期刊
NPJ PARKINSONS DISEASE
卷 7, 期 1, 页码 -出版社
NATURE PORTFOLIO
DOI: 10.1038/s41531-021-00221-7
关键词
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资金
- Science and Technology Major Project of Hunan Provincial Science and Technology Department [2020SK2123, 2018SK1030]
- National Natural Science Foundation of China [82101601]
- Natural Science Foundation of Hunan Province [2019JJ80024, 2020JJ8081]
- Scientific Research Fund of Hunan Provincial Health and Family Planning Commission [C2019043]
- Hunan Science and Technology Innovation Project [2018SK50605]
This study aimed to integrate GWAS results with eQTL data to identify candidate causal genes for PD, conducting a TWAS for PD and identifying 18 significantly associated genes. The most significant gene, LRRC37A2, was associated across multiple brain tissues. Additionally, new genes with conditional independence at known PD loci were identified, and phenotypes associated with PD were also explored through querying SNPs in identified genes in phenome databases.
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 x 10(-22)) and nucleus accumbens basal ganglia (P = 5.62 x 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.
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