Noncoding variants and sulcal patterns in congenital heart disease: Machine learning to predict functional impact

IF 4.6 2区综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES

iScience Pub Date : 2024-12-28 DOI:10.1016/j.isci.2024.111707

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

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Abstract

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.

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来源期刊

iScience Multidisciplinary-Multidisciplinary

CiteScore

7.20

自引率

1.70%

发文量

1972

审稿时长

6 weeks

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