A genome-wide association study of obstructive heart defects among participants in the National Birth Defects Prevention Study

Bioinformatics in Neonatal/Pediatric Medicine-A Literature Review

Bioinformatics is a scientific field that uses computer technology to gather, store, analyze, and share biological data and information. DNA sequences of genes or entire genomes, protein amino acid sequences, nucleic acid, and protein-nucleic acid complex structures are examples of traditional bioinformatics data. Moreover, proteomics, the distribution of proteins in cells, interactomics, the patterns of interactions between proteins and nucleic acids, and metabolomics, the types and patterns of small-molecule transformations by the biochemical pathways in cells, are further data streams. Currently, the objectives of bioinformatics are integrative, focusing on how various data combinations might be utilized to comprehend organisms and diseases. Bioinformatic techniques have become popular as novel instruments for examining the fundamental mechanisms behind neonatal diseases. In the first few weeks of newborn life, these methods can be utilized in conjunction with clinical data to identify the most vulnerable neonates and to gain a better understanding of certain mortalities, including respiratory distress, bronchopulmonary dysplasia, sepsis, or inborn errors of metabolism. In the current study, we performed a literature review to summarize the current application of bioinformatics in neonatal medicine. Our aim was to provide evidence that could supply novel insights into the underlying mechanism of neonatal pathophysiology and could be used as an early diagnostic tool in neonatal care.

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.

Sex-Specific Whole-Transcriptome Analysis in the Cerebral Cortex of FAE Offspring

Fetal alcohol spectrum disorders (FASDs) are associated with systemic inflammation and neurodevelopmental abnormalities. Several candidate genes were found to be associated with fetal alcohol exposure (FAE)-associated behaviors, but a sex-specific complete transcriptomic analysis was not performed at the adult stage. Recent studies have shown that they are regulated at the developmental stage. However, the sex-specific role of RNA in FAE offspring brain development and function has not been studied yet. Here, we carried out the first systematic RNA profiling by utilizing a high-throughput transcriptomic (RNA-seq) approach in response to FAE in the brain cortex of male and female offspring at adulthood (P60). Our RNA-seq data analysis suggests that the changes in RNA expression in response to FAE are marked sex-specific. We show that the genes Muc3a, Pttg1, Rec8, Clcnka, Capn11, and pnp2 exhibit significantly higher expression in the male offspring than in the female offspring at P60. FAE female mouse brain sequencing data also show an increased expression of Eno1, Tpm3, and Pcdhb2 compared to male offspring. We performed a pathway analysis using a commercial software package (Ingenuity Pathway Analysis). We found that the sex-specific top regulator genes (Rictor, Gaba, Fmri, Mlxipl) are highly associated with eIF2 (translation initiation), synaptogenesis (the formation of synapses between neurons in the nervous system), sirtuin (metabolic regulation), and estrogen receptor (involved in obesity, aging, and cancer) signaling. Taken together, our transcriptomic results demonstrate that FAE differentially alters RNA expression in the adult brain in a sex-specific manner.

Considering the Genetic Architecture of Hypoplastic Left Heart Syndrome

Hypoplastic left heart syndrome (HLHS) is among the most severe cardiovascular malformations and understanding its causes is crucial to making progress in prevention and treatment. Genetic analysis is a broadly useful tool for dissecting complex causal mechanisms and it is playing a significant role in HLHS research. However, unlike classical Mendelian disorders where a relatively small number of genes are largely determinative of the occurrence and severity of the disease, the picture in HLHS is complex. De novo single-gene and copy number variant (CNV) disorders make an important contribution, but there is emerging evidence for causal contributions from lower penetrance and common variation. Integrating this emerging knowledge into clinical diagnostics and translating the findings into effective prevention and treatment remain challenges for the future.