Detecting maternal-fetal genotype interactions associated with conotruncal heart defects: a haplotype-based analysis with penalized logistic regression

Association of maternal methionine synthase reductase gene polymorphisms with the risk of congenital heart disease in offspring: a hospital-based case-control study

BACKGROUND

Evidence suggests that periconceptional folic acid supplementation may prevent congenital heart disease (CHD). Methionine synthase reductase (MTRR) is one of the key regulatory enzymes in the folate metabolic pathway. This study aimed to comprehensively evaluate the association of single nucleotide polymorphisms (SNPs) in the maternal MTRR gene with CHD risk in offspring.

METHODS

A hospital-based case-control study involving 740 mothers of CHD cases and 683 health controls was conducted.

RESULTS

The study showed that maternal MTRR gene polymorphisms at rs1532268 (C/T vs. C/C: aOR = 1.524; T/T vs. C/C: aOR = 3.178), rs1802059 (G/A vs. G/G: aOR = 1.410; A/A vs. G/G: aOR = 3.953), rs2287779 (G/A vs. G/G: aOR = 0.540), rs16879334 (C/G vs. C/C: aOR = 0.454), and rs2303080 (T/A vs. T/T: aOR = 0.546) were associated with the risk of CHD. And seven haplotypes were observed to be associated with the risk of CHD, T-G-A haplotype (OR = 1.298), C-A-C-C (OR = 4.824) and A-G haplotype (OR = 1.751) were associated with increased risk of CHD in offspring; A-A-A (OR = 0.773), T-A-A (OR = 0.557), G-A-C-C (OR = 0.598) and G-C (OR = 0.740) were associated with decreased risk of CHD in offspring.

CONCLUSIONS

Maternal MTRR gene polymorphisms were associated with CHD in offspring, and its haplotypes have affected the occurrence of CHD. Furthermore, given the complexity and heterogeneity of CHD, the mechanisms by which these factors influence offspring cardiac development remain unknown, and studies in larger samples in an ethnically diverse population are needed.

A gene-based association test of interactions for maternal-fetal genotypes identifies genes associated with nonsyndromic congenital heart defects

The risk of congenital heart defects (CHDs) may be influenced by maternal genes, fetal genes, and their interactions. Existing methods commonly test the effects of maternal and fetal variants one-at-a-time and may have reduced statistical power to detect genetic variants with low minor allele frequencies. In this article, we propose a gene-based association test of interactions for maternal-fetal genotypes (GATI-MFG) using a case-mother and control-mother design. GATI-MFG can integrate the effects of multiple variants within a gene or genomic region and evaluate the joint effect of maternal and fetal genotypes while allowing for their interactions. In simulation studies, GATI-MFG had improved statistical power over alternative methods, such as the single-variant test and functional data analysis (FDA) under various disease scenarios. We further applied GATI-MFG to a two-phase genome-wide association study of CHDs for the testing of both common variants and rare variants using 947 CHD case mother-infant pairs and 1306 control mother-infant pairs from the National Birth Defects Prevention Study (NBDPS). After Bonferroni adjustment for 23,035 genes, two genes on chromosome 17, TMEM107 (p = 1.64e-06) and CTC1 (p = 2.0e-06), were identified for significant association with CHD in common variants analysis. Gene TMEM107 regulates ciliogenesis and ciliary protein composition and was found to be associated with heterotaxy. Gene CTC1 plays an essential role in protecting telomeres from degradation, which was suggested to be associated with cardiogenesis. Overall, GATI-MFG outperformed the single-variant test and FDA in the simulations, and the results of application to NBDPS samples are consistent with existing literature supporting the association of TMEM107 and CTC1 with CHDs.

