[show abstract][hide abstract] ABSTRACT: Germline variations in allele-specific expression (ASE) are associated with highly penetrant familial cancers, but their role in common sporadic cancers is unclear. ASE of adenomatous polyposis coli (APC) is associated with pathogenesis of familial adenomatous polyposis. We investigated whether moderate variations in ASE of APC contribute to common forms of colorectal cancer (CRC).
Denaturing high-performance liquid chromatography was used to analyze germline ASE of APC in blood samples from patients with CRC (cases, n = 53) and controls (n = 68). Means, medians, and variances of ASE were compared. Variants in the APC gene region also were analyzed.
The distribution of ASE differed significantly between groups; cases had significantly larger amounts of variance than controls (P = .0004). Risk for CRC increased proportionally with the degree of deviation from the mean. The odds ratio for individuals with levels of ASE that deviated more than 1 standard deviation from the mean was 3.97 (95% confidence interval, 1.71-9.24; P = .001); for those with levels greater than 1.645 standard deviations, the odds ratio was 13.46 (95% confidence interval, 1.76-609.40; P = .005). Sequence analysis revealed that a patient with a high level of ASE who did not have a family history of CRC carried a nonsense mutation in APC (p.Arg216X). Genotype analysis of APC associated multiple single-nucleotide polymorphisms with ASE values and/or variance among cases, but not controls. Cis variants, therefore, might account for some of the variance in ASE of APC.
Patients with CRC have a larger variance in germline levels of ASE in APC than controls; large distances from the mean ASE were associated with risk for common forms of CRC.
[show abstract][hide abstract] ABSTRACT: To examine the association between maternal and fetal genetic variants and small-for-gestational-age (SGA).
A case-control study was conducted in patients with SGA neonates (530 maternal and 436 fetal) and controls (599 maternal and 628 fetal); 190 candidate genes and 775 SNPs were studied. Single-locus, multi-locus and haplotype association analyses were performed on maternal and fetal data with logistic regression, multifactor dimensionality reduction (MDR) analysis, and haplotype-based association with 2 and 3 marker sliding windows, respectively. Ingenuity pathway analysis (IPA) software was used to assess pathways that associate with SGA.
The most significant single-locus association in maternal data was with a SNP in tissue inhibitor of metalloproteinase 2 (TIMP2) (rs2277698 OR = 1.71, 95% CI [1.26-2.32], p = 0.0006) while in the fetus it was with a SNP in fibronectin 1 isoform 3 preproprotein (FN1) (rs3796123, OR = 1.46, 95% CI [1.20-1.78], p = 0.0001). Both SNPs were adjusted for potential confounders (maternal body mass index and fetal sex). Haplotype analyses resulted in associations in α 1 type I collagen preproprotein (COL1A1, rs1007086-rs2141279-rs17639446, global p = 0.006) in mothers and FN1 (rs2304573-rs1250204-rs1250215, global p = 0.045) in fetuses. Multi-locus analyses with MDR identified a two SNP model with maternal variants collagen type V α 2 (COL5A2) and plasminogen activator urokinase (PLAU) predicting SGA outcome correctly 59% of the time (p = 0.035).
Genetic variants in extracellular matrix-related genes showed significant single-locus association with SGA. These data are consistent with other studies that have observed elevated circulating fibronectin concentrations in association with increased risk of SGA. The present study supports the hypothesis that DNA variants can partially explain the risk of SGA in a cohort of Hispanic women.
The journal of maternal-fetal & neonatal medicine: the official journal of the European Association of Perinatal Medicine, the Federation of Asia and Oceania Perinatal Societies, the International Society of Perinatal Obstetricians 02/2011; 24(2):362-80. · 1.36 Impact Factor
[show abstract][hide abstract] ABSTRACT: We sought to determine whether maternal/fetal single-nucleotide polymorphisms (SNPs) in candidate genes are associated with preterm prelabor rupture of membranes (pPROM).
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; false discovery rate was used to correct for multiple testing (q* = 0.15).
First, a SNP in tissue inhibitor of metalloproteinase 2 in mothers was significantly associated with pPROM (odds ratio, 2.12; 95% confidence interval, 1.47-3.07; P = .000068), and this association remained significant after correction for multiple comparisons. Second, haplotypes for Alpha 3 type IV collagen isoform precursor in the mother were associated with pPROM (global P = .003). Third, multilocus analysis identified a 3-locus model, which included maternal SNPs in collagen type I alpha 2, defensin alpha 5 gene, and endothelin 1.
DNA variants in a maternal gene involved in extracellular matrix metabolism doubled the risk of pPROM.
