Open AccessA novel nonsense variant in SUPT20H gene associated with Rheumatoid Arthritis identified by Whole Exome Sequencing of multiplex families
Author(s) -
Maëva Veyssiere,
Javier Perea,
Laëtitia Michou,
Anne Boland,
Christophe Caloustian,
Robert Olaso,
JeanFrançois Deleuze,
François Cornélis,
Élisabeth Petit-Teixeira,
Valérie Chaudru
Publication year - 2019
Publication title -
plos one
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.99
H-Index - 332
ISSN - 1932-6203
DOI - 10.1371/journal.pone.0213387
Subject(s) - exome sequencing , genetics , pedigree chart , multiplex , biology , gene , genetic association , genome wide association study , exome , single nucleotide polymorphism , genotype , mutation
The triggering and development of Rheumatoid Arthritis (RA) is conditioned by environmental and genetic factors. Despite the identification of more than one hundred genetic variants associated with the disease, not all the cases can be explained. Here, we performed Whole Exome Sequencing in 9 multiplex families (N = 30) to identify rare variants susceptible to play a role in the disease pathogenesis. We pre-selected 77 genes which carried rare variants with a complete segregation with RA in the studied families. Follow-up linkage and association analyses with pVAAST highlighted significant RA association of 43 genes (p-value < 0.05 after 10 6 permutations) and pinpointed their most likely causal variant. We re-sequenced the 10 most significant likely causal variants (p-value ≤ 3.78*10 −3 after 10 6 permutations) in the extended pedigrees and 9 additional multiplex families (N = 110). Only one SNV in SUPT20H : c . 73A>T (p . Lys25*) , presented a complete segregation with RA in an extended pedigree with early-onset cases. In summary, we identified in this study a new variant associated with RA in SUPT20H gene. This gene belongs to several biological pathways like macro-autophagy and monocyte/macrophage differentiation, which contribute to RA pathogenesis. In addition, these results showed that analyzing rare variants using a family-based approach is a strategy that allows to identify RA risk loci, even with a small dataset.