Gene-Folic Acid Interactions and Risk of Conotruncal Heart Defects: Results from the National Birth Defects Prevention Study

Conotruncal heart defects (CTDs) are heart malformations that affect the cardiac outflow tract and typically cause significant morbidity and mortality. Evidence from epidemiological studies suggests that maternal folate intake is associated with a reduced risk of heart defects, including CTD. However, it is unclear if folate-related gene variants and maternal folate intake have an interactive effect on the risk of CTDs. In this study, we performed targeted sequencing of folate-related genes on DNA from 436 case families with CTDs who are enrolled in the National Birth Defects Prevention Study and then tested for common and rare variants associated with CTD. We identified risk alleles in maternal MTHFS (OR_meta = 1.34; 95% CI 1.07 to 1.67), maternal NOS2 (OR_meta = 1.34; 95% CI 1.05 to 1.72), fetal MTHFS (OR_meta = 1.35; 95% CI 1.09 to 1.66), and fetal TCN2 (OR_meta = 1.38; 95% CI 1.12 to 1.70) that are associated with an increased risk of CTD among cases without folic acid supplementation. We detected putative de novo mutations in genes from the folate, homocysteine, and transsulfuration pathways and identified a significant association between rare variants in MGST1 and CTD risk. Results suggest that periconceptional folic acid supplementation is associated with decreased risk of CTD among individuals with susceptible genotypes.

Human Birth Weight and Reproductive Immunology: Testing for Interactions between Maternal and Offspring KIR and HLA-C Genes

BACKGROUND/AIMS

Maternal and offspring cell contact at the site of placentation presents a plausible setting for maternal-fetal genotype (MFG) interactions affecting fetal growth. We test hypotheses regarding killer cell immunoglobulin-like receptor (KIR) and HLA-C MFG effects on human birth weight by extending the quantitative MFG (QMFG) test.

METHODS

Until recently, association testing for MFG interactions had limited applications. To improve the ability to test for these interactions, we developed the extended QMFG test, a linear mixed-effect model that can use multi-locus genotype data from families.

RESULTS

We demonstrate the extended QMFG test's statistical properties. We also show that if an offspring-only model is fit when MFG effects exist, associations can be missed or misattributed. Furthermore, imprecisely modeling the effects of both KIR and HLA-C could result in a failure to replicate if these loci's allele frequencies differ among populations. To further illustrate the extended QMFG test's advantages, we apply the extended QMFG test to a UK cohort study and the Norwegian Mother and Child Cohort (MoBa) study.

CONCLUSION

We find a significant KIR-HLA-C interaction effect on birth weight. More generally, the QMFG test can detect genetic associations that may be missed by standard genome-wide association studies for quantitative traits.

Transgenerational cardiology: One way to a baby's heart is through the mother

Despite decades of progress, congenital heart disease remains a major cause of mortality and suffering in children and young adults. Prevention would be ideal, but formidable biological and technical hurdles face any intervention that seeks to target the main causes, genetic mutations in the embryo. Other factors, however, significantly modify the total risk in individuals who carry mutations. Investigation of these factors could lead to an alternative approach to prevention. To define the risk modifiers, our group has taken an "experimental epidemiologic" approach via inbred mouse strain crosses. The original intent was to map genes that modify an individual's risk of heart defects caused by an Nkx2-5 mutation. During the analysis of >2000 Nkx2-5(+/-) offspring from one cross we serendipitously discovered a maternal-age associated risk, which also exists in humans. Reciprocal ovarian transplants between young and old mothers indicate that the incidence of heart defects correlates with the age of the mother and not the oocyte, which implicates a maternal pathway as the basis of the risk. The quantitative risk varies between strain backgrounds, so maternal genetic polymorphisms determine the activity of a factor or factors in the pathway. Most strikingly, voluntary exercise by the mother mitigates the risk. Therefore, congenital heart disease can in principle be prevented by targeting a maternal pathway even if the embryo carries a causative mutation. Further mechanistic insight is necessary to develop an intervention that could be implemented on a broad scale, but the physiology of maternal-fetal interactions, aging, and exercise are notoriously complex and undefined. This suggests that an unbiased genetic approach would most efficiently lead to the relevant pathway. A genetic foundation would lay the groundwork for human studies and clinical trials.

