medRxivGenetic & Genomic MedicinePreprint
Large-scale Rare Variant Burden Testing in Parkinson's Disease Identifies Novel Associations with Genes Involved in Neuro-inflammation
Mary B. MakariousiD, Julie LakeiD, Vanessa PitziD, Allen Ye FuiD, Joseph L. Guidubaldi, Caroline Warly Solsberg, Sara Bandrés‐CigaiD, Hampton L. LeonardiD, Jonggeol Jeffrey KimiD, Kimberley J. BillingsleyiD, Francis P. Grenn, Pilar Álvarez JereziD, Chelsea X. AlvaradoiD, Hirotaka IwakiiD, Michael TaiD, Dan VitaleiD, Dena G. HernandeziD, Ali TorkamaniiD, Mina RyteniD, John HardyiD, Sonja W. ScholziD, Bryan J. TraynoriD, Clifton L. DalgardiD, Debra J. EhrlichiD, Toshiko TanakaiD, , Cornelis BlauwendraatiD
- Citations
- 4
Abstract
Parkinsons disease (PD) has a large heritable component and genome-wide association studies to date have identified over 90 variants associated with PD, providing deeper insights into the disease biology. However, there have not been large-scale rare variant analyses for PD. To address this gap, we investigated the rare genetic component of PD at minor allele frequencies <1%, using whole genome and whole exome sequencing data from 7,184 PD cases, 6,701 proxy-cases, and 51,650 healthy controls from the Accelerating Medicines Partnership Parkinsons disease (AMP-PD) initiative, the National Institutes of Health, the UK Biobank, and Genentech. We performed burden tests meta-analyses on protein-altering variants, prioritized based on their predicted functional impact. Our work identified several genes reaching exome-wide significance. While two of these genes, GBA and LRRK2, have been previously implicated as risk factors for PD, we identify potential novel associations for B3GNT3, AUNIP, ADH5, TUBA1B, OR1G1, CAPN10, and TREML1. Of these, B3GNT3 and TREML1 provide new evidence for the role of neuroinflammation in PD. To date, this is the largest analysis of rare genetic variation in PD.
- organism: human
- assay: whole-genome sequencing
- assay: whole-exome sequencing
- research focus: variant effect prediction
- approach: statistical model
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