New multi-layered approach discovers 50 novel candidate genes for Parkinson’s disease

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

Researchers at Texas Children’s Hospital and Baylor College of Medicine’s Jan and Duncan Neurology Institute recently developed an integrated functional genomics approach that led to the discovery of 50 genes first shown to alter PD pathology in diseased animals. did. model. This research human molecular genetics.

The study was led by Dr. Juan Botas, a Baylor University professor and Duncan NRI researcher. A highlight of this research is a new interdisciplinary study. high throughput An approach developed by the team to identify and functionally validate dozens of PD-causing neuroprotective genes.

It usually takes years to identify and functionally validate a gene’s role. hereditary disease, which is a particularly laborious task for polygenic diseases like PD. By integrating several computational and in vivo biological approaches within a single screening strategy, the team was able to identify and validate many PD gene candidates in a fairly short time.

Since 2005, we have used genome-wide association studies (GWAS) to analyze the genomes of large numbers of individuals to identify genomic variants that are statistically associated with increased risk of complex inherited diseases. Although this method reveals loci/gene variants that may be potentially associated with specific diseases, demonstrating the biological involvement of these variants in pathogenesis requires experiments in cultured cells. Further in vitro and/or in vivo studies in animal models are needed. This is a labor intensive and time consuming process.

Recently, a new approach known as transcriptome-wide association studies (TWAS) has been developed to predict genetic risk for complex diseases. Combining TWAS and GWAS with machine learning algorithms gave researchers insight into the potential functions of these variants. Nevertheless, genes identified by both methods require further validation.

To speed up the gene validation process, the first author of this study, graduate student Jiayang Li, and colleagues developed a multistep approach that combines several computational and in vivo validation methods.

“First, we nominated 160 potential PD candidate genes through GWAS and TWAS, which were further analyzed using other state-of-the-art computational tools, leading to 80 high-confidence PD genes,” said Dr. Li said. “Second, we established a link between these candidates and PD-related pathologies by assessing whether the expression patterns of these candidates were altered in the brain and blood transcriptomes of PD patients. Finally, to assess the functional relationships between these candidates and assess which biological pathways they are involved in, we performed several in silico and in vivo analyzes and finally We obtained 50 PD risk genes and 14 potential neuroprotective genes.”

“We successfully identified a large number of new variants, and the striking concordance of results obtained at each step of this screen supports this as a powerful method for identifying and validating novel PD candidate genes.” We do,” said Dr. Botas. “Furthermore, as long as genomic information is readily available, this approach is broadly applicable to a wide range of complex genetic diseases, so we anticipate that this study will have broad implications for disease areas well beyond his PD.” .”

Others involved in this study were Bismarck Amor, Emma McCormick, Akash Tarkunde, Katie Chu, Alma Perez, Megan Mair, Justin Moore, Joshua Shulman and Ismael Alramahhi. They were affiliated with the Baylor College of Medicine and the Jan and Dunduncan Neurological Institute at Texas Children’s Hospital.

Courtesy of Texas Children’s Hospital