5	ANG	rs11701	14	20231893	Intron
	Toll-like receptor 1	TLR1	rs3923647	4	38475934	Coding Exon (H305L)
	Nitric oxide synthase 3 (endothelial cell)	NOS3	rs3730305	7	1.5E+08	Intron
	Alpha 3 type IV collagen isoform 5 precursor	COL4A3	rs1882435	2	2.28E+08	Intron
	Prostaglandin E receptor 1, subtype EP1	PTGER1	rs3745459	19	14445317	Coding Exon (A272A)
Fetal	Chemokine (C-C motif) receptor 2	CCR2	rs3749461	3	46370317	Promoter
	Matrix metalloproteinase 19 isoform rasi-1	MMP19	rs1056784	12	54519580	Coding Exon (P245S)
	Corticotropin releasing hormone receptor 1	CRHR1	rs28364026	17	41268075	Promoter
	Collagen, type IV, alpha 3	COL4A3	rs1882435	2	2.28E+08	Intron
	CD55 molecule, decay accelerating factor for complement	CD55/DAF	rs10746462	1	2.06E+08	Intron
	Defensin, beta 1 preproprotein	DEFB1	rs5743418	8	6722970	Promoter
	Lipase C precursor	LIPC	rs6080	15	56625225	Intron
	Insulin-like growth factor 1 receptor	IGF1R	rs3743262	15	97282996	Coding Exon (T766T)
	Plasminogen activator, tissue type isoform 3	PLAT	rs8178750	8	42164028	Intron
	Tumor necrosis factor alpha	TNF	rs1800610	6	31651806	Intron
	Lymphotoxin alpha precursor	LTA	rs1041981	6	31648763	Coding Exon (T60N)

Table 3.

Single locus tests of association (p < 0.01) unadjusted for covariates

Population	Gene Code	rs#	Minor Allele	Minor Allele Frequency		OR	95% CI		P-Value
				Cases	Controls		Lower	Upper
Maternal (Cases n = 225; Controls n = 599)	REN	rs8192282	A	0.13	0.08	1.68	1.18	2.39	0.004
	COL4A3	rs1882435	A	0.33	0.26	1.4	1.1	1.78	0.006
	TLR1	rs3923647	T	0.06	0.02	2.59	1.47	4.58	0.001
	CSPG2	rs2287926	A	0.14	0.09	1.57	1.12	2.22	0.009
	RNASE4	rs11701	G	0.13	0.18	0.64	0.47	0.89	0.008
	IGF1R	rs3743262	T	0.2	0.14	1.58	1.18	2.12	0.002
	TIMP2	rs2277698	A	0.13	0.07	1.88	1.32	2.69	0.0005
Fetal (Cases n = 155; Controls n = 628)	F5	rs6019	C	0.07	0.04	1.99	1.18	3.33	0.0096
	CD55	rs10746462	A	0.31	0.24	1.5	1.13	1.99	0.005
	COL4A3	rs1882435	A	0.35	0.26	1.51	1.16	1.97	0.002
	CCR2	rs3749461	G	0.07	0.03	2.24	1.26	3.97	0.006
	FGF1	rs34003	G	0.3	0.4	0.67	0.51	0.88	0.004
	IL18BP	rs5743658	C	0.05	0.02	2.59	1.32	5.09	0.006
	MMP19	rs1056784	T	0.02	0	7.16	2.07	24.79	0.002
	IMP5	rs283640261	A	0.08	0.15	0.57	0.38	0.85	0.007

The most significant association in maternal DNA, after adjusting for fetal gender and BMI, was at a synonymous coding SNP (S101S) in tissue inhibitor of metalloproteinase 2 (TIMP2) rs2277698 (OR = 2.12 [95% CI 1.47-3.07], p = 6.8 × 10⁻⁶) (Table 4). The minor allele frequency of this SNP (A) was 0.13 in cases and 0.07 in controls. The most significant association observed in fetal DNA was in a SNP in the chemokine (C-C motif) receptor 2 (CCR2) promoter region, rs3749461 (OR = 2.62 [95% CI 1.44-4.75], p = 0.002). The minor allele frequency for this SNP (G) was 0.07 in cases and 0.03 in controls. Only the association with maternal SNP rs2277698 remained statistically significant after correction for multiple testing using FDR.

Table 4.

