2023 ACMG Annual Clinical Genetics Meeting Digital Edition: Poster Gallery-Single vs. Dual Disease Causing Variant Load in a Pediatric Cohort with Congenital Anomalies and Cancer

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This study aimed to uncover disease-causing genetic variants in children with congenital anomalies and cancer using whole genome sequencing (WGS). The researchers selected 1464 children from a pediatric biobank, who were molecularly undiagnosed and had major congenital malformations and childhood-onset cancer. They hypothesized that a single disease-causing variant would explain their complex disease presentations. <br /><br />To identify disease-causing variants among the millions of called variants, the researchers developed an artificial intelligence-based variant annotation and prioritization algorithm. This algorithm ranked candidate disease-causing single nucleotide variants (SNVs) based on human phenotype ontology (HPO) terms and other databases. Three other algorithms were used for comparison. <br /><br />The study enrolled a total of 1464 patients with cancer and/or birth defects, of which 832 had both diseases based on the International Classification of Diseases 9 (ICD9) codes. The researchers used their variant annotation and prioritization algorithm (GDCross) in combination with WGS to provide molecular diagnoses for children with congenital anomalies and/or neoplasms. <br /><br />The results showed that some patients had multiple genetic diagnoses, with certain genes being associated with specific organ systems. The researchers demonstrated that structured clinical ontologies can help assess the overlap between different Mendelian diseases in the same patient. WGS was found to be a powerful approach for unraveling the molecular underpinnings of severe and complex phenotypes, such as congenital malformations and cancer. In some cases, a single genetic defect could explain seemingly unrelated phenotypes.<br /><br />Overall, this study highlighted the potential of WGS and artificial intelligence-based algorithms in diagnosing and understanding the genetic basis of complex diseases in children with congenital anomalies and cancer.

Asset Subtitle

Presenting Author - Amir Hossein Saeidian, PhD; Co-Author - Deborah J. Watson, PhD; Co-Author - Xiang Wang, PhD; Co-Author - Margaret Harr, MS LCGC; Co-Author - Shannon Terek, MS, CGC; Co-Author - Michael March, PhD; Co-Author - Haijun Qiu, PhD; Co-Author - Isabella Barcelos, MD; Co-Author - Patrick MA. Sleiman, PhD; Co-Author - Joseph Glessner, PhD; Co-Author - Hakon Hakonarson, MD, PhD;

Meta Tag

Cancer Syndromes

Genome sequencing

Genotype-Phenotype Correlations

Identification of Disease Genes

Phenotypic delineation of disorders

Co-Author Deborah J. Watson, PhD

Co-Author Xiang Wang, PhD

Co-Author Margaret Harr, MS LCGC

Co-Author Shannon Terek, MS, CGC

Co-Author Michael March, PhD

Co-Author Haijun Qiu, PhD

Co-Author Isabella Barcelos, MD

Co-Author Patrick MA. Sleiman, PhD

Co-Author Joseph Glessner, PhD

Co-Author Hakon Hakonarson, MD, PhD

Presenting Author Amir Hossein Saeidian, PhD

Keywords

disease-causing genetic variants

children

congenital anomalies

cancer

whole genome sequencing

artificial intelligence

variant annotation

single nucleotide variants

Mendelian diseases

genetic basis