Noncoding variants and sulcal patterns in congenital heart disease: Machine learning to predict functional impact
Enrique Mondragon-Estrada,
Jane W. Newburger,
Steven R. DePalma,
Martina Brueckner,
John Cleveland,
Wendy K. Chung,
Bruce D. Gelb,
Elizabeth Goldmuntz,
Donald J. Hagler, Jr.,
Hao Huang,
Patrick McQuillen,
Thomas A. Miller,
Ashok Panigrahy,
George A. Porter, Jr.,
Amy E. Roberts,
Caitlin K. Rollins,
Mark W. Russell,
Martin Tristani-Firouzi,
P. Ellen Grant,
Kiho Im,
Sarah U. Morton
Affiliations
Enrique Mondragon-Estrada
Division of Newborn Medicine, Department of Pediatrics, Boston Children’s Hospital, Boston, MA, USA; Fetal Neonatal Neuroimaging and Developmental Science Center, Boston Children’s Hospital, Boston, MA, USA
Jane W. Newburger
Department of Pediatrics, Harvard Medical School, Boston, MA, USA; Department of Cardiology, Boston Children’s Hospital, Boston, MA, USA
Steven R. DePalma
Department of Genetics, Harvard Medical School, Boston, MA, USA
Martina Brueckner
Departments of Genetics and Pediatrics, Yale University School of Medicine, New Haven, CT, USA
John Cleveland
Departments of Surgery and Pediatrics, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
Wendy K. Chung
Department of Pediatrics, Harvard Medical School, Boston, MA, USA; Department of Pediatrics, Boston Children’s Hospital, Boston, MA, USA
Bruce D. Gelb
Mindich Child Health and Development Institute and Department of Pediatrics, Icahn School of Medicine at Mount Sinai, New York, NY, USA
Elizabeth Goldmuntz
Division of Cardiology, Children’s Hospital of Philadelphia, Department of Pediatrics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
Donald J. Hagler, Jr.
Center for Multimodal Imaging and Genetics, University of California San Diego, La Jolla, CA, USA; Department of Radiology, School of Medicine, University of California San Diego, La Jolla, CA, USA
Hao Huang
Department of Radiology, Children’s Hospital of Philadelphia, University of Pennsylvania, Philadelphia, PA, USA
Patrick McQuillen
Departments of Pediatrics and Neurology, University of California, San Francisco, San Francisco, CA, USA
Thomas A. Miller
Department of Pediatrics, Primary Children’s Hospital, University of Utah, Salt Lake City, UT, USA; Division of Pediatric Cardiology, Maine Medical Center, Portland, ME, USA
Ashok Panigrahy
Department of Pediatric Radiology, Children’s Hospital of Pittsburgh, University of Pittsburgh Medical Center, Pittsburgh, PA, USA
George A. Porter, Jr.
Department of Pediatrics, University of Rochester Medical Center, Rochester, NY, USA
Amy E. Roberts
Department of Pediatrics, Harvard Medical School, Boston, MA, USA; Department of Cardiology, Boston Children’s Hospital, Boston, MA, USA; Division of Genetics and Genomics, Department of Pediatrics, Boston Children’s Hospital, Boston, MA, USA
Caitlin K. Rollins
Department of Neurology, Boston Children’s Hospital, Boston, MA, USA; Department of Neurology, Harvard Medical School, Boston, MA, USA
Mark W. Russell
Department of Pediatrics, C.S. Mott Children’s Hospital, University of Michigan, Ann Arbor, MI, USA
Martin Tristani-Firouzi
Division of Pediatric Cardiology, University of Utah School of Medicine, Salt Lake City, UT, USA
P. Ellen Grant
Division of Newborn Medicine, Department of Pediatrics, Boston Children’s Hospital, Boston, MA, USA; Fetal Neonatal Neuroimaging and Developmental Science Center, Boston Children’s Hospital, Boston, MA, USA; Department of Pediatrics, Harvard Medical School, Boston, MA, USA; Department of Radiology, Boston Children’s Hospital, Boston, MA, USA
Kiho Im
Division of Newborn Medicine, Department of Pediatrics, Boston Children’s Hospital, Boston, MA, USA; Fetal Neonatal Neuroimaging and Developmental Science Center, Boston Children’s Hospital, Boston, MA, USA; Department of Pediatrics, Harvard Medical School, Boston, MA, USA; Corresponding author
Sarah U. Morton
Division of Newborn Medicine, Department of Pediatrics, Boston Children’s Hospital, Boston, MA, USA; Fetal Neonatal Neuroimaging and Developmental Science Center, Boston Children’s Hospital, Boston, MA, USA; Department of Pediatrics, Harvard Medical School, Boston, MA, USA; Corresponding author
Summary: Neurodevelopmental impairments associated with congenital heart disease (CHD) may arise from perturbations in brain developmental pathways, including the formation of sulcal patterns. While genetic factors contribute to sulcal features, the association of noncoding de novo variants (ncDNVs) with sulcal patterns in people with CHD remains poorly understood. Leveraging deep learning models, we examined the predicted impact of ncDNVs on gene regulatory signals. Predicted impact was compared between participants with CHD and a jointly called cohort without CHD. We then assessed the relationship of the predicted impact of ncDNVs with their sulcal folding patterns. ncDNVs predicted to increase H3K9me2 modification were associated with larger disruptions in right parietal sulcal patterns in the CHD cohort. Genes predicted to be regulated by these ncDNVs were enriched for functions related to neuronal development. This highlights the potential of deep learning models to generate hypotheses about the role of noncoding variants in brain development.
