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PNAS Papers Tie De Novo Mutations to Sudden Unexplained Death in Childhood, Genetic Pathology of Abdominal Aortic Aneurysm

Using next-generation sequencing data, a team led by researchers from New York University and the University of North Carolina at Chapel Hill have uncovered mutations associated with sudden unexplained death in childhood (SUDC). Annually, about 400 children in the US aged 1 year and older die suddenly from unexplained causes. To investigate genetic risk factors for SUDC, the researchers studied whole-exome sequence data from 124 trios comprising a deceased child and their parents. As reported in this week's Proceedings of the National Academy of Sciences, they found nonsynonymous mutations, mostly de novo, were highly enriched in genes associated with cardiac and seizure disorders relative to controls and contributed to 9 percent of deaths. The team also discovered significant over-transmission of loss-of-function or pathogenic missense variants in cardiac and seizure disorder genes. The findings, the investigators write, indicate that deleterious de novo mutations are "significant genetic risk factors for childhood sudden unexplained death, and that their identification may lead to medical intervention that ultimately saves lives."

Combining whole-genome sequencing data with gene regulatory relations in disease-relevant cell types, a Stanford University-led group has identified key players in the pathogenesis of abdominal aortic aneurysm (AAA). Despite the prevalence of AAA, its genetic pathology is poorly understood. Presenting their work in the Proceedings of the National Academy of Sciences, the researchers analyzed whole-genome sequencing (WGS) data in cell types involved in AAA, finding changes in predicted chromatin accessibility between AAA patients and controls. This information was then integrated with disease-associated variants in regulatory elements and gene bodies, providing insights into the etiology and pathogenetic mechanisms of AAA. The study, the authors write, "provides an avenue to decipher underlying mechanisms of disease by combining WGS data with gene regulatory relationships in relevant cell types."