Risk prediction of late-onset Alzheimer’s disease implies an oligogenic architecture

Archive ouverte

Zhang, Qian | Sidorenko, Julia | Couvy-Duchesne, Baptiste | Marioni, Riccardo, E | Wright, Margaret, J | Goate, Alison, M | Marcora, Edoardo | Huang, Kuan-Lin | Porter, Tenielle | Laws, Simon, M | Masters, Colin, L | Bush, Ashley, I | Fowler, Christopher | Darby, David | Pertile, Kelly | Restrepo, Carolina | Roberts, Blaine | Robertson, Jo | Rumble, Rebecca | Ryan, Tim | Collins, Steven | Thai, Christine | Trounson, Brett | Lennon, Kate | Li, Qiao-Xin | Ugarte, Fernanda Yevenes | Volitakis, Irene | Vovos, Michael | Williams, Rob | Baker, Jenalle | Russell, Alyce | Peretti, Madeline | Milicic, Lidija | Lim, Lucy | Rodrigues, Mark | Taddei, Kevin | Taddei, Tania | Hone, Eugene | Lim, Florence | Fernandez, Shane | Rainey-Smith, Stephanie | Pedrini, Steve | Martins, Ralph | Doecke, James | Bourgeat, Pierrick | Fripp, Jurgen | Gibson, Simon | Leroux, Hugo | Hanson, David | Dore, Vincent | Zhang, Ping | Burnham, Samantha | Rowe, Christopher, C | Villemagne, Victor, L | Yates, Paul | Pejoska, Sveltana Bozin | Jones, Gareth | Ames, David | Cyarto, Elizabeth | Lautenschlager, Nicola | Barnham, Kevin | Cheng, Lesley | Hill, Andy | Killeen, Neil | Maruff, Paul | Silbert, Brendan | Brown, Belinda | Sohrabi, Harmid | Savage, Greg | Vacher, Michael | Sachdev, Perminder, S | Mather, Karen, A | Armstrong, Nicola, J | Thalamuthu, Anbupalam | Brodaty, Henry | Yengo, Loic | Yang, Jian | Wray, Naomi, R | Mcrae, Allan, F | Visscher, Peter, M

Edité par CCSD ; Nature Publishing Group -

International audience. Abstract Genetic association studies have identified 44 common genome-wide significant risk loci for late-onset Alzheimer’s disease (LOAD). However, LOAD genetic architecture and prediction are unclear. Here we estimate the optimal P -threshold ( P optimal ) of a genetic risk score (GRS) for prediction of LOAD in three independent datasets comprising 676 cases and 35,675 family history proxy cases. We show that the discriminative ability of GRS in LOAD prediction is maximised when selecting a small number of SNPs. Both simulation results and direct estimation indicate that the number of causal common SNPs for LOAD may be less than 100, suggesting LOAD is more oligogenic than polygenic. The best GRS explains approximately 75% of SNP-heritability, and individuals in the top decile of GRS have ten-fold increased odds when compared to those in the bottom decile. In addition, 14 variants are identified that contribute to both LOAD risk and age at onset of LOAD.

Suggestions

Du même auteur

Meta-analysis of genome-wide DNA methylation identifies shared associations across neurodegenerative disorders

Archive ouverte | Nabais, Marta, F | CCSD

International audience. Abstract Background People with neurodegenerative disorders show diverse clinical syndromes, genetic heterogeneity, and distinct brain pathological changes, but studies report overlap between...

Alzheimer's disease research progress in Australia: The Alzheimer's Association International Conference Satellite Symposium in Sydney

Archive ouverte | Sexton, Claire, E | CCSD

International audience. The Alzheimer's Association International Conference held its sixth Satellite Symposium in Sydney, Australia in 2019, highlighting the leadership of Australian researchers in advancing the un...

Amyloid imaging results from the Australian Imaging, Biomarkers and Lifestyle (AIBL) study of aging.

Archive ouverte | Rowe, Christopher C | CCSD

International audience. The Australian Imaging, Biomarkers and Lifestyle (AIBL) study of aging, a participant of the worldwide Alzheimer's Disease Neuroimaging Initiative (ADNI), performed (11)C-Pittsburgh Compound ...

Chargement des enrichissements...