A new multi-layered approach has identified 50 new candidate genes for Parkinson’s disease

Many neurodegenerative diseases, such as Parkinson’s disease (PD), result from the combined effects of mutations in multiple genes (e.g., polygenic). Although previous studies have identified several genes responsible for familial or sporadic cases of PD, we are still far from knowing the full spectrum of genes that contribute to this complex disorder.

Researchers at the John and Dan Duncan Institute for Neurological Research at Texas Children’s Hospital and Baylor College of Medicine recently developed an integrated functional genomics approach that led to the discovery of 50 genes that were first shown to alter PD pathology in a diseased animal model. The study was published in Human molecular genetics.

The study was led by Dr. Juan Botas, a professor at Baylor College and a researcher at Duncan Research Institute. The highlight of the study is the new interdisciplinary high bandwidth approach developed by the team to identify and functionally validate dozens of PD-causing and neuroprotective genes.

It usually takes several years to identify and functionally confirm the role of a gene in a genetic disorder, and this is a particularly difficult task for a polygenic disease like PD. By integrating multiple computational and in vivo biological approaches within a single screening strategy, the team was able to identify and validate many candidate PD genes in a relatively short time.

Since 2005, genome-wide association studies (GWAS) have been used to analyze the genomes of large numbers of individuals in order to identify genomic variants that are statistically associated with an increased risk of a complex genetic disease. Although this method identifies genetic loci/gene variants that may potentially be associated with a particular disease, further in-depth studies in vitro in cultured cells and/or in vivo in animal models are needed to demonstrate the biological involvement of these variants in pathogenesis. animals of this disease, which are time-consuming and long processes.

In recent years, a new approach known as transcriptome-based association studies (TWAS) has been developed to predict genetic risk for complex diseases. Combining TWAS and GWAS with a machine learning algorithm gave researchers insight into the potential function of these variants. Nevertheless, the genes detected by both methods need further experimental verification.

To speed up the gene validation process, the study’s lead author, graduate student Jiayang Li, and others developed a multi-step approach that combined multiple computational and in vivo validation methods.

“We initially identified 160 potential PD candidate genes using GWAS and TWAS, which were further analyzed using other state-of-the-art computational tools and resulted in 80 high-confidence PD genes,” Li said. “Second, we established a link between these candidates and PD-related pathology by assessing whether the expression patterns of these candidates were altered in the brain and blood transcriptomes of PD patients. Finally, to evaluate the functional relationships between these candidates and determine which biological pathways they are involved in, we performed several in silico and in vivo analyses, which resulted in 50 PD risk genes and 14 potentially neuroprotective genes.”

“Our success in identifying so many new variants and the excellent concordance of the results we obtained at each step of this screen support this as a powerful method for identifying and validating new candidate genes for PD,” said Dr. Bottas. “Furthermore, as long as genomic information is readily available, this approach can be broadly applied to a wide range of complex genetic disorders, and we therefore expect this research to have a broad impact on disease areas beyond PD.”

Other study participants included Bismarck Amoh, Emma McCormick, Akash Tarkunde, Katie Zhu, Alma Perez, Megan Mair, Justin Moore, Joshua Shulman, and Ismael Al-Ramahi. They were affiliated with Baylor College of Medicine and the John and Dan Duncan Institute for Neurological Research at Texas Children’s Hospital.

Courtesy of Texas Children’s Hospital