Literature DB >> 33239198

Association study of DNAJC13, UCHL1, HTRA2, GIGYF2, and EIF4G1 with Parkinson's disease.

Prabhjyot Saini1, Uladzislau Rudakou1, Eric Yu1, Jennifer A Ruskey2, Farnaz Asayesh2, Sandra B Laurent1, Dan Spiegelman2, Stanley Fahn3, Cheryl Waters3, Oury Monchi4, Yves Dauvilliers5, Nicolas Dupré6, Lior Greenbaum7, Sharon Hassin-Baer8, Alberto J Espay9, Guy A Rouleau10, Roy N Alcalay11, Edward A Fon2, Ronald B Postuma2, Ziv Gan-Or12.   

Abstract

Rare mutations in genes originally discovered in multigenerational families have been associated with increased risk of Parkinson's disease (PD). The involvement of rare variants in DNAJC13, UCHL1, HTRA2, GIGYF2, and EIF4G1 loci has been poorly studied or has produced conflicting results across cohorts. However, they are still being often referred to as "PD genes" and used in different models. To further elucidate the role of these 5 genes in PD, we fully sequenced them using molecular inversion probes in 2408 patients with PD and 3444 controls from 3 different cohorts. A total of 788 rare variants were identified across the 5 genes and 3 cohorts. Burden analyses and optimized sequence Kernel association tests revealed no significant association between any of the genes and PD after correction for multiple comparisons. Our results do not support an association of the 5 tested genes with PD. Combined with previous studies, it is unlikely that any of these genes plays an important role in PD. Their designation as "PARK" genes should be reconsidered.
Copyright © 2020 Elsevier Inc. All rights reserved.

Entities:  

Keywords:  Ashkenazi-Jewish; DNAJC13; EIF4G1; French-Canadian; GIGYF2; HTRA2; Parkinson's disease; SKAT-O; UCHL1

Mesh:

Substances:

Year:  2020        PMID: 33239198      PMCID: PMC7940813          DOI: 10.1016/j.neurobiolaging.2020.10.019

Source DB:  PubMed          Journal:  Neurobiol Aging        ISSN: 0197-4580            Impact factor:   4.673


  77 in total

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10.  Fast and accurate short read alignment with Burrows-Wheeler transform.

Authors:  Heng Li; Richard Durbin
Journal:  Bioinformatics       Date:  2009-05-18       Impact factor: 6.937

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