Publications

Predicting the functional significance of structural variants (SVs) associated with genetic diseases remains challenging. To test the hypothesis that SVs from people with congenital heart disease (CHD) disrupt developmental chromatin interactions, we developed CardioAkita, a machine-learning model that predicts how variants alter 3D chromatin structure. Analyzing previously genotyped de novo SVs ( dn SVs), we observed a positive association between CHD severity and CardioAkita scores across dozens of families. From whole-genome sequencing of three individuals with CHD we predicted disruptive dn SVs. Induced pluripotent stem cells engineered to harbor these variants confirmed CardioAkita's predictions of 3D chromatin changes, and further revealed aberrant expression of local genes including cardiac developmental genes, suggesting that chromatin reorganization plays a significant mechanistic role in the genetic etiology of CHD. Our findings highlight the potential for models of 3D chromatin organization to predict the pathogenicity and underlying mechanisms of SVs in human disease.

OBJECTIVE: Investigate the association between placental vascular abnormalities and regional brain volumes in congenital heart disease (CHD) fetuses with and without genetic abnormalities.

STUDY DESIGN: Secondary analysis of brain magnetic resonance imaging (MRI) and placental pathology data from 121 CHD fetuses enrolled in prospective neuroimaging studies at two centers.

RESULTS: Placental vascular abnormality was present in 46% of fetuses, and genetic abnormality was present in 19%, including 12% with both abnormalities. Fetuses with the combination of placental and genetic abnormalities had smaller brain volumes compared to fetuses without either abnormality for total brain, subcortical gray matter, brainstem, and cerebellum, with a significant interaction (P < 0.05) between placental and genetic abnormalities for intracranial and subcortical gray matter volumes.

CONCLUSION: Smaller brain volumes for CHD fetuses with placental and genetic abnormalities may suggest common genetic pathways affect placental, heart, and brain development, or that genetic abnormalities heighten vulnerability when placental changes occur.

BACKGROUND: Thrombotic events, including acute ischemic stroke, are more common in individuals with congenital heart disease (CHD). Whether common thrombophilia variants contribute to thrombotic risk in this population remains unclear. We evaluated whether prothrombin G20210A (F2 c.97G>A) and factor V Leiden (F5 c.1601G>A; p.Arg534Gln) are associated with thrombotic events in CHD.

METHODS: Participants in the Pediatric Cardiac Genomics Consortium with exome sequencing and electronic medical record data were identified. Individuals were stratified by prothrombin G20210A and factor V Leiden genotypes, ventricular physiology, and antithrombotic therapy. The primary outcome was the presence of International Classification of Diseases (ICD) or Phecodes (phenotype codes) for thrombotic events.

RESULTS: Among 4008 participants (median age, 11.4 [interquartile range, 5.1-17.9] years; 44.4% boys), thrombotic events occurred in 737 (18%), including 93 (13%) with acute ischemic stroke. Compared with the Genome Aggregation Database, the CHD cohort had a lower prevalence of heterozygous prothrombin G20210A and factor V Leiden variants. Variant prevalence did not differ between participants with and without thrombotic events. Single-ventricle CHD was associated with higher thrombosis frequency than biventricular CHD (35% versus 16%, P≤0.0001), without differences in variant prevalence.

CONCLUSIONS: In this multicenter CHD cohort, prothrombin G20210A and factor V Leiden were not significantly associated with thrombotic outcomes, supporting recommendations against routine screening. Given low variant prevalence, the study was powered to exclude only large associations. Reduced variant frequency suggests survivorship bias beginning in fetal life. Larger integrated clinical-genomic studies are needed to refine thrombotic risk stratification in CHD.

REGISTRATION: URL: https://www.clinicaltrials.gov; Unique Identifier: NCT03347214.

BACKGROUND: Congenital heart disease (CHD) affects about 1% of births and is linked to differences in thinking and learning. Understanding how birth, genetic, clinical, and environmental factors together explain cognitive variability can inform monitoring and care. This study builds a multivariate model predicting cognition across multiple domains in adolescents and young adults with CHD.

METHODS: We studied 89 adolescents and young adults (AYAs; mean age 16 years) with CHD who completed structural and diffusion MRI and fifteen neurocognitive tests across seven domains. Using an enhanced forward-inclusion and backward-elimination strategy with cross-validation, we built multivariate models incorporating biological, socioeconomic, clinical, genetic, and brain imaging features. Performance was evaluated using Pearson correlation (r) between observed and inferred scores, mean absolute error (MAE), and inverse inferability score (IIS).

RESULTS: Here we show that models infer scores with moderate accuracy (r = 0.245-0.648; MAE = 1.6-12.0 points; mean MAE = 6.3). Highest correlations include Digit Span (r = 0.65; p < 0.001), Verbal Comprehension Index (r = 0.594; p < 0.001), and Matrix Reasoning (r = 0.574; p < 0.001). Domain ranking by IIS shows the best (lowest) scores for general intelligence (0.0886), followed by working memory (0.7100), and a higher (worse) score for perceptual reasoning (1.9199).

CONCLUSIONS: A multivariate approach combining brain imaging with genetic, clinical, and environmental factors provides clinically meaningful inference of individual cognitive performance in AYAs with CHD. These findings suggest complementary roles of brain, genetic, and contextual factors in shaping cognitive variability and motivate validation in larger cohorts.

With improvement in medical and surgical care, the number of adults with congenital heart disease (CHD) is soaring. Adults with CHD commonly have impairments in brain health. However, significant gaps in knowledge remain regarding the relevant types and prevalence of neurologic and psychiatric risk and their associated risk factors. We sought to review current evidence, identify gaps in knowledge, and develop key next steps to improve scientific understanding and clinical care. Three working groups-Genetics and Brain Health, Characterizing Neuropsychological and Psychological Outcomes, and Neuropsychological and Psychosocial Interventions-were composed of multidisciplinary experts in relevant clinical and research domains, as well as adults with CHD. Each group identified 5 key knowledge gaps and associated next investigations needed to address those gaps. For Genetics and Brain Health, 5 key knowledge gaps were identified: lack of a standardized neuroimaging protocol for adults with CHD, need to understand neuroradiological-pathological-neuropsychological correlates, role of gene-environment interactions, what can be learned from brain health risk models from other groups, and how existing multimodal approaches influence risk and neuroresilience. Adults with CHD can benefit from routine assessment of brain health, as well as increased clinical and basic research into the underlying factors that contribute to risk and neuroresilience for neurologic and psychiatric sequalae. Multidisciplinary collaborative efforts that incorporate adults with CHD across the research cycle are essential for all key next steps.