Single locus tests of association (p < 0.01) adjusted for fetal sex and BMI

Population	Gene Code	rs#	Minor Allele	Minor Allele Frequency		OR2	95% CI3		p-value
				Cases	Controls		Lower	Upper
Maternal (Cases n = 225; Controls n = 599)	TIMP2	rs2277698	A	0.13	0.07	2.12	1.47	3.07	0.000068*
	ANG	rs11701	G	0.13	0.18	0.58	0.41	0.83	0.003
	TLR1	rs3923647	T	0.06	0.02	2.40	1.30	4.43	0.005
	NOS3	rs3730305	A	0.07	0.04	1.91	1.21	3.01	0.005
	COL4A3	rs1882435	A	0.33	0.26	1.42	1.10	1.83	0.007
	PTGER1	rs37454591	T	0.05	0.02	2.00	1.20	3.34	0.008
Fetal (Cases n = 155; Controls n = 628)	CCR2	rs3749461	G	0.07	0.03	2.62	1.44	4.75	0.002
	MMP19	rs1056784	T	0.02	0.00	6.81	1.95	23.83	0.003
	CRHR1	rs283640261	A	0.08	0.15	0.52	0.33	0.80	0.003
	COL4A3	rs1882435	A	0.35	0.26	1.51	1.14	1.99	0.004
	CD55/DAF	rs10746462	A	0.31	0.24	1.52	1.13	2.04	0.006
	DEFB1	rs5743418	T	0.04	0.02	2.84	1.33	6.07	0.007
	LIPC	rs6080	A	0.04	0.02	2.74	1.31	5.73	0.007
	IGF1R	rs3743262	T	0.20	0.14	1.62	1.14	2.30	0.008
	PLAT	rs8178750	T	0.05	0.09	0.42	0.22	0.80	0.008
	TNF	rs1800610	A	0.25	0.32	0.67	0.50	0.91	0.009
	LTA	rs1041981	A	0.34	0.27	1.47	1.10	1.97	0.0097

¹These SNPs are deviated from HWE.

²OR is the Odds Ratio for the additive genotypic model.

³95% CI is the 95% confidence interval of the Odds Ratio.

^*Significant after FDR correction.

Additional SNPs that were associated with pPROM at a p value < 0.05 are presented in Table 5. Although we have not emphasized these findings in the present report, in some instances they represent associations in genes reported that may lend support to previous findings or may be additional SNPs in genes reported with a p value < 0.01. Such findings strengthen the likelihood of an association because it will be based on multiple SNPs for the same gene (e.g. NOS3 in mothers, collagen genes in mothers, and MMP19 in the fetus).

Table 5.

Single SNP associations with pPROM (unadjusted p < 0.05)

A. Maternal DNA
Gene	SNP	A1	OR	L95	U95	p
TIMP2	rs2277698	A	2.122	1.466	3.073	6.79E-05
ANG	rs11701	G	0.5805	0.4076	0.8268	0.002579
TLR1	rs3923647	T	2.399	1.301	4.426	0.005087
NOS3	rs3730305	A	1.909	1.211	3.01	0.005372
COL4A3	rs1882435	A	1.42	1.101	1.832	0.007009
PTGER1	rs3745459	T	2.001	1.199	3.341	0.007988
CSF1	rs1058885	C	1.426	1.086	1.873	0.01072
IL6R	rs8192282	A	1.637	1.121	2.391	0.01081
VWF	rs1800377	A	0.4161	0.2094	0.8271	0.01236
COL4A4	rs12475686	T	1.361	1.066	1.738	0.01349
IGF1	rs5742620	A	2.361	1.194	4.667	0.01353
REN	rs3730103	G	1.87	1.128	3.101	0.01521
CSF1	rs333970	C	1.343	1.05	1.717	0.01877
ACE	rs4311	T	1.342	1.05	1.717	0.01899
MMP10	rs486055	A	1.837	1.105	3.054	0.01912
LPL	rs270	A	0.6478	0.4501	0.9323	0.01943
IL1A	rs3783550	C	1.324	1.046	1.676	0.01946
TNFRSF1B	rs5746051	G	0.66	0.4654	0.936	0.01975
IGF1R	rs3743262	T	1.447	1.058	1.978	0.02072
NOS2A	rs2779248	G	1.359	1.045	1.768	0.02193
CSPG2	rs2287926	A	1.519	1.058	2.179	0.02331
COL1A1	rs17639446	G	1.667	1.072	2.592	0.02331
TNR	rs1385540	T	1.367	1.023	1.827	0.03462
F3	rs610277	C	2.15	1.057	4.376	0.03467
IL1A	rs17561	T	0.7351	0.5516	0.9796	0.03569
ACE	rs4354	T	0.2752	0.08158	0.9286	0.03758
TBXAS1	GNSC_53711588	T	3.073	1.048	9.012	0.04087
MMP1	rs470132	T	1.387	1.013	1.898	0.04103
IFNGR2	rs9808753	G	1.412	1.013	1.967	0.04152
PLAT	rs8178750	T	0.6275	0.4008	0.9824	0.04157
MMP10	rs17860949	T	1.471	1.014	2.132	0.04191
FGF4	rs3740640	G	1.636	1.017	2.633	0.0425
LIPC	GNSC_16324977	A	1.893	1.007	3.556	0.04736
NOS3	rs1800782	T	1.664	1.005	2.756	0.04792

