Gene-gene interaction among cell adhesion genes and risk of nonsyndromic cleft lip with or without cleft palate in Chinese case-parent trios

Harnessing the Power of Artificial Intelligence in Cleft Lip and Palate: An In-Depth Analysis from Diagnosis to Treatment, a Comprehensive Review

Cleft lip and palate (CLP) is the most common craniofacial malformation, with a range of physical, psychological, and aesthetic consequences. In this comprehensive review, our main objective is to thoroughly examine the relationship between CLP anomalies and the use of artificial intelligence (AI) in children. Additionally, we aim to explore how the integration of AI technology can bring about significant advancements in the fields of diagnosis, treatment methods, and predictive outcomes. By analyzing the existing evidence, we will highlight state-of-the-art algorithms and predictive AI models that play a crucial role in achieving precise diagnosis, susceptibility assessment, and treatment planning for children with CLP anomalies. Our focus will specifically be on the efficacy of alveolar bone graft and orthodontic interventions. The findings of this review showed that deep learning (DL) models revolutionize the diagnostic process, predict susceptibility to CLP, and enhance alveolar bone grafts and orthodontic treatment. DL models surpass human capabilities in terms of precision, and AI algorithms applied to large datasets can uncover the intricate genetic and environmental factors contributing to CLP. Additionally, Machine learning aids in preoperative planning for alveolar bone grafts and provides personalized treatment plans in orthodontic treatment. In conclusion, these advancements inspire optimism for a future where AI seamlessly integrates with CLP management, augmenting its analytical capabilities.

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.

Identification of a Novel Variant of PDGFC Associated with Nonsyndromic Cleft Lip and Palate in a Chinese Family

Nonsyndromic cleft lip with or without cleft palate (NSCL/P) accounts for 70% of the total number of patients with cleft lip with or without cleft palate (CL/P) and is the most common type of congenital deformity of the craniomaxillofacial region. In this study, whole exome sequencing (WES) and Sanger sequencing were performed on affected members of a Han Chinese family, and a missense variant in the platelet-derived growth factor C (PDGFC) gene (NM_016205: c.G93T: p.Q31H) was identified to be associated with NSCL/P. Bioinformatic studies demonstrated that the amino acid corresponding to this variation is highly conserved in many mammals and leads to a glutamine-to-histidine substitution in an evolutionarily conserved DNA-binding domain. It was found that the expression of PDGFC was significantly decreased in the dental pulp stem cells (DPSCs) of NSCL/P cases, compared to the controls, and that the variant (NM_016205: c.G93T) reduced the expression of PDGFC. In addition, the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis showed that Pdgfc deficiency disrupted NSCL/P-related signaling pathways such as the MAPK signaling pathway and cell adhesion molecules. In conclusion, our study identified a missense variant (NM_016205: c.G93T) in exon 1 of PDGFC potentially associated with susceptibility to NSCL/P.

Knowledge structure and emerging trends in the application of deep learning in genetics research: A bibliometric analysis [2000–2021]

Introduction: Deep learning technology has been widely used in genetic research because of its characteristics of computability, statistical analysis, and predictability. Herein, we aimed to summarize standardized knowledge and potentially innovative approaches for deep learning applications of genetics by evaluating publications to encourage more research.

Methods: The Science Citation Index Expanded TM (SCIE) database was searched for deep learning applications for genomics-related publications. Original articles and reviews were considered. In this study, we derived a clustered network from 69,806 references that were cited by the 1,754 related manuscripts identified. We used CiteSpace and VOSviewer to identify countries, institutions, journals, co-cited references, keywords, subject evolution, path, current characteristics, and emerging topics.

Results: We assessed the rapidly increasing publications concerned about deep learning applications of genomics approaches and identified 1,754 articles that published reports focusing on this subject. Among these, a total of 101 countries and 2,487 institutes contributed publications, The United States of America had the most publications (728/1754) and the highest h-index, and the US has been in close collaborations with China and Germany. The reference clusters of SCI articles were clustered into seven categories: deep learning, logic regression, variant prioritization, random forests, scRNA-seq (single-cell RNA-seq), genomic regulation, and recombination. The keywords representing the research frontiers by year were prediction (2016–2021), sequence (2017–2021), mutation (2017–2021), and cancer (2019–2021).

