An innovative integrative functional genomics approach has identified 50 new Parkinson's disease candidate genes

Many neurodegenerative diseases, such as Parkinson's disease (PD), are caused
by a combination of several genetic mutations, known as polygenic mutations.
Although previous studies have identified some genes associated with familial or sporadic cases of PD, we are still far from knowing the full spectrum
of genes that contribute to this complex disease.
Researchers at the Jan and Dan Duncan Institute for Neurological Research at Texas Children's Hospital and Baylor College of Medicine recently developed an integrative functional genomics approach that identified 50 genes, demonstrated for the first time that PD pathology
can be modified in animal models of disease.
The study was published in
Human Molecular Genetics.

The highlight of the study is a new multidisciplinary, high-throughput method developed by the team to identify and functionally validate dozens of genes that cause PD and neuroprotection
.

Typically, it takes years to identify and functionally verify a gene's role in a genetic disease, which is a particularly onerous task
for polygenic diseases like Parkinson's disease.
By integrating several computational and in vivo biological methods in a single screening strategy, the team was able to identify and validate many PD gene candidate genes
in a fairly short period of time.

Since 2005, genome-wide association studies (GWAS) have been used to analyze the genomes of large numbers of individuals to identify genomic variants
that are statistically associated with an increased risk of complex genetic diseases.
While this approach reveals genetic locus/genetic variants that may be potentially relevant to a particular disease, further in-depth research in vivo cultured cells and/or animal models is required to demonstrate the biological role of these variants in the pathogenesis of that disease, a labor-intensive and time-consuming process
.
In recent years, a new method called transcriptome total association studies (TWAS) has been developed to predict genetic risk
for complex diseases.
Combining TWAS and GWAS with machine learning algorithms gave them insight into the potential capabilities of
these variables.
However, the genes identified by both methods require further experimental validation
.

To speed up the genetic validation process, a multi-step method has been developed here that combines several computational and in vivo validation methods
.

"First, we nominated 160 potential PD candidate genes via GWAS and TWAS and analyzed them further using other advanced computational tools, yielding 80 high-confidence PD genes
.
" The author said
.
Second, we established a link between
these candidate genes and PD-related pathology by assessing whether the expression patterns of these candidate genes in the brain and blood transcriptome of PD patients were altered.
Finally, to measure the functional relationships between these candidate genes and assess the biological pathways in which they participate, we performed several silicon wafer and in vivo analyses that resulted in 50 PD risk genes and 14 potential neuroprotective genes
.
”

"We succeeded in identifying so many new variants, and the remarkable consistency of results obtained at each step of screening, demonstrates that this is a powerful way to
identify and validate new Parkinson's disease candidate genes.
" In addition, as long as genomic information is readily available, this approach can be widely applied to a wide range of complex genetic diseases, so we expect this research to have broad implications
for disease areas beyond Parkinson's disease.
" ”

Jiayang Li, Bismark Kojo Amoh, Emma McCormick, Akash Tarkunde, Katy Fan Zhu, Alma Perez, Megan Mair, Justin Moore, Joshua M Shulman, Ismael Al-Ramahi, Juan Botas.
Integration of transcriptome-wide association study with neuronal dysfunction assays provides functional genomics evidence for Parkinson’ s disease genes.
Human Molecular Genetics, 2022; DOI: 10.
1093/hmg/ddac230