B. Fetal DNA
Gene	SNP	A1	OR	L95	U95	p
CCR2	46370317	G	2.62	1.444	4.753	0.001529
MMP19	54519580	T	6.807	1.945	23.83	0.002696
CRHR1	41268075	A	0.5161	0.3314	0.8036	0.003411
COL4A3	227810996	A	1.51	1.144	1.994	0.003652
DAF	205577171	A	1.517	1.13	2.038	0.005597
DEFB1	6722970	T	2.84	1.33	6.065	0.007014
LIPC	56625225	A	2.739	1.309	5.732	0.00749
IGF1R	97282996	T	1.616	1.135	2.302	0.007757
PLAT	42164028	T	0.4245	0.2244	0.8029	0.008413
TNF	31651806	A	0.6721	0.4981	0.907	0.009364
LTA	31648763	A	1.472	1.098	1.974	0.009728
PROS1	95129086	A	1.445	1.087	1.922	0.01135
SERPINE1	100567623	T	4.524	1.402	14.6	0.01157
GNB3	6820171	A	2.005	1.166	3.447	0.01192
COL4A1	109659786	G	1.598	1.107	2.307	0.01231
PLAUR	48851659	G	2.075	1.166	3.693	0.01301
COL4A4	227681867	T	1.415	1.076	1.862	0.01314
FGF1	141955251	G	0.7177	0.5443	0.9465	0.01879
IGF2R	160443699	T	0.4062	0.1912	0.8632	0.01915
IL5RA	3093142	A	1.53	1.07	2.188	0.01968
COL5A2	189683203	C	1.422	1.056	1.915	0.02058
TLR2	154844859	C	1.881	1.088	3.254	0.02377
IL18BP	71387372	C	2.223	1.077	4.586	0.03069
MMP16	89409417	A	1.521	1.036	2.235	0.03247
MMP19	54521518	T	0.4382	0.2047	0.9377	0.03354
CSF1	110267989	C	1.387	1.021	1.884	0.03618
PTGS1	124173328	T	0.2857	0.0868	0.9405	0.03932
IL12RB1	18031384	T	1.433	1.016	2.023	0.0406
APOC3	116206884	A	1.347	1.009	1.797	0.04324
IGF1	101398994	C	0.5903	0.3539	0.9845	0.04339
AGT	228912600	T	0.6469	0.4224	0.9906	0.04512
FLT1	27910114	A	0.7273	0.5323	0.9937	0.04551
FN1	215951895	C	0.7467	0.5605	0.9947	0.04589
F5	167808137	C	1.744	1.006	3.024	0.04763
VWF	6043694	A	0.4215	0.1792	0.9916	0.04779

Haplotype Tests of Association

Haplotype analyses of genes with at least one significant SNP (p < 0.05) and two SNPs in the gene identified one gene, Alpha 3 type IV collagen isoform precursor (COL4A3), in maternal DNA samples that was associated with risk for pPROM (Table 6). The haplotype included markers rs1882435-rs10178458-GNSC_634673878 (global p = 0.003). This haplotype had rs1882435, a SNP that was associated with the risk of pPROM (Table 4) (p = 0.007) where the (A) allele is the risk allele. Upon examining the individual haplotypes, it was clear that all statistically significant haplotypes contained the rs1882435 risk allele, although the effect size is greater and the p value is much less for the haplotype than for the single SNP results. Examination of the LD plot for COL4A3 (Figure 1) demonstrated that these three markers were in overall weak LD (r² ≤ 0.03), further supporting a true haplotype effect.

Table 6.

