A new data analysis approach identifies splicing variants associated with disease

A new data analysis approach identifies splicing variants associated with disease

Alternative splicing of CARD9, regulated by a Crohn’s disease risk variant (sQTL variant), alters protein structure. Credit: Department of Genome Function and Diversity, TMDU

In the age of big data, having a massive amount of information is the easy part; Knowing what to do with it is another story entirely. But now, researchers from Japan report that a new approach to analyzing data from genome-wide association studies can help reveal the genetic basis of many diseases.

In a study published in August in Nature ConnectionsResearchers from Tokyo Medical and Dental University (TMDU) have revealed that analysis of the coding sequences of gene splicing variants at disease-related loci can help reveal the genetic cause of some complex human diseases.

Differences in our genes cause complex diseases, but it can be difficult to know how a single genetic difference leads to disease. While some variants cause disease by change gene expression levelsIt is increasingly clear that the splicing variants that influence how a gene is transcribed — that is, how the gene’s DNA sequence is transcribed into RNA — also play an important role.

“There are a number of current approaches to identifying and analyzing genetic variants that cause splicing changes in disease-related genes,” explains Kensuke Yamaguchi, lead author of the study. “However, these methods are limited by incomplete annotation of the splicing isoforms and using the same splicing crossover by multiple isoforms, which makes them difficult to distinguish from one another.”

To overcome these drawbacks, the researchers developed a set of two analyzes that fully capture the complexity of linkage differences and their relationship to human disease: The first analysis combines isoforms with the same coding sequence to detect the resulting changes in Protein Structure, and the second analysis examines the effects of isoforms with incomplete annotations but unique coding sequences. The team then identified the full sequence of these isoforms and validated their expression in cells.

“The results show that our approach is robust and effective,” says Utah Kochi, senior author of the research paper. “We have successfully identified 29 full-length isoforms with coding sequences not associated with genetic alterations that have been linked to diseases such as Parkinson’s disease, ankylosing spondylitis, irritable bowel disease, and neurodegenerative disease.”

Furthermore, they showed that genes with disease-associated splicing variants can be identified by evaluating their effects on the expression of other genes within the genome. For example, a alternative leading to a change in the ratio of two isoforms of the SNRPC gene identified as being associated with Systemic lupus erythematosus.

Taken together, these results highlight the underappreciated role of protein-altering splicing variants in disease pathogenesis. Identification of relevant variants and assessment of their function in future research using animal models can help elucidate how complex diseases arise.


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more information:
Kinsuke Yamaguchi et al., QTL linkage analysis focusing on coding sequences reveals mechanisms for disease susceptibility loci, Nature Connections (2022). DOI: 10.1038 / s41467-022-32358-1

Provided by Tokyo Medical and Dental University

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