Large-scale data-driven analysis to understand the genetics of Congenital Heart Disease

Congenital Heart Disease (CHD) delineates a large group of structural defects, which can occur due to perturbations at some stage in the cardiac embryogenesis process. With a global incidence ranging from 7 to 9 cases per 1000 live births, CHD accounts for a significant fraction of new-borns deaths worldwide. Different studies have identified genetics as an essential factor underlying CHD, along with environmental factors. The technological advances within the last years have helped improve CHD diagnosis and understand its genetic causes. Nevertheless, despite the advances in our understanding of the disease, many molecular mechanisms underlying CHD remain uncertain.

Herein I present my efforts focused on discovering new genes and biological pathways altered in patients with CHD. The work is based on large CHD patient cohorts, collected and analysed as part of an international collaboration. The adopted integrative data-driven approach in this work can roughly be grouped into two principal aims: i) the development of statistical frameworks and bioinformatics tools to analyse high-dimensional data and ii) the meta-analysis of large-scale exome sequencing data to elucidate variants and genes conferring risk of CHD.

By meta-analysing copy number variations and de novo variants in CHD probands, we implicated novel genes reaching genome-wide significant association with CHD and strengthened previously described associations. We also explored the differences between non-syndromic and syndromic CHD by analysing a large-scale exome cohort of patients.

In summary, our integrative approach, supported by the data analysis of ~15,000 CHD patients, allowed us to gain new insights into the genetic origin of CHD. Consequently, we present here a valuable resource to continue investigating the causes of CHD and pave the way to promote new studies in this area.


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