Haplotype sliding windows association results unadjusted for covariates (p < 0.05)

Population	Gene Code	SNP rs#	Haplotype	Frequency		OR	95% CI		p-value
				Cases	Controls		Lower	Upper
Maternal	COL4A3	rs1882435-rs10178458-GNSC_634673878	Global p						0.003
			CCT (Referent)	0.59	0.66
			ACT	0.32	0.23	1.55	1.20	1.99	0.0004
			CTT	0.09	0.11	0.92	0.62	1.36	0.680

Figure 1. Haploview plots of genes identified in analyses of haplotype tests of association in maternal samples.

LD plots were generated in Haploview and are presented for: A) COL4A3 cases r²; and B) COL4A3 controls r². Within each triangle is presented the pairwise correlation coefficient (r²) LD plots white, (r² = 0), shades of grey, (0 < r² < 1), black, (r² = 1).

Histologic chorioamnionitis

Sub-analyses of all statistically significant single locus and haplotype associations for differences between cases with histologic chorioamnionitis and controls (Tables 7 and 8) demonstrated a decrease in the OR for the maternal SNP in TIMP2, rs2277698, with the OR dropping from 2.12 to 1.22, as well as a loss of statistical significance for maternal samples (Table 7). The OR for CCR2 SNP rs3749461 increased from 2.62 to 3.41 and remained statistically significant (Table 7). The COL4A3 haplotype rs1882435-rs10178458-GNSC_634673878 that associated in maternal samples was not statistically different between cases with histologic chorioamnionitis and controls (Table 8).

Table 7.

Histologic chorioamnionitis analysis of statistically significant associations (p < 0.01, see Table 5)

Population	Gene Code	rs#	OR	95% CI		p-value
				Lower	Upper
Maternal (Cases n = 78; Controls n = 452)	COL4A3	rs1882435	1.20	0.82	1.77	0.352
	TLR1	rs3923647	2.14	0.93	4.91	0.074
	NOS3	rs3730305	2.54	1.42	4.52	0.002
	ANG	rs11701	0.49	0.28	0.86	0.013
	TIMP2	rs2277698	1.22	0.67	2.23	0.517
	PTGER1	rs3745459	1.48	0.62	3.55	0.382
Fetal (Cases n = 57; Controls n = 469)	CCR2	rs3749461	3.41	1.49	7.81	0.004
	CD55/DAF	rs10746462	1.90	1.24	2.93	0.003
	COL4A3	rs1882435	1.65	1.09	2.50	0.019
	LTA	rs1041981	1.77	1.14	2.75	0.012
	TNF	rs1800610	0.79	0.50	1.24	0.304
	DEFB1	rs5743418	1.09	0.26	4.51	0.907
	PLAT	rs8178750	0.43	0.16	1.18	0.101
	MMP19	rs1056784	7.67	1.47	39.98	0.016
	LIPC	rs6080	2.48	0.92	6.71	0.073
	IGF1R	rs3743262	1.47	0.87	2.50	0.153
	CRHR1	rs28364026	0.57	0.30	1.08	0.083

Table 8.

Histologic chorioamnionitis analyses of statistically significant haplotype associations (p<0.05)

Population	Gene Code	SNP rs#	Haplotype	Frequency		OR	95% CI		p-value
				Cases	Controls		Lower	Upper
Maternal	COL4A3	rs1882435-rs10178458-GNSC_634673878	Global p						0.211
			CCT (Referent)	0.63	0.66	-	-	-	-
			ACT	0.29	0.23	1.32	0.87	1.97	0.161
			CTT	0.08	0.11	0.74	0.36	1.41	0.349

MDR analysis

Exploratory MDR analyses were performed using different filtering approaches (Table 9). The only model with a p < 0.05 and a high cross validation consistency (10 of 10) was found in analyses of SNPs filtered using TuRF in the combined fetal and maternal analyses (Table 9C, Figure 2). This model included rs5369_maternal rs1800248_maternal rs4610776_maternal and had a testing balanced accuracy of 0.60 (p = 0.047), with a cross validation consistency of 10/10. The SNP, rs5369, is a synonymous substitution in exon 3 in endothelin 1 (EDN1), the SNP, rs1800248, also encodes a synonymous substitution in exon 47 in collagen type I alpha 2 (COL1A2) and SNP, rs4610776, is 5′ to the transcribed part of the defensin alpha 5 gene (DEFA5).

Table 9.

