Details

Autor(en) / Beteiligte

• Ramos, Eliana Marisa
• Mirza, Saira S.
• Wojta, Kevin
• Yang, Zhongan
• Paterson, Andrew D.
• Rogaeva, Ekaterina
• Brushaber, Danielle
• Foroud, Tatiana M.
• Forsberg, Leah K.
• Heuer, Hilary W.
• Russell, Lucy L.
• Lago, Argentina Lario
• Rademakers, Rosa
• Boeve, Brad F.
• Rosen, Howard J.
• Rohrer, Jonathan D.
• Boxer, Adam L.
• Masellis, Mario
• Geschwind, Daniel H.

Titel

Familial relatedness in genetic frontotemporal dementia cohorts: findings from the international Frontotemporal Dementia Prevention Initiative

Ist Teil von

• Alzheimer's & dementia, 2023-12, Vol.19 (S18), p.n/a

Erscheinungsjahr

2023

Quelle

Wiley-Blackwell Journals

Beschreibungen/Notizen

• Background Given the rarity of genetic frontotemporal dementia (FTD), researchers across the world have come together to form the FTD Prevention Initiative (FPI) in an effort to improve prevention trials design. As this initiative begins to bring together large‐scale data from worldwide cohort series, including ALLFTD in North America and GENFI in Europe and Canada, it is critical for FPI to quantify the level of relatedness between all participants. Here we provide the most recent update to these ongoing analyses. Methods Genome‐wide SNP genotyping data from 1,684 ALLFTD and 568 GENFI participants was used to perform lineage analyses using PLINK. Briefly, QC was performed similarly in all datasets to remove individuals with low call rate and filter autosomal SNPs for missingness, frequency, and deviation from Hardy‐Weinberg equilibrium. Genetic ancestry was inferred by projecting genotyped samples into the principal components of the 1000 Genomes reference panel, using R package bigsnpr. Overlapping ALLFTD and GENFI genotyping data was then used, in a two‐stage approach, to calculate pairwise identity‐by‐descent (IBD) estimates and KING coefficients, followed by family‐network identification and pedigree reconstruction using PRIMUS. Results First, we calculated IBD estimates among all participants by restricting pairs to those with estimates>0.1875 (up to second‐degree relatives). Overall, we identified a total of 292 second‐degree family networks, including 168 ALLFTD and 120 GENFI families, mostly associated with pathogenic variants in the 3 major FTD‐causing genes. We also identified 4 family networks with participants enrolled in both the ALLFTD and GENFI series, as well as several multi‐site families within the ALLFTD consortium. This first, overall approach allowed us to predict close relationships even between individuals with different ancestral backgrounds, including at least 4 confirmed admixed families. More distant relationships were also detected within ALLFTD and GENFI by performing ancestry‐based analysis among participants with estimated European ancestry, using the KING‐robust algorithm. Conclusions These lineage analyses allowed us to identify, otherwise unknown, close (and distant) relatives from different study sites, as well as within the ALLFTD and GENFI series. This dataset will be a crucial resource to increase statistical accuracy and power in upcoming collaborative FPI studies.