American journal of obstetrics and gynecology 10/2010; 203(4):361.e1-361.e30. · 3.28 Impact Factor
[show abstract][hide abstract] ABSTRACT: The purpose of this study was to determine whether maternal/fetal single nucleotide polymorphisms (SNPs) in candidate genes are associated with spontaneous preterm labor/delivery.
A genetic association study was conducted in 223 mothers and 179 fetuses (preterm labor with intact membranes who delivered <37 weeks of gestation [preterm birth (PTB)]), and 599 mothers and 628 fetuses (normal pregnancy); 190 candidate genes and 775 SNPs were studied. Single locus/haplotype association analyses were performed; the false discovery rate was used to correct for multiple testing.
The strongest single locus associations with PTB were interleukin-6 receptor 1 (fetus; P=.000148) and tissue inhibitor of metalloproteinase 2 (mother; P=.000197), which remained significant after correction for multiple comparisons. Global haplotype analysis indicated an association between a fetal DNA variant in insulin-like growth factor F2 and maternal alpha 3 type IV collagen isoform 1 (global, P=.004 and .007, respectively).
An SNP involved in controlling fetal inflammation (interleukin-6 receptor 1) and DNA variants in maternal genes encoding for proteins involved in extracellular matrix metabolism approximately doubled the risk of PTB.
American journal of obstetrics and gynecology 05/2010; 202(5):431.e1-34. · 3.28 Impact Factor
[show abstract][hide abstract] ABSTRACT: Genetic association is often determined in case-control studies by the differential distribution of alleles or genotypes. Recent work has demonstrated that association can also be assessed by deviations from the expected distributions of alleles or genotypes. Specifically, multiple methods motivated by the principles of Hardy-Weinberg equilibrium (HWE) have been developed. However, these methods do not take into account many of the assumptions of HWE. Therefore, we have developed a prevalence-based association test (PRAT) as an alternative method for detecting association in case-control studies. This method, also motivated by the principles of HWE, uses an estimated population allele frequency to generate expected genotype frequencies instead of using the case and control frequencies separately. Our method often has greater power, under a wide variety of genetic models, to detect association than genotypic, allelic or Cochran-Armitage trend association tests. Therefore, we propose PRAT as a powerful alternative method of testing for association.
[show abstract][hide abstract] ABSTRACT: The identification of susceptibility genes for common, chronic disease presents great challenges. The development of novel statistical and computational methodologies to help identify these genes is an area of great necessity. Much research is ongoing and the Genetic Analysis Workshop (GAW) is a venue for the dissemination and comparison of many of these methods. GAW15 included real data sets to look for disease susceptibility genes for rheumatoid arthritis (RA). RA is a complex, chronic inflammatory disease with several replicated disease genes, but much of the genetic variation in the phenotype remains unexplained. We applied two computational methods, namely multifactor dimensionality reduction (MDR) and grammatical evolution neural networks (GENN), to three data sets from GAW15. While these analytic methods were applied with the intention of detecting of multilocus models of association, both methods identified a strong single locus effect of a single-nucleotide polymorphism (SNP) in PTPN22 that is significantly associated with RA. This SNP has previously been associated with RA in several other published studies. These results demonstrate that both MDR and GENN are capable of identifying a single-locus main effect, in addition to multilocus models of association. This is the first published comparison of the two methods. Because GENN employs an evolutionary computation search strategy in comparison to the exhaustive search strategy of MDR, it is encouraging that the two methods produced similar results. This comparison should be extended in future studies with both simulated and real data.
[show abstract][hide abstract] ABSTRACT: Interest in mapping susceptibility alleles for complex diseases, which do not follow a classic single-gene segregation pattern, has driven interest in methods that account for, or use information from one locus when mapping another. Our discussion group examined methods related to epistasis or gene x gene interaction. The goal of modeling gene x gene interaction varied across groups; some papers tried to detect gene x gene interaction while others tried to exploit it to map genes. Most of the 10 papers summarized here applied newly created or newly modified statistical methods related to gene x gene interaction, while two groups primarily examined computational issues. As is often the case, comparisons are complicated by little overlap in the data used across the papers, and further complicated by the fact that the available data may not have been ideal for some gene x gene interaction methods. However, the main difficulty in comparing and contrasting methods across the papers is the lack of a consistent statistical definition of gene x gene interaction. But despite these issues, two clear trends emerged across the analyses: First, the methods for quantitative trait gene x gene interaction appeared to perform very well, even in families initially ascertained as affected sib pairs; and second, dichotomous trait gene x gene interaction methods failed to produce consistent results. The difficulty of using (primarily) affected sib pair data in a gene x gene interaction analysis is explored.