Summary of MDR analyses

A. Preterm PROM Maternal with tagged SNPs
Model	Training Balance Accuracy	Testing Balance Accuracy	Cross Validation Consistency	p-value
All SNPs
rs11701	0.5700	0.5199	3/10	0.8263
rs2301339 rs5445	0.6265	0.5839	5/10	0.1062
rs28763986 rs6083 rs1800774	0.6896	0.4652	1/10	0.5703
Turf Option - 10 SNPs
rs11701	0.5677	0.5315	6/10	0.5779
rs2069849 rs11701	0.6103	0.5621	5/10	0.3066
rs1385540 rs454078 rs2301339	0.6430	0.5743	4/10	0.2292
Genotypic p-value cutoff - 0.05
rs11701	0.5700	0.5119	3/10	0.8263
rs2071307 rs1254600	0.6129	0.5393	3/10	0.5234
rs352140 rs2479426 rs2293117	0.6673	0.5732	6/10	0.2350
Genotypic p-value cutoff - 0.10
rs11701	0.5674	0.5350	5/10	0.5360
rs17876029 rs11701	0.6057	0.5273	2/10	0.6554
rs8192282 rs8178610 rs2479426	0.6521	0.5842	4/10	0.1513

B. Preterm PROM Fetal with tagged SNPs
Model	Training Balance Accuracy	Testing Balance Accuracy	Cross Validation Consistency	p-value
All SNPs
rs34003	0.5848	0.5729	9/10	0.2881
rs2069762 rs2301339	0.6410	0.5687	5/10	0.3097
rs2069762 rs1041981 rs2252070	0.7042	0.5016	1/10	0.9820
Turf Option - 10 SNPs
rs25645	0.5639	0.5286	7/10	0.6870
rs11541998 rs25645	0.6086	0.5270	4/10	0.7071
rs11764718 rs25645 rs3746190	0.6633	0.5596	6/10	0.4020
Genotypic p-value cutoff - 0.05
rs2020920	0.5312	0.4934	4/10	0.8692
rs3917727 rs2071538	0.5740	0.5217	8/10	0.7487
rs3917727 rs2071538 rs16940668	0.6071	0.5336	10/10	0.6173
Genotypic p-value cutoff - 0.10
rs2069762	0.5559	0.5431	10/10	0.4278
rs2069762 rs1799962	0.5876	0.5615	4/10	0.2881
rs5990 rs2069762 rs2071538	0.6441	0.5607	7/10	0.3909

C. Preterm PROM Maternal-Fetal Combined with tagged SNPs
Model	Training Balance Accuracy	Testing Balance Accuracy	Cross Validation Consistency	p-value
All SNPs
rs2071538_2	0.6837	0.6636	4/10	0.0002
rs6750027_2 rs549908	0.7134	0.6586	2/10	0.0010
GNSC_634673660_2 rs549908 rs1077835_2	0.7543	0.6817	2/10	0.0015
Turf Option - 10 SNPs
rs5445	0.5536	0.5521	8/10	0.2572
rs5369 rs1800248	0.5925	0.5791	10/10	0.1061
rs5369 rs1800248 rs4610776	0.6211	0.5995	10/10	0.0465
Genotypic p-value cutoff - 0.05
rs5743418_2	0.6825	0.6363	5/10	0.0013
rs1058885 rs645114_2	0.7088	0.6686	4/10	0.0008
rs1882435_2 rs4311 rs4251883_2	0.7408	0.6984	3/10	0.0003
Genotypic p-value cutoff - 0.10
rs5743418_2	0.6817	0.6434	4/10	0.0008
rs1058885 rs645114_2	0.7088	0.6686	4/10	0.0008
rs1058885 rs1882435_2 rs6909681_2	0.7474	0.6778	4/10	0.0012

^*Bold indicates a statistically significant interaction (permutation p < 0.05).

Combined analysis consisted of matching fetal to maternal individuals and adding the SNPs to the analysis (i.e., instead of an individual having 672 SNPs this would increase to 1374). Any result with a “_2” indicates the genotypes were fetal. Any maternal or fetal genotypes without matches were removed from the combined analysis.

Figure 2. MDR results for maternal-fetal analyses.

