Large‑scale pathway specific polygenic risk and transcriptomic community network analysis identifies novel functional pathways in Parkinson disease - Table 13

The Molecular Genetics Section (MGS) of the Laboratory of Neurogenetics, led by Dr. Andrew Singleton, works on the identification of genetic variants that cause and contribute to both simple and complex neurological diseases. The MGS works across varied neurodegenerative diseases including most prominently Parkinson's disease but also ataxia, dementia with Lewy bodies, frontotemporal dementia, and rare orphan syndromes.

The MGS uses a combination of state of the art genetics and genomics approaches. Further the MGS has acted as a catalyst for the creation of large collaborative consortia aimed at dissecting the genetic basis of common complex diseases.

The Neuromuscular Diseases Research Section of the Laboratory of Neurogenetics, led by Dr. Bryan J. Traynor, is best known for its work to understand the genetic etiology of amyotrophic lateral sclerosis (also known as Lou Gehrig's disease) and frontotemporal dementia. In 2011, he led the international consortium that identified a pathogenic hexanucleotide repeat expansion in the C9orf72 gene as the underlying gene variant in a large proportion of amyotrophic lateral sclerosis and frontotemporal dementia (Neuron 2011).

Other notable achievements of Dr. Traynor's laboratory include the first genome-wide association study of ALS (Lancet Neuro 2007); identification of the chromosome 9p21 association signal for ALS in the Finnish founder population (Lancet Neuro 2010); the identification of the C9orf72 repeat expansion in patients clinically diagnosed with Alzheimer’s disease (NEJM 2012); and the description of variations in the VCP, MATR3, CHCHD10, HTT and SPTLC1 genes as causes of familial ALS and FTD (Neuron 2010, Nature Neuroscience 2014, Brain 2014, Neuron 2018, Neuron 2021, JAMA Neurology 2021).

To assess PD risk, summary statistics from Chang et al. PD GWAS meta-analysis involving 26,035 PD cases and 403,190 controls of European ancestry were used as the reference dataset for the primary analysis to define risk allele weights. Individual-level genotyping data not included in Chang et al. [7] and from the last GWAS meta-analysis [17] was then randomly divided as the training and testing datasets. The training dataset used to construct the PRS consisted of 7218 PD cases and 9424 controls, while the testing dataset to validate the results consisted of 5429 PD cases and 5814 controls, all of European ancestry. A polygenic effect score (PES) was generated to estimate polygenic risk for each of the 2199 gene sets representative of biological pathways and then tested for association with PD. PES was calculated based on the weighted allele dose as implemented in PRSice2 (v2.1.1). The sequence kernel association test-optimal (SKAT-O) was implemented using default parameters in RVTESTS [35] to determine the difference in the aggregate burden of rare coding genetic variants (minor allele count≥ 3) between PD cases and controls for the nominated gene-sets by PRS. ANNOVAR was used for variant annotation. Baseline peri-diagnostic RNA sequencing data derived from the blood for 1612 PD patients and 1042 healthy subjects available from the Parkinson Progression Marker Initiative (PPMI) was used to construct a network of expression communities based on a graph model with Louvain clusters. This cleaned and normalized data was downloaded from the Accelerating Medicines Partnership for Parkinson’s disease (AMP-PD) on March 1st, 2020. Scikit-learn’s extraTreeClassifer option was used to extract coding gene features for inclusion in the network builds that are likely to contribute to classifying cases versus controls under default settings in the feature selection phase, leaving 8.3 k protein-coding genes for candidate networks. Following this feature extraction phase, controls were excluded, and case-only correlations were calculated for all remaining gene features. Next, this correlation structure was converted to a graph object using NetworkX. Subsequently, the Louvain algorithm was employed to build network communities within this graph object derived from the selected feature set. Finally, pathway enrichment analysis within expression communities was performed to further dissect its biological function using the function g:GOSt from g:ProfleR. The significance of each pathway was tested by hypergeometric tests with Bonferroni correction to calculate the error rate of each network. Single-cell RNA sequencing data [25] based on a total of 9970 cells obtained from several mouse brain regions (neocortex, hippocampus, hypothalamus, striatum, and midbrain) was used to explore cell types associated with PD risk. Linear regression adjusted by the number of SNPs included in the PRS was performed to assess the trend of increased PRS R2 per decile of cell-type expression specificity. Two-sample SMR was applied to explore the enrichment of cis eQTLs within the 46 gene-sets nominated by our large scale PRS analysis. The methodology can be interpreted as an analysis to test if the effect size of genetic variants influencing PD risk is mediated by gene expression or methylation to prioritize genes underlying these gene-sets for follow-up functional studies. Additionally, we studied expression patterns in blood from the largest eQTL meta-analysis so far. The number of genes tested per gene-set were Bonferroni corrected, and a Chisq test was applied to assess whether the proportion of QTLs per geneset was significantly higher than expected by chance.

SMR revealed functional genomic associations with eQTLs in 201 genes (Supplementary Table 12, online resource) of which 88 were found to be part of the network communities significantly associated with PD in our transcriptome community map (Supplementary Table 13, online resource).