High Comorbidity of Pediatric Cancers in Patients with Birth Defects: Insights from Whole Genome Sequencing Analysis of Copy Number Variations.

BACKGROUND: Patients with birth defects (BD) exhibit an elevated risk of cancer. We aimed to investigate the potential link between pediatric cancers and BDs, exploring the hypothesis of shared genetic defects contributing to the coexistence of these conditions. METHODS: This study included 1454 probands with BDs (704 females and 750 males), including 619 (42.3%) with and 845 (57.7%) without co-occurrence of pediatric onset cancers. Whole genome sequencing (WGS) was done at 30X coverage through the Kids First/Gabriella Miller X01 Program. RESULTS: 8211 CNV loci were called from the 1454 unrelated individuals. 191 CNV loci classified as pathogenic/likely pathogenic (P/LP) were identified in 309 (21.3%) patients, with 124 (40.1%) of these patients having pediatric onset cancers. The most common group of CNVs are pathogenic deletions covering the region ChrX:52,863,011-55,652,521, seen in 162 patients including 17 males. Large recurrent P/LP duplications >5MB were detected in 33 patients. CONCLUSIONS: This study revealed that P/LP CNVs were common in a large cohort of BD patients with high rate of pediatric cancers. We present a comprehensive spectrum of P/LP CNVs in patients with BDs and various cancers. Notably, deletions involving E2F target genes and genes implicated in mitotic spindle assembly and G2/M checkpoint were identified, potentially disrupting cell-cycle progression and providing mechanistic insights into the concurrent occurrence of BDs and cancers.

The Circassians and the Chechens in Jordan: results of a decade of epidemiological and genetic studies.

Circassians and Chechens in Jordan, both with Caucasian ancestry, are genetically isolated due to high rate of endogamous marriages. Recent interest in these populations has led to studies on their genetic similarities, differences, and epidemiological differences in various diseases. Research has explored their predisposition to conditions like diabetes, hypertension, and cancer. Moreover, pharmacogenetic (PGx) studies have also investigated medication response variations within these populations, and forensic studies have further contributed to understanding these populations. In this review article, we first discuss the background of these minority groups. We then show the results of a principle component analysis (PCA) to investigate the genetic relationships between Circassian and Chechen populations living in Jordan. We here present a summary of the findings from the 10 years of research conducted on them. The review article provides a comprehensive summary of research findings that are truly valuable for understanding the unique genetic characteristics, diseases' prevalence, and medication responses among Circassians and Chechens living in Jordan. We believe that gaining deeper comprehension of the root causes of various diseases and developing effective treatment methods that benefit the society as a whole are imperative to engaging a wide range of ethnic groups in genetic research.

Genetic analysis for type 1 diabetes genes in juvenile dermatomyositis unveils genetic disease overlap.

OBJECTIVES: JDM is a serious autoimmune and complex genetic disease. Another autoimmune genetic disease, type 1 diabetes (T1D), has been observed for significantly increased prevalence in families with JDM, while increased JDM risk has also been observed in T1D cases. This study aimed to study whether these two autoimmune diseases, JDM and T1D, share common genetic susceptibility. METHODS: From 169 JDM families, 121 unrelated cases with European ancestry (EA) were identified by genome-wide genotyping, principal component analysis and identical-by-descent (IBD) analysis. T1D genetic risk score (GRS) were calculated in these cases and were compared with 361 EA T1D cases and 1943 non-diabetes EA controls. A total of 113 cases of the 121 unrelated European cases were sequenced by whole exome sequencing. RESULTS: We observed increased T1D GRS in JDM cases (P = 9.42E-05). Using whole exome sequencing, we uncovered the T1D genes, phospholipase B1, cystic fibrosis transmembrane conductance regulator, tyrosine hydroxylase, CD6 molecule, perforin 1 and dynein axonemal heavy chain 2, potentially associated with JDM by the burden test of rare functional coding variants. CONCLUSION: Novel mechanisms of JDM related to these T1D genes are suggested by this study, which may imply novel therapeutic targets for JDM and warrant further study.

Application of deep learning algorithm on whole genome sequencing data uncovers structural variants associated with multiple mental disorders in African American patients.

Mental disorders present a global health concern, while the diagnosis of mental disorders can be challenging. The diagnosis is even harder for patients who have more than one type of mental disorder, especially for young toddlers who are not able to complete questionnaires or standardized rating scales for diagnosis. In the past decade, multiple genomic association signals have been reported for mental disorders, some of which present attractive drug targets. Concurrently, machine learning algorithms, especially deep learning algorithms, have been successful in the diagnosis and/or labeling of complex diseases, such as attention deficit hyperactivity disorder (ADHD) or cancer. In this study, we focused on eight common mental disorders, including ADHD, depression, anxiety, autism, intellectual disabilities, speech/language disorder, delays in developments, and oppositional defiant disorder in the ethnic minority of African Americans. Blood-derived whole genome sequencing data from 4179 individuals were generated, including 1384 patients with the diagnosis of at least one mental disorder. The burden of genomic variants in coding/non-coding regions was applied as feature vectors in the deep learning algorithm. Our model showed ~65% accuracy in differentiating patients from controls. Ability to label patients with multiple disorders was similarly successful, with a hamming loss score less than 0.3, while exact diagnostic matches are around 10%. Genes in genomic regions with the highest weights showed enrichment of biological pathways involved in immune responses, antigen/nucleic acid binding, chemokine signaling pathway, and G-protein receptor activities. A noticeable fact is that variants in non-coding regions (e.g., ncRNA, intronic, and intergenic) performed equally well as variants in coding regions; however, unlike coding region variants, variants in non-coding regions do not express genomic hotspots whereas they carry much more narrow standard deviations, indicating they probably serve as alternative markers.