MDR model for a three-way interaction involving maternal SNPs rs4610776 (DEFA5) and rs5359 (EDN1) and rs1800248 (COL1A2). Each panel represents a three locus genotype; the genotype for each SNP is labeled on the figure. Each large square (3 × 3 box) represents a different genotype for rs4610776 (AA on left, AT in the middle, and TT on the right). Within each square, each row of cells delineates rs1800248 genotypes (top CC, middle CT and bottom TT) and each column the rs35369 genotypes. Therefore, each small cell describes a single and unique three locus genotype. Within each cell are two bars that represent the number of cases with this genotype (left hand bar) and number of controls (right hand bar). Each multilocus cell is denoted as “high risk” (dark gray) or “low risk” (light gray) for spontaneous preterm labor/delivery with intact membranes. Empty cells are shown in white. Risk status is determined by the ratio of cases to controls adjusted by the number of cases and controls studied. The testing average balanced accuracy is 60% (p-value = 0.047) with a cross-validation consistency of 10 out of 10.

Pathway analysis

To discover novel networks of interacting molecules, the IPA was seeded with SNPs meeting the criteria of p < 0.05. In mothers, the IPA network algorithm discovered that the focus molecules were significantly interconnected in four networks (scoring 3 to 36, which corresponds to p=10⁻³ to p=10⁻³⁶) (Supplemental Table 3A). These networks were joined together by a few molecules, namely TIMP2 among our input genes (in two of four networks) as well as several network partners derived from the IPA database, which included MMPs and a wide representation of extracellular matrix proteins. The top ranked network is illustrated in Figure 3. IPA identified regulatory interactions involving our “focus SNPs” (pink and red in Figure 3) that incorporated other molecules of interest in pPROM. The IPA algorithm identified the top functions of this network as “organismal injury and abnormalities”, “connective tissue disorders” and “inflammatory disease.” Notably, “connective tissue disorders” or “connective tissue development” pathways were identified in three of the four top ranking networks, all of which include multiple inflammatory and extracellular matrix metabolism related genes, supporting the involvement of these genes in pPROM.

Figure 3. Connection map for the first ranked network generated by IPA from maternal focus gene input.

The biomarkers passing the p < 0.05 significance threshold (focus molecules, depicted as pink or red) were entered into the IPA software for an unsupervised functional analysis to discern regulatory networks involving these molecules. The asterisk indicates that there was more than one SNP probe for the gene tested and the most significant value was placed into the analysis. Solid lines show direct interaction (binding/physical contact); dashed line, indirect interaction supported by the literature but possibly involving one or more intermediate molecules that have not been investigated definitively. Molecular interactions involving only binding are connected with a solid line (no arrowhead) since directionality cannot be inferred.

Using the fetal genes as input, the IPA network algorithm discovered that “fetal focus genes” are highly interconnected. It modeled these into four networks (scoring 3 to 35, corresponding to p=10⁻³ to p=10⁻³⁵) (Supplemental Table 3B; Figure 4). Similar to the maternal model, the top-ranking network identified “organismal injury and abnormalities” as one of the top disease function which contain collagen type IV, MMPs, pro-inflammatory cytokines/chemokines-related molecules (including TNF alpha, CSF-1, CCR2, IL12 receptor beta 1, IL18 binding protein). Notably, other pathways identified by IPA contained a substantial number of extracellular matrix proteins such as collagens, MMPs, and related molecules (e.g. plasminogen activator). Other top functions included infection mechanisms and cell death that have previously been implicated in pPROM.

Figure 4. Connection map for the first ranked network generated by IPA from fetal focus gene input.

The biomarkers passing the p < 0.05 significance threshold (focus molecules, depicted as pink or red) were entered into the IPA software for an unsupervised functional analysis to discern regulatory networks involving these molecules. The asterisk indicates that there was more than one SNP probe for the gene tested and the most significant value was placed into the analysis. Solid lines show direct interaction (binding/physical contact); dashed line, indirect interaction supported by the literature but possibly involving one or more intermediate molecules that have not been investigated definitively. Molecular interactions involving only binding are connected with a solid line (no arrowhead) since directionality cannot be inferred.

COMMENT

Principal findings of the study

We report the results of a relatively large carefully phenotyped genetic association study of women with pPROM in a homogeneous Hispanic population. This genetic association study of maternal and fetal candidate genes identified DNA variants that predispose to pPROM leading to preterm delivery. The main observations were: 1) A SNP in TIMP2 in mothers was significantly associated with this phenotype; 2) Haplotypes for COL4A3 in the mother were associated with pPROM; 3) Multilocus analysis identified a three locus model, which included maternal SNPs in COL1A2, DEFA5, as well as EDN1; and 4) Pathway analysis suggests that maternal and fetal genes involved in the regulation of extracellular matrix metabolism and inflammation are involved in the biological processes that predispose to pPROM. Taken together, these findings support the hypothesis that genetic variation plays a significant role in predisposition to pPROM, and that this involves DNA variants in genes that participate in the inflammatory response and extra cellular matrix metabolism.

