By studying genetic data from people born with congenital heart disease or autism, researchers have uncovered almost two dozen genes that contribute to heart defects.
Identifying causative variants
The development of advanced sequencing technologies, such as whole-exome sequencing (WES), has enabled researchers to identify the genetic causes of many diseases. WES has been particularly successful in identifying novel causal genes in both Mendelian and complex disorders.
These techniques typically generate a large amount of variant data. Therefore, one way to narrow down the pool of candidate genes is to scan for de novo mutations (DMNs). These mutations have not been through natural selection and have proven informative in identifying risk genes for early onset diseases, such as congenital heart disease (CHD). However, statistical power is currently limited by the small sample size of DNM studies due to high costs of recruiting and sequencing samples as well as the rarity of DNMs.
Recent studies have shown that multiple early-onset diseases have shared risk genes. For example, a recent study, identified a striking overlap between genes with damaging DNMs in probands with CHD and autism. This observation suggests that researchers could leverage information from one trait to improve statistical power to identify genes for another trait. Nonetheless, there are currently few methods that can jointly analyse DNM data on multiple traits.
Identifying risk genes for multiple traits simultaneously
In a paper, published in PLoS Genetics, researchers presented a framework to identify risk genes for multiple traits simultaneously for DNM data. The researchers developed an algorithm – M-DATA (Multi-trait framework for De novo mutation Association Test with Annotations) – to increase the statistical power of association analysis by integrating sequencing data from people with related conditions.
The team applied the framework to genetic data from people with CHD or autism. Here, they identified 23 genes for CHD, including 12 genes that were previously unknown. The authors noted that this framework is more effective at identifying risk genes than analyses focussed on a single disease alone. This method could help researchers identify previously unknown genes linked to disease as well as improve our understanding of the aetiology for different conditions.
Hongyu Zhao, co-author of the study, said:
“By jointly analysing de novo mutations from congenital heart disease (CHD) and autism, we identified novel genes that may play an important role in explaining the shared genetic aetiology of CHD and autism.”
Image credit: canva
