High-resolution genomic DNA copy number analysis of 125 congenital heart disease family trios from the Pediatric Cardiac Genomics Consortium

Doctor's Name: 
Ronemus, Michael, MD
Cold Spring Harbor Laboratory, Cold Spring Harbor, NY

High-resolution genomic DNA copy number analysis of 125 congenital heart disease family trios from the Pediatric Cardiac Genomics Consortium

Congenital heart disease (CHD) represents the most common human congenital malformation, occurring in approximately 1 in 100 live births. Despite considerable advances in care, CHD remains a major contributor to newborn mortality and is associated with substantial morbidities and premature death. Genetic abnormalities are the primary cause of CHD, but identifying the precise defects that underlie CHD has proven challenging. Due to recent advances in genomic technology such as microarray analysis and next-generation DNA sequencing, scientists have begun to identify the genetic factors underlying CHD. Over the last 5 years in particular, there has been significant progress from many research groups in identifying genetic contributors to CHD.

Copy number variants, or CNVs—large deletions or duplications of DNA ranging from several thousand to millions of DNA bases—have been implicated in many common genetic disorders, including autism, schizophrenia and early onset cancers. The application of genomic technology to CHD has led to a number of recent publications linking CNVs to CHD. In many cases, these events arise as ‘de novo’ mutations—the CNV is found only in the child, and not in either parent. Current published estimates are that up 8–14% of CHD cases may be caused by de novo CNVs, and the rates are generally higher in more severe cases that  However, this is believed to be an underestimate, as many of these events cannot be detected by the current clinical tools, e.g. chromosomal microarrays.

To address the limitations of the earlier methods, we have developed a next-generation DNA sequencing technology called SMASH (short multiply aggregated sequence homologies) for CNV detection. SMASH yields results comparable to results achieved by sequencing the entire genome, but at a fraction of the cost. Using this technology, we will assess the distribution of CNVs in a high-resolution pilot study on CHD families. This will reveal the full spectrum of causal copy number mutations in CHD. Families will be drawn from the Pediatric Cardiac Genomics Consortium set of families, the most clinically and molecularly well-characterized CHD cohort that is currently available.

The ‘gold standard’ of genetic causality is to find the same mutations in unrelated families. For CHD we hope to determine whether a specific gene—or set of genes—contributes to the condition. With a greater yield of detectable CNVs, we expect to find both new events and ‘recurrent’ CNV mutations in known CHD risk genes. This will allow us to unambiguously ascribe individual genes to CHD

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