Single locus analysis for mothers

The observed association between TIMP2 and pPROM is novel and lends support to the view that the genetic control of extracellular matrix metabolism is an important factor predisposing to pPROM. This result is consistent with our initial argument regarding the relationship between Ehlers-Danlos Syndrome and risk of pPROM as well as with previously demonstrated imbalance between MMPs and TIMPs in the amniotic fluid of women with pPROM in the presence or absence of intra-amniotic infection.^130-134

These observations are consistent with in vitro studies in which microbial products added to fetal membrane explants generated an imbalance between MMPs and TIMPs, tilting the balance towards matrix degradation.^135-141 TIMP2 plays an important role in regulating the activities of matrix degrading enzymes. MMP1, MMP8 and MMP9 have been implicated in the mechanisms responsible for membrane rupture. Indeed, the amniotic fluid concentrations of all these enzymes are increased in patients with pPROM (with and without intra-amniotic infection/inflammation).^{130-134;142-148} Inasmuch as TIMP2 can modulate the activities of MMPs, the association of a DNA variant in TIMP2 with pPROM is of considerable interest. MMP2 is a constitutive enzyme, while MMP9 is inducible.¹⁴⁹ Both have been found in amniotic fluid and the concentrations of both zymogen and inhibitor free active forms of MMP9 are elevated in the amniotic fluid of women with pPROM.^{133;144;150-153} We have previously reported that amniotic fluid TIMP2 concentrations are lower in women with spontaneous labor (term and preterm), with intact or ruptured membranes, regardless of the microbial status of the amniotic cavity, than in women not in labor.¹³³ A decrease in TIMP2 amniotic fluid concentration is thought to favor MMP activity promoting extracellular matrix degradation, which has been associated with labor.¹⁵² In a parallel genetic association study of women with preterm labor with intact membranes, we found a significant association between the same TIMP2 SNP and this phenotype (in press). Therefore, there is consistency in the finding of an association between the carriage of this particular DNA variant in TIMP2 and spontaneous preterm labor/delivery, regardless of membrane status. The SNP associated with preterm PROM in TIMP2 is located in an exon; however, there is no evidence at this time that this SNP is functional. In other words, that it changes the protein level.

Haplotype analyses identified novel genes predisposing to pPROM

Maternal haplotype analyses revealed that haplotypes in COL4A3 were associated with pPROM. One particular haplotype (ACT) was associated with a 55% increased risk of pPROM (See Table 6). Collagen IV is a major component of the basement membrane of the amnion, chorion, and the uterine cervix. The degradation of collagen type IV is important for parturition. MMP2 and MMP9 specifically cleave collagen type IV; and TIMP2, where we observed the most significant single locus SNP association with pPROM, is a regulator of the activity for these enzymes. Therefore, the findings of haplotype analysis for collagen IV and the single locus association in the mother supports the relationship between structural proteins of the extracellular matrix (collagen IV) and a regulator of its degradation (TIMP2), lending substantial biological plausibility to both associations.

Histologic chorioamnionitis

The sub-analysis of cases with histologic chorioamnionitis was informative because several associations were either weakened or completely disappeared when compared to the entire data set. This may mean that, in this subset, the major association is not driven by infection but by other biological processes that are independent of this. For example, the major association with TIMP2 in maternal DNA changes from highly significant in the entire data set to not significant in the histologic chorioamnionitis subset. Such changes may reflect variation in gene by environment interactions for this and other genes. Thus, the data are suggestive that most of the associations are not motivated by histologic chorioamnionitis. However, we recognize the need to be cautious in this interpretation because the sample size in the subset was substantially less than in the entire dataset, thereby reducing power.

Multi-locus analyses

Preterm PROM is syndromic in nature,^154;155 and multiple mechanisms of disease are likely to be involved.^2;156-173 To address the complexity of the genetic predisposition to this phenotype,^174;175 we performed exploratory multi-locus analyses using MDR to explicitly address the potential role of interactions among genes (maternal, fetal, and maternal-fetal).¹⁷⁶ The results of these analyses indicate that three maternal genes, COL1A2, DEFA5, and EDN1, may interact to modify the risk for pPROM. These genes are involved in collagen metabolism,^58;177 susceptibility to bacterial infection¹⁷⁸ and uterine contractility,^179-181 respectively. Taken together, these findings may support the hypothesis that genetic epistasis between three major components of the common pathway of parturition (uterine contractility, cervical ripening, and membrane rupture) affect risk of pPROM.

