Details

Autor(en) / Beteiligte

• Kaivola, Karri
• Chia, Ruth
• Ding, Jinhui
• Rasheed, Memoona
• Fujita, Masashi
• Menon, Vilas
• Walton, Ronald L.
• Collins, Ryan
• Billingsley, Kimberley
• Brand, Harrison
• Talkowski, Michael
• Zhao, Xuefang
• Dewan, Ramita
• Ray, Anindita K
• Solaiman, Sultana
• Jerez, Pilar Alvarez
• Malik, Laksh
• Dawson, Ted M
• Rosenthal, Liana
• Albert, Marilyn S.
• Pletnikova, Olga
• Troncoso, Juan C
• Masellis, Mario
• Keith, Julia
• Black, Sandra E.
• Ferrucci, Luigi
• Resnick, Susan M.
• Tanaka, Toshiko
• Topol, Eric
• Torkamani, Ali
• Tienari, Pentti
• Foroud, Tatiana M.
• Ghetti, Bernardino Francesco
• Landers, John E
• Ryten, Mina
• Morris, Huw R
• Hardy, John A.
• Mazzini, Letizia
• D'Alfonso, Sandra
• Moglia, Cristina
• Calvo, Andrea
• Serrano, Geidy E
• Beach, Thomas G
• Ferman, Tanis J
• Graff‐Radford, Neill R
• Boeve, Brad F.
• Wszolek, Zbigniew
• Dickson, Dennis W.
• Chio, Adriano
• Bennett, David A. A
• De Jager, Philip L
• Ross, Owen A.
• Dalgard, Clifton L.
• Gibbs, J. Raphael
• Traynor, Bryan J
• Scholz, Sonja W

Titel

Structural variants in Lewy body dementia and frontotemporal dementia spectrum

Ist Teil von

• Alzheimer's & dementia, 2023-12, Vol.19 (S12), p.n/a

Erscheinungsjahr

2023

Quelle

Wiley-Blackwell Journals

Beschreibungen/Notizen

• Background Structural variants range from simple loss or gain of genetic material to complex events that restructure entire chromosomes. This heterogeneity coupled with variant size greater than sequencing read‐length makes structural variant mapping from short‐read sequencing data difficult and error‐prone. Consequently, the role of structural variants is poorly explored in many phenotypes, including dementia. Method We used the GATK‐SV pipeline that combines four external structural variant mapping algorithms with machine‐learning to build high‐quality consensus structural variant calls. We applied GATK‐SV to short‐read whole‐genome sequencing data of 2,601 frontotemporal dementia‐amyotrophic lateral sclerosis spectrum (FTD/ALS) patients, 2,612 Lewy body dementia (LBD) patients, and 4,132 neurologically unaffected participants. Result After stringent quality‐control, we performed genome‐wide association studies on 2,307 ALS/FTD patients versus 3,677 controls with 4,699 common structural variants and 2,355 LBD patients versus 3,700 controls with 4,889 common structural variants. In the FTD/ALS cohort, we identified well‐known risk variants at the C9orf72 (p‐value = 4.99×10‐18, OR = 14.47, 95% CI = 7.90–26.49) and MAPT loci (p‐value = 3.48×10‐6, OR = 0.77, 95% CI = 0.68–0.86) corroborating the ability of our pipeline to identify disease‐associated structural variants. Moreover, we discovered, replicated, and validated TPCN1 as a novel risk locus for LBD (p‐value = 9.18×10‐6, OR = 1.43, 95% CI = 1.22–1.67). Further, we identified rare exonic variants with known pathogenic or likely pathogenic effects, such as an SNCA gene duplication in LBD and a deletion of exons 1 and 2 in CHCHD10 in FTD/ALS. We additionally observed structural variants in regulatory regions, e.g. a deletion of a key PSEN2 enhancer, which resulted in an approximate 0.70 fold‐change in PSEN2 expression in LBD. Finally, we built an interactive web app where structural variants can be visualized and explored (https://ndru-ndrs-lng-nih.shinyapps.io/non_ad_dementias_sv_app/). Conclusion Common and rare structural variants play a role in LBD and FTD/ALS. Our structural variant resource is publicly available and can be used to further explore the role of structural variants in LBD and FTD/ALS.