A Three-Way Interaction among Maternal and Fetal Variants Contributing to Congenital Heart Defects

Congenital heart defects (CHDs) develop through a complex interplay between genetic variants, epigenetic modifications, and maternal environmental exposures. Genetic studies of CHDs have commonly tested single genetic variants for association with CHDs. Less attention has been given to complex gene-by-gene and gene-by-environment interactions. In this study, we applied a recently developed likelihood-ratio Mann-Whitney (LRMW) method to detect joint actions among maternal variants, fetal variants, and maternal environmental exposures, allowing for high-order statistical interactions. All subjects are participants from the National Birth Defect Prevention Study, including 623 mother-offspring pairs with CHD-affected pregnancies and 875 mother-offspring pairs with unaffected pregnancies. Each individual has 872 single nucleotide polymorphisms encoding for critical enzymes in the homocysteine, folate, and trans-sulfuration pathways. By using the LRMW method, three variants (fetal rs625879, maternal rs2169650, and maternal rs8177441) were identified with a joint association to CHD risk (nominal P-value = 1.13e-07). These three variants are located within genes BHMT2, GSTP1, and GPX3, respectively. Further examination indicated that maternal SNP rs2169650 may interact with both fetal SNP rs625879 and maternal SNP rs8177441. Our findings suggest that the risk of CHD may be influenced by both the intragenerational interaction within the maternal genome and the intergenerational interaction between maternal and fetal genomes.

The Quantitative-MFG Test: A Linear Mixed Effect Model to Detect Maternal-Offspring Gene Interactions

Maternal-offspring gene interactions, aka maternal-fetal genotype (MFG) incompatibilities, are neglected in complex diseases and quantitative trait studies. They are implicated in birth to adult onset diseases but there are limited ways to investigate their influence on quantitative traits. We present the quantitative-MFG (QMFG) test, a linear mixed model where maternal and offspring genotypes are fixed effects and residual correlations between family members are random effects. The QMFG handles families of any size, common or general scenarios of MFG incompatibility, and additional covariates. We develop likelihood ratio tests (LRTs) and rapid score tests and show they provide correct inference. In addition, the LRT's alternative model provides unbiased parameter estimates. We show that testing the association of SNPs by fitting a standard model, which only considers the offspring genotypes, has very low power or can lead to incorrect conclusions. We also show that offspring genetic effects are missed if the MFG modeling assumptions are too restrictive. With genome-wide association study data from the San Antonio Family Heart Study, we demonstrate that the QMFG score test is an effective and rapid screening tool. The QMFG test therefore has important potential to identify pathways of complex diseases for which the genetic etiology remains to be discovered.

GSTA1 gene variation associated with gestational hypertension and its involvement in pregnancy-related pathogenic conditions

OBJECTIVE(S)

Glutathione S-transferases (GSTs) are the main phase II enzymes involved in the cellular detoxification. Through phase I and phase II detoxification reactions, the cell is able to detoxify endogenous and exogenous toxic compounds. In this study, we focused our attention on the GSTA1*-69C/T gene polymorphism (rs3957357) in order to explore its involvement in the genetic predisposition to gestational hypertension (GH).

STUDY DESIGN

The case-control population consists of 195 subjects. The genotyping of the GSTA1*-69C/T was performed by using an RFLP-PCR technique. We calculated odds ratios (ORs), adjusted for the confounding variables, to estimate the association between GSTA1 and GH.

RESULTS

Significant allelic differences in GSTA1*-69C/T are present between GH women and pregnant women without cardiovascular complications (p<0.05). Specifically, we observed that the dominant genetic model best explains the observed genetic association, according to the Akaike information criterion and the Bayesian information criterion.

CONCLUSION(S)

Our study highlighted a significant association between the GSTA1 gene and the risk of GH in Italian patients. In particular, the -69C/T variant was significantly associated with disease risk. Since previous studies indicated that this GSTA1 polymorphism is associated with different pregnancy-related conditions, our finding supports the notion that GSTA1 may play a key role during pregnancy.

Developments in our understanding of the genetic basis of birth defects

Birth defects are a major cause of morbidity and mortality worldwide. There has been much progress in understanding the genetic basis of familial and syndromic forms of birth defects. However, the etiology of nonsydromic birth defects is not well-understood. Although there is still much work to be done, we have many of the tools needed to accomplish the task. Advances in next-generation sequencing have introduced a sea of possibilities, from disease-gene discovery to clinical screening and diagnosis. These advances have been fruitful in identifying a host of candidate disease genes, spanning the spectrum of birth defects. With the advent of CRISPR-Cas9 gene editing, researchers now have a precise tool for characterizing this genetic variation in model systems. Work in model organisms has also illustrated the importance of epigenetics in human development and birth defects etiology. Here we review past and current knowledge in birth defects genetics. We describe genotyping and sequencing methods for the detection and analysis of rare and common variants. We remark on the utility of model organisms and explore epigenetics in the context of structural malformation. We conclude by highlighting approaches that may provide insight into the complex genetics of birth defects.