Collagen I is a fibrillar protein which, together with type III collagen, are the major structural proteins present in the chorio-amniotic membranes and confer tensile strength to the membranes. Collagen I is also an important structural protein in the uterine cervix,¹⁸² and may play a role in the process of cervical remodeling during pregnancy.^183;184 DNA variants in the collagen I gene may alter the predisposition to pPROM by altering this structural protein in the reproductive tract; specifically, membranes and the cervix. Cervical insufficiency has been recognized as a cause of pPROM, and this would link maternal collagen I (structure and degradation) with rupture of membranes.

Concentrations of vaginal defensins are elevated in the presence of bacterial vaginosis,^185;186 a condition characterized by a gene-environment interaction in the etiology of preterm birth.^73;187 We have previously reported that DEFA5 is expressed by endocervical cells,¹⁸⁸ and this protein has been found in vaginal fluid, but expression has also been found in the stratified squamous epithelium of the vagina and ectocervix.¹⁸⁹ This antimicrobial peptide is also detectable in cervico-vaginal lavage fluid.¹⁹⁰ The highest concentrations in this fluid occur during the secretory phase of the menstrual cycle, indicating that it may be under progesterone control.¹⁸⁹ Defensin 5 has been implicated in the control of microbial proliferation in the lower genital tract and in preventing ascending intrauterine infection. Therefore, it is possible that DNA variants in this gene may modify the susceptibility to infection, and therefore, pPROM.

Finally, we note that the multi-locus analyses identified a SNP in EDN1 (endothelin 1) as contributing to gene-to-gene interaction predisposing to pPROM. We have previously reported that amniotic fluid EDN1 concentrations are elevated in the presence of intra-amniotic infection.¹⁹¹ This molecule induces smooth muscle contraction and is an uterotonic agent.^179-181 Importantly, Margarit et al.¹⁹² reported that women destined to develop pPROM have higher amniotic fluid concentrations of EDN1 in the midtrimester than those who do not have pPROM.

An integrated view of these findings is that three components of the common pathway of parturition (uterine contractility, cervical ripening, and membrane rupture) can be modified simultaneously by the genes identified in multi-locus analyses. Moreover, the finding that DNA variants in DEFA5 may also contribute to risk, link alterations in the host defense mechanisms in the lower genital tract and activation of the common pathway of parturition.

Pathway analysis

There is an increasing realization of the importance of pathways in the etiology of complex phenotypes. We utilized IPA to examine the contribution of genetic variants in determining networks and disease functions. As with the results presented above, the findings of IPA support the hypothesis that genes involved in extracellular matrix metabolism and inflammation pathways are associated with pPROM. Such findings are also consistent with a large body of literature supporting this view. Because the IPA knowledgebase has extensive coverage of molecular mechanisms across broad domains of biology and pathology, and it constructs the network models based on > 1 million known molecular interactions, the network partners discovered by our IPA analysis could represent novel pPROM biomarker candidates.

Strengths and limitations of the study

The strengths of our study include a well-defined phenotype (pPROM) and a homogeneous population. This is the largest study to examine the genetic predisposition to pPROM in Hispanics. Moreover, the study includes both maternal and fetal DNA and a relatively large number of genes and DNA variants. The number of DNA variants selected was estimated to cover 90% of the exonic and proximal DNA variation in the candidate genes. Importantly, we identified that maternal DNA variants contributed to modify the risk. Limitations of these types of studies are that confirmation of the findings is required and that we have not examined the effect of environmental factors that are known to play a role in the risk of pPROM. In addition, functional studies are needed to assess the precise physiological implications of the DNA variants identified in this study. Although previous studies have found associations between fetal DNA variants and pPROM, we did not find any significant association in fetal DNA that passed correction for multiple testing. This may represent false negative results. Also, we did not study the identical variants found in previous studies because our genotyping platform was not appropriate for these variants. Finally, the findings observed in this Hispanic population may not be representative of other ethnic groups. Further studies are required to replicate our findings and those of others, as well as to identify if fetal DNA variants may play a significant role in the predisposition to pPROM. Moreover, the role of maternal-fetal interactions and incompatibility needs to be explored. It is possible that differences in DNA variants between the maternal and fetal genome predispose to adverse pregnancy outcome.^193-195

Conclusion

This genetic association study of candidate genes involved in adverse pregnancy outcome revealed that maternal DNA variants are associated with pPROM.