Conclusion: Here, we summarized the current literature related to the status of deep learning for genetics applications and analyzed the current research characteristics and future trajectories in this field. This work aims to provide resources for possible further intensive exploration and encourages more researchers to overcome the research of deep learning applications in genetics.

Current Applications of Artificial Intelligence in Cleft Care: A Scoping Review

Objective: This scoping review aims to identify the various areas and current status of the application of artificial intelligence (AI) for aiding individuals with cleft lip and/or palate.

Introduction: Cleft lip and/or palate contributes significantly toward the global burden on the healthcare system. Artificial intelligence is a technology that can help individuals with cleft lip and/or palate, especially those in areas with limited access to receive adequate care.

Inclusion Criteria: Studies that used artificial intelligence to aid the diagnosis, treatment, or its planning in individuals with cleft lip and/or palate were included.

Methodology: A search of the Pubmed, Embase, and IEEE Xplore databases was conducted using search terms artificial intelligence and cleft lip and/or palate. Gray literature was searched using Google Scholar. The study was conducted according to the PRISMA- ScR guidelines.

Results: The initial search identified 458 results, which were screened based on title and abstracts. After the screening, removal of duplicates, and a full-text reading of selected articles, 26 publications were included. They explored the use of AI in cleft lip and/or palate to aid in decisions regarding diagnosis, treatment, especially speech therapy, and prediction.

Conclusion: There is active interest and immense potential for the use of artificial intelligence in cleft lip and/or palate. Most studies currently focus on speech in cleft palate. Multi-center studies that include different populations, with collaboration amongst academicians and researchers, can further develop the technology.

Gene-by-gene interactions associated with the risk of conotruncal heart defects

BACKGROUND

The development of conotruncal heart defects (CTDs) involves a complex relationship among genetic variants and maternal lifestyle factors. In this article, we focused on the interactions between 13 candidate genes within folate, homocysteine, and transsulfuration pathways for potential association with CTD risk.

METHODS

Targeted sequencing was used for 328 case-parental triads enrolled in the National Birth Defects Prevention Study (NBDPS). To evaluate the interaction of two genes, we applied a conditional logistic regression model for all possible SNP pairs within two respective genes by contrasting the affected infants with their pseudo-controls. The findings were replicated in an independent sample of 86 NBDPS case-parental triads genotyped by DNA microarrays. The results of two studies were further integrated by a fixed-effect meta-analysis.

RESULTS

One SNP pair (i.e., rs4764267 and rs6556883) located in gene MGST1 and GLRX, respectively, was found to be associated with CTD risk after multiple testing adjustment using simpleM, a modified Bonferroni correction approach (nominal p-value of 4.62e-06; adjusted p-value of .04). Another SNP pair (i.e., rs11892646 and rs56219526) located in gene DNMT3A and MTRR, respectively, achieved marginal significance after multiple testing adjustment (adjusted p-value of .06).

CONCLUSION

Further studies with larger sample sizes are needed to confirm and elucidate these potential interactions.

Gene-gene interaction among cell adhesion genes and risk of nonsyndromic cleft lip with or without cleft palate in Chinese case-parent trios

Background

Nonsyndromic cleft lip with or without cleft palate (NSCL/P) is a common birth defect with complex etiology. One strategy for studying the genetic risk factors of NSCL/P is to consider gene–gene interaction (G × G) among gene pathways having a role in craniofacial development. The present study aimed to investigate the G × G among cell adhesion gene pathway.

Methods

We carried out an interaction analysis of eight genes involved in cell adherens junctions among 806 NSCL/P Chinese case‐parent trios originally recruited for a genome‐wide association study (GWAS). Regression‐based approach was used to test for two‐way G × G interaction, while machine learning algorithm was run for exploring both two‐way and multi‐way interaction that may affect the risk of NSCL/P.

Results

A two‐way ACTN1 × CTNNB1 interaction reached the adjusted significance level. The single nucleotide polymorphisms pair composed of rs17252114 (CTNNB1) and rs1274944 (ACTN1) yielded a p value of .0002, and this interaction was also supported by the logic regression algorithm. Higher order interactions involving ACTN1, CTNNB1, and CDH1 were picked out by logic regression, suggesting a potential role in NSCL/P risk.

Conclusion

This study suggests for the first time evidence of both two‐way and multi‐way G × G interactions among cell adhesion genes contributing to the NSCL/P risk.