TY - JOUR
T1 - Risk prediction of late-onset Alzheimer’s disease implies an oligogenic architecture
AU - Australian Imaging Biomarkers and Lifestyle (AIBL) Study
AU - Zhang, Qian
AU - Sidorenko, Julia
AU - Couvy-Duchesne, Baptiste
AU - Marioni, Riccardo E.
AU - Wright, Margaret J.
AU - Goate, Alison M.
AU - Marcora, Edoardo
AU - Huang, Kuan lin
AU - Porter, Tenielle
AU - Laws, Simon M.
AU - Masters, Colin L.
AU - Bush, Ashley I.
AU - Fowler, Christopher
AU - Darby, David
AU - Pertile, Kelly
AU - Restrepo, Carolina
AU - Roberts, Blaine
AU - Robertson, Jo
AU - Rumble, Rebecca
AU - Ryan, Tim
AU - Collins, Steven
AU - Thai, Christine
AU - Trounson, Brett
AU - Lennon, Kate
AU - Li, Qiao Xin
AU - Ugarte, Fernanda Yevenes
AU - Volitakis, Irene
AU - Vovos, Michael
AU - Williams, Rob
AU - Baker, Jenalle
AU - Russell, Alyce
AU - Peretti, Madeline
AU - Milicic, Lidija
AU - Lim, Lucy
AU - Rodrigues, Mark
AU - Taddei, Kevin
AU - Taddei, Tania
AU - Hone, Eugene
AU - Lim, Florence
AU - Fernandez, Shane
AU - Rainey-Smith, Stephanie
AU - Pedrini, Steve
AU - Martins, Ralph
AU - Doecke, James
AU - Bourgeat, Pierrick
AU - Fripp, Jurgen
AU - Gibson, Simon
AU - Leroux, Hugo
AU - Hanson, David
AU - Dore, Vincent
N1 - Publisher Copyright:
© 2020, The Author(s).
PY - 2020/12/1
Y1 - 2020/12/1
N2 - 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 (Poptimal) 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.
AB - 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 (Poptimal) 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.
UR - http://www.scopus.com/inward/record.url?scp=85091435420&partnerID=8YFLogxK
U2 - 10.1038/s41467-020-18534-1
DO - 10.1038/s41467-020-18534-1
M3 - Article
C2 - 32968074
AN - SCOPUS:85091435420
SN - 2041-1723
VL - 11
JO - Nature Communications
JF - Nature Communications
IS - 1
M1 - 4799
ER -