A transcriptome-wide association study identifies susceptibility genes for Parkinson’s disease

Abstract

Genome-wide association study (GWAS) has seen great strides in revealing initial insights into the genetic architecture of Parkinson’s disease (PD). Since GWAS signals often reside in non-coding regions, relatively few of the associations have implicated specific biological mechanisms. Here, we aimed to integrate the GWAS results with large-scale expression quantitative trait loci (eQTL) in 13 brain tissues to identify candidate causal genes for PD. We conducted a transcriptome-wide association study (TWAS) for PD using the summary statistics of over 480,000 individuals from the most recent PD GWAS. We identified 18 genes significantly associated with PD after Bonferroni corrections. The most significant gene, LRRC37A2, was associated with PD in all 13 brain tissues, such as in the hypothalamus (P = 6.12 × 10−22) and nucleus accumbens basal ganglia (P = 5.62 × 10−21). We also identified eight conditionally independent genes, including four new genes at known PD loci: CD38, LRRC37A2, RNF40, and ZSWIM7. Through conditional analyses, we demonstrated that several of the GWAS significant signals on PD could be driven by genetically regulated gene expression. The most significant TWAS gene LRRC37A2 accounts for 0.855 of the GWAS signal at its loci, and ZSWIM7 accounts for all the GWAS signals at its loci. We further identified several phenotypes previously associated with PD by querying the single nucleotide polymorphisms (SNPs) in the final model of the identified genes in phenome databases. In conclusion, we prioritized genes that are likely to affect PD by using a TWAS approach and identified phenotypes associated with PD.

Longitudinal intronic RNA-Seq analysis of Parkinson’s disease patients reveals disease-specific nascent transcription

“…Whole transcriptome analysis is typically based on gene or exon annotation; quite seldom transcript-based annotation is used. 26,27 Gene-based annotation is an amalgamated approach where the reads mapping to different exons and transcripts will be merged under a single functional identifier, the gene. 28 This approach is the most widely used and therefore most of the whole transcriptome studies provide this aggregated information.…”

A Novel Defined Super-Enhancer Associated Gene Signature to Predict Prognosis in Patients With Diffuse Large B-Cell Lymphoma

“…LRRC37A2 is a member of the LRRC37 gene family which is involved in the regulation of protein–ligand interactions and mapped to chromosome 17q21.31-q21.32 ( Giannuzzi et al, 2013 ). Several studies suggested that LRRC37A2 is implicated in epilepsy, epileptic encephalopathy, and Parkinson’s disease, while the effect on DLBCL has never been reported ( Yao et al, 2021 ). In this study, high expression of LRRC37A2 corresponds with an inferior survival outcome that merits further exploitation.…”

Molecular Quantitative Trait Locus Mapping in Human Complex Diseases

“…Using this approach, Li et al identified 66 genes whose predicted expression or splicing levels in the dorsolateral prefrontal cortex (DLFPC) and peripheral monocytes are significantly associated with Parkinson's disease (PD) risk (Li, Wong, Humphrey, & Raj, 2019). Additionally, considering the essential role of epigenetic features in predicting gene expression, Yao et al developed an epigenetic elementbased TWAS considering both genetic and epigenetic effects on gene expression as a powerful method to identify specific genes and mechanisms that underlie PD genetic risk factors (Yao et al, 2021). Applying the same idea to integrate pQTL results, Wingo et al performed a brain proteome-wide association study and implicated novel proteins in depression pathogenesis (Wingo et al, 2021b).…”

“…Additionally, considering the essential role of epigenetic features in predicting gene expression, Yao et al. developed an epigenetic element‐based TWAS considering both genetic and epigenetic effects on gene expression as a powerful method to identify specific genes and mechanisms that underlie PD genetic risk factors (Yao et al., 2021). Applying the same idea to integrate pQTL results, Wingo et al.…”