Testing Allele Transmission of an SNP Set Using a Family-Based Generalized Genetic Random Field Method

Family-based association studies are commonly used in genetic research because they can be robust to population stratification (PS). Recent advances in high-throughput genotyping technologies have produced a massive amount of genomic data in family-based studies. However, current family-based association tests are mainly focused on evaluating individual variants one at a time. In this article, we introduce a family-based generalized genetic random field (FB-GGRF) method to test the joint association between a set of autosomal SNPs (i.e., single-nucleotide polymorphisms) and disease phenotypes. The proposed method is a natural extension of a recently developed GGRF method for population-based case-control studies. It models offspring genotypes conditional on parental genotypes, and, thus, is robust to PS. Through simulations, we presented that under various disease scenarios the FB-GGRF has improved power over a commonly used family-based sequence kernel association test (FB-SKAT). Further, similar to GGRF, the proposed FB-GGRF method is asymptotically well-behaved, and does not require empirical adjustment of the type I error rates. We illustrate the proposed method using a study of congenital heart defects with family trios from the National Birth Defects Prevention Study (NBDPS).

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.

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

Nonsyndromic congenital heart defects (CHDs) develop during embryogenesis as a result of a complex interplay between environmental exposures, genetics, and epigenetic causes. Genetic factors associated with CHDs may be attributed to either independent effects of maternal or fetal genes, or the intergenerational interactions between maternal and fetal genes. Detecting gene-by-gene interactions underlying complex diseases is a major challenge in genetic research. Detecting maternal-fetal genotype (MFG) interactions and differentiating them from the maternal/fetal main effects has presented additional statistical challenges due to correlations between maternal and fetal genomes. Traditionally, genetic variants are tested separately for maternal/fetal main effects and MFG interactions on a single-locus basis. We conducted a haplotype-based analysis with a penalized logistic regression framework to dissect the genetic effect associated with the development of nonsyndromic conotruncal heart defects (CTD). Our method allows simultaneous model selection and effect estimation, providing a unified framework to differentiate maternal/fetal main effect from the MFG interaction effect. In addition, the method is able to test multiple highly linked SNPs simultaneously with a configuration of haplotypes, which reduces the data dimensionality and the burden of multiple testing. By analyzing a dataset from the National Birth Defects Prevention Study (NBDPS), we identified seven genes (GSTA1, SOD2, MTRR, AHCYL2, GCLC, GSTM3, and RFC1) associated with the development of CTDs. Our findings suggest that MFG interactions between haplotypes in three of seven genes, GCLC, GSTM3, and RFC1, are associated with nonsyndromic conotruncal heart defects.

Conotruncal heart defects and common variants in maternal and fetal genes in folate, homocysteine, and transsulfuration pathways

BACKGROUND

We investigated the association between conotruncal heart defects (CTDs) and maternal and fetal single nucleotide polymorphisms (SNPs) in 60 genes in the folate, homocysteine, and transsulfuration pathways. We also investigated whether periconceptional maternal folic acid supplementation modified associations between CTDs and SNPs

METHODS

Participants were enrolled in the National Birth Defects Prevention Study between 1997 and 2008. DNA samples from 616 case-parental triads affected by CTDs and 1645 control-parental triads were genotyped using an Illumina® Golden Gate custom SNP panel. A hybrid design analysis, optimizing data from case and control trios, was used to identify maternal and fetal SNPs associated with CTDs

RESULTS

Among 921 SNPs, 17 maternal and 17 fetal SNPs had a Bayesian false-discovery probability of <0.8. Ten of the 17 maternal SNPs and 2 of the 17 fetal SNPs were found within the glutamate-cysteine ligase, catalytic subunit (GCLC) gene. Fetal SNPs with the lowest Bayesian false-discovery probability (rs2612101, rs2847607, rs2847326, rs2847324) were found within the thymidylate synthetase (TYMS) gene. Additional analyses indicated that the risk of CTDs associated with candidate SNPs was modified by periconceptional folic acid supplementation. Nineteen maternal and nine fetal SNPs had a Bayesian false-discovery probability <0.8 for gene-by-environment (G × E) interactions with maternal folic acid supplementation.

CONCLUSION

These results support previous studies suggesting that maternal and fetal SNPs within folate, homocysteine, and transsulfuration pathways are associated with CTD risk. Maternal use of supplements containing folic acid may modify the impact of SNPs on the developing heart.