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Additional file 2: Figure S2. Distribution of methylation by the genotypes of mQTL SNPs in Table 1.

Additional file 3: Figure S3. Sensitivity analysis by subgroup analysis within NY fetal samples, NY adult samples and TX samples.

Additional file 5: Figure S5. Two-sample MR for causal effect from mQTL to CHD through CpG.

Additional file 6: Figure S6. Sample QC. A. Median intensities for methylated channels vs unmethylated channels. No bad quality sample was identified. B. Distribution of methylation beta values across samples. One sample (internal ID: NY07) showed abnormal distribution. C. Principal components of Epigenomic profiles. Red color: fetal heart samples; Green color: adult heart samples; Black color: ages unknown. One sample (NY07) was removed from the analysis.

Additional file 1: Figure S1 . Distribution of modeling fitting statistics evaluating genetic-epigenetic association. Left: Volcano plot of coefficient estimates. Right: model goodness-of-fit R2.

Additional file 4: Figure S4. Distribution of posterior probabilities (PP0 – PP4) from colocalization analysis with two GWAS phases and five eQTL heart tissues.

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).

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.