The maternal-age-associated risk of congenital heart disease is modifiable

Maternal age is a risk factor for congenital heart disease even in the absence of any chromosomal abnormality in the newborn^1-7. Whether the basis of the risk resides with the mother or oocyte is unknown. The impact of maternal age on congenital heart disease can be modeled in mouse pups that harbor a mutation of the cardiac transcription factor gene Nkx2-5⁸. Here, reciprocal ovarian transplants between young and old mothers establish a maternal basis for the age-associated risk. A high-fat diet does not accelerate the effect of maternal aging, so hyperglycemia and obesity do not simply explain the mechanism. The age-associated risk varies with the mother's strain background, making it a quantitative genetic trait. Most remarkably, voluntary exercise, whether begun by mothers at a young age or later in life, can mitigate the risk when they are older. Thus, even when the offspring carry a causal mutation, an intervention aimed at the mother can meaningfully reduce their risk of congenital heart disease.

Placental genome and maternal-placental genetic interactions: a genome-wide and candidate gene association study of placental abruption

While available evidence supports the role of genetics in the pathogenesis of placental abruption (PA), PA-related placental genome variations and maternal-placental genetic interactions have not been investigated. Maternal blood and placental samples collected from participants in the Peruvian Abruptio Placentae Epidemiology study were genotyped using Illumina's Cardio-Metabochip platform. We examined 118,782 genome-wide SNPs and 333 SNPs in 32 candidate genes from mitochondrial biogenesis and oxidative phosphorylation pathways in placental DNA from 280 PA cases and 244 controls. We assessed maternal-placental interactions in the candidate gene SNPS and two imprinted regions (IGF2/H19 and C19MC). Univariate and penalized logistic regression models were fit to estimate odds ratios. We examined the combined effect of multiple SNPs on PA risk using weighted genetic risk scores (WGRS) with repeated ten-fold cross-validations. A multinomial model was used to investigate maternal-placental genetic interactions. In placental genome-wide and candidate gene analyses, no SNP was significant after false discovery rate correction. The top genome-wide association study (GWAS) hits were rs544201, rs1484464 (CTNNA2), rs4149570 (TNFRSF1A) and rs13055470 (ZNRF3) (p-values: 1.11e-05 to 3.54e-05). The top 200 SNPs of the GWAS overrepresented genes involved in cell cycle, growth and proliferation. The top candidate gene hits were rs16949118 (COX10) and rs7609948 (THRB) (p-values: 6.00e-03 and 8.19e-03). Participants in the highest quartile of WGRS based on cross-validations using SNPs selected from the GWAS and candidate gene analyses had a 8.40-fold (95% CI: 5.8-12.56) and a 4.46-fold (95% CI: 2.94-6.72) higher odds of PA compared to participants in the lowest quartile. We found maternal-placental genetic interactions on PA risk for two SNPs in PPARG (chr3:12313450 and chr3:12412978) and maternal imprinting effects for multiple SNPs in the C19MC and IGF2/H19 regions. Variations in the placental genome and interactions between maternal-placental genetic variations may contribute to PA risk. Larger studies may help advance our understanding of PA pathogenesis.

Entropy-based selection for maternal-fetal genotype incompatibility with application to preterm prelabor rupture of membranes

Background

Maternal-fetal genotype incompatibility (MFGI) is increasingly reported to influence human diseases, especially pregnancy-related complications. In practice, it is challenging to identify the ideal incompatibility model for analysis, since the true MFGI mechanism is generally unknown. The underlying MFGI mechanism for different genetic variants can vary, and to use a single incompatibility model for all circumstances would cause power loss in testing MFGI.

Results

In this article, we propose a practical 2-step procedure that incorporates a model selection strategy based on an entropy measurement to select the most appropriate MFGI model represented by data and test the significance of the MFGI effect using the chosen model within the generalized linear regression framework.

Conclusions

Our simulation studies show that the proposed two-step procedure controls the type I error rate and increase the testing power under various scenarios. In a real data application, our analysis reveals genes having an MFGI effect, which may not be detected with a non-model selection counterpart.