Summary: A study of genetic data from people with autism and congenital heart disease identified 23 genes associated with heart disease. 12 of the genes were previously unknown.
Researchers identified almost two dozen genes that contribute to heart defects by studying genetic data from people born with congenital heart disease or autism.
Hongyu Zhao of Yale University and colleagues developed a new algorithm to analyze genetic data from related conditions, which they describe in a new paper publishing November 4th in the journal PLOS Genetics.
Multiple diseases that start early in life appear to be linked to mutations in the same genes. Recent research looking at de novo mutations—new mutations that pop up in children that were not present in the parents—has demonstrated a connection between congenital heart defects and autism.
However, sequencing de novo mutations is expensive, so small studies of individual diseases have limited power to identify genes that increase a person’s risk of the disease.
In the new study, researchers developed an algorithm called M-DATA (Multi-trait De novo mutation Association Test with Annotations) that combines sequencing data from people with related conditions to identify genes that contribute to disease. They applied the new method to genetic data from people with congenital heart disease or autism and successfully identified 23 genes for congenital heart disease, including 12 that were previously unknown.
The researchers conclude that M-DATA is more effective at identifying genes that increase a person’s risk than analyses focusing on a single disease. This is because instead of analyzing a small number of genomes from affected individuals, M-DATA analyzes a larger number of combined genomes from multiple groups of people.
The new method may help researchers identify previously unknown genes linked to disease and improve our understanding of the cause and potential treatment for different conditions.
Zhao adds, “By jointly analyzing 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 etiology of CHD and autism.”
Yuhan Xie, the lead student of the research, says, “As a biostatistics student, it’s very motivating to find what could be meaningful to the patients and their families.”
About this genetics research news
Author: Hongyu Zhao
Contact: Hongyu Zhao – PLOS
Image: The image is credited to Yuhan Xie
Original Research: Open access.
“M-DATA: A statistical approach to jointly analyzing de novo mutations for multiple traits” by Xie Y, Li M, Dong W, Jiang W, Zhao H. PLOS Genetics
M-DATA: A statistical approach to jointly analyzing de novo mutations for multiple traits
Recent studies have demonstrated that multiple early-onset diseases have shared risk genes, based on findings from de novo mutations (DNMs). Therefore, we may leverage information from one trait to improve statistical power to identify genes for another trait. However, there are few methods that can jointly analyze DNMs from multiple traits.
In this study, we develop a framework called M-DATA (Multi-trait framework for De novo mutation Association Test with Annotations) to increase the statistical power of association analysis by integrating data from multiple correlated traits and their functional annotations.
Using the number of DNMs from multiple diseases, we develop a method based on an Expectation-Maximization algorithm to both infer the degree of association between two diseases as well as to estimate the gene association probability for each disease. We apply our method to a case study of jointly analyzing data from congenital heart disease (CHD) and autism.
Our method was able to identify 23 genes for CHD from joint analysis, including 12 novel genes, which is substantially more than single-trait analysis, leading to novel insights into CHD disease etiology.