While genome sequencing has transformed medicine by elucidating the genetic underpinnings of both rare and common complex disorders, its utility to predict clinical outcomes remains understudied. Here, we used artificial intelligence (AI) technologies to explore the predictive value of genome sequencing in forecasting clinical outcomes following surgery for congenital heart defects (CHD). We report results for a cohort of 2,253 CHD patients from the Pediatric Cardiac Genomics Consortium with a broad range of complex heart defects, pre- and post-operative clinical variables and exome sequencing. Damaging genotypes in chromatin-modifying and cilia-related genes were associated with an elevated risk of adverse post-operative outcomes, including mortality, cardiac arrest and prolonged mechanical ventilation. The impact of damaging genotypes was further amplified in the context of specific CHD phenotypes, surgical complexity and extra-cardiac anomalies. The absence of a damaging genotype in chromatin-modifying and cilia-related genes was also informative, reducing the risk for adverse postoperative outcomes. Thus, genome sequencing enriches the ability to forecast outcomes following congenital cardiac surgery.

The authors declare the following competing interests: M.Y. -- GEM commercialization through Fabric Genomics, Inc; E.F. is an employee of Fabric Genomics.

The Pediatric Cardiac Genomics Consortium (PCGC) program is funded by the National Heart, Lung, and Blood Institute, National Institutes of Health, U.S. Department of Health and Human Services through grants UM1HL128711, UM1HL098162, UM1HL098147, UM1HL098123, UM1HL128761, U01-HL098153, U01-HL098163, and U01HL131003.

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The details of the IRB/oversight body that provided approval or exemption for the research described are given below:

Approval for human subjects research was obtained by the institutional review boards of participating centers, including Boston's Children's Hospital, Brigham and Women's Hospital, Great Ormond Street Hospital, Children's Hospital of Los Angeles, Children's Hospital of Philadelphia, Columbia University Medical Center, Icahn School of Medicine at Mount Sinai, Rochester School of Medicine and Dentistry, Steven and Alexandra Cohen Children's Medical Center of New York, Lucile Packard Children's Hospital Stanford, University of California-San Francisco, University of Utah, and Yale School of Medicine.

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Genetic and phenotypic data used in the paper are provided in the supplementary tables. Exome sequencing data have been deposited in the database of Genotypes and Phenotypes (dbGaP) under accession numbers phs000571.v1.p1, phs000571.v2.p1 and phs000571.v3.p2.