New Study Reveals Distinct Gene Discovery Methods in Disease Research

A recent study published in the journal Nature highlights the complementary roles of two primary gene discovery methods in identifying disease-linked genes. Conducted by researchers from NYU Langone Health, Stanford University, UC San Francisco, and the University of Tokyo, the research underscores how these methods yield different insights that could significantly impact drug development.

The investigation focused on the human genome, which comprises thousands of genes that provide instructions for protein production and regulatory DNA that dictates gene activation. By examining data from the UK Biobank, which includes genetic information from hundreds of thousands of individuals, the researchers analyzed two prevalent approaches: genome-wide association studies (GWAS) and burden tests.

GWAS explore common genetic variants across the genome to identify associations with diseases, while burden tests concentrate on rare variants that modify protein functions. The study scrutinized results for 209 traits, revealing that burden tests tend to pinpoint genes with narrow disease associations, whereas GWAS can identify both disease-specific genes and those influencing a broader range of biological processes.

Hakhamanesh Mostafavi, Ph.D., co-senior author of the study and assistant professor at NYU Grossman School of Medicine, emphasized, “Our study explains why the methods produce different results and why both are biologically important.” He noted that the findings offer clarity on how genetic discoveries can inform applications such as drug development.

The research highlights a critical challenge in genetics: GWAS often implicate numerous genes per disease, complicating the determination of which genes are truly impactful. Conversely, burden tests have emerged as a powerful alternative, yielding a more precise understanding of the genes associated with specific diseases.

Key findings indicate that the differences in results between the two methodologies arise from how genes affect various traits. Some genes primarily influence a single trait, while others may impact multiple traits simultaneously. Variants that disrupt these “multi-trait” genes typically face evolutionary removal, leading to their rarity in the population. In contrast, GWAS can still detect these genes as regulatory DNA variants often have less pronounced effects on gene activity.

The researchers propose two essential features for effective gene prioritization regarding disease risk: “importance,” which refers to how much a gene impacts disease when disrupted, and “specificity,” which indicates whether a gene predominantly affects one disease or multiple traits. Understanding these features is crucial for identifying promising therapeutic targets and anticipating potential side effects in drug development.

The study also examined the reliability of the p-value, a common statistical measure. The authors found that the p-values from GWAS and burden tests do not reliably indicate a gene’s significance, which poses challenges for identifying key biological processes related to diseases.

“Our results do not mean that GWAS and burden tests lack useful information,” said Mostafavi. “They just have not been interpreted in this way before.” He called for new methods to better infer the biological significance of genes.

Looking ahead, the research team intends to develop innovative methods to prioritize genes based on their importance. They contend that neither GWAS nor burden tests alone possess sufficient power to accurately measure the impact of each gene on disease. By integrating these results with emerging experimental data on gene functions within cells, the researchers believe machine learning techniques can uncover shared patterns that enhance understanding of disease genetics.

“This would be revolutionary because it would let us leverage all of the cell-level experimental data to learn about human-level traits, identify the most important disease genes, and streamline drug development,” remarked Jeffrey Spence, Ph.D., co-senior author and assistant professor at UC San Francisco.

As the field of genetics continues to evolve, the insights from this study may pave the way for more effective strategies in understanding and treating various diseases.