Risk prediction of late-onset Alzheimer’s disease implies an oligogenic architecture - Sorbonne Université
Journal Articles Nature Communications Year : 2020

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

Julia Sidorenko
Riccardo E Marioni
  • Function : Author
Margaret J Wright
Alison M Goate
Edoardo Marcora
Kuan-Lin Huang
Tenielle Porter
  • Function : Author
Simon M Laws
Colin L Masters
  • Function : Author
Ashley I Bush
  • Function : Author
Christopher Fowler
  • Function : Author
David Darby
  • Function : Author
Kelly Pertile
  • Function : Author
Carolina Restrepo
  • Function : Author
Blaine Roberts
  • Function : Author
Jo Robertson
  • Function : Author
Rebecca Rumble
  • Function : Author
Tim Ryan
  • Function : Author
Steven Collins
  • Function : Author
Christine Thai
  • Function : Author
Brett Trounson
  • Function : Author
Kate Lennon
  • Function : Author
Qiao-Xin Li
  • Function : Author
Fernanda Yevenes Ugarte
  • Function : Author
Irene Volitakis
  • Function : Author
Michael Vovos
  • Function : Author
Rob Williams
  • Function : Author
Jenalle Baker
  • Function : Author
Alyce Russell
  • Function : Author
Madeline Peretti
  • Function : Author
Lidija Milicic
  • Function : Author
Lucy Lim
  • Function : Author
Mark Rodrigues
  • Function : Author
Kevin Taddei
  • Function : Author
Tania Taddei
  • Function : Author
Eugene Hone
  • Function : Author
Florence Lim
  • Function : Author
Shane Fernandez
  • Function : Author
Stephanie Rainey-Smith
  • Function : Author
Steve Pedrini
  • Function : Author
Ralph Martins
  • Function : Author
James Doecke
  • Function : Author
Pierrick Bourgeat
  • Function : Author
Jurgen Fripp
  • Function : Author
Simon Gibson
  • Function : Author
Hugo Leroux
  • Function : Author
David Hanson
  • Function : Author
Vincent Dore
  • Function : Author
Ping Zhang
  • Function : Author
Samantha Burnham
  • Function : Author
Christopher C Rowe
  • Function : Author
Victor L Villemagne
  • Function : Author
Paul Yates
  • Function : Author
Sveltana Bozin Pejoska
  • Function : Author
Gareth Jones
  • Function : Author
David Ames
  • Function : Author
Elizabeth Cyarto
  • Function : Author
Nicola Lautenschlager
  • Function : Author
Kevin Barnham
  • Function : Author
Lesley Cheng
  • Function : Author
Andy Hill
  • Function : Author
Neil Killeen
  • Function : Author
Paul Maruff
  • Function : Author
Brendan Silbert
  • Function : Author
Belinda Brown
  • Function : Author
Harmid Sohrabi
  • Function : Author
Greg Savage
  • Function : Author
Michael Vacher
  • Function : Author
Perminder S Sachdev
Karen A Mather
  • Function : Author
Nicola J Armstrong
  • Function : Author
Anbupalam Thalamuthu
  • Function : Author
Henry Brodaty
Jian Yang
Naomi R Wray
Peter M Visscher
  • Function : Author
  • PersonId : 1060516

Abstract

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.
Fichier principal
Vignette du fichier
s41467-020-18534-1.pdf (1.7 Mo) Télécharger le fichier
Origin Publication funded by an institution
Licence

Dates and versions

hal-04520268 , version 1 (25-03-2024)

Licence

Identifiers

Cite

Qian Zhang, Julia Sidorenko, Baptiste Couvy-Duchesne, Riccardo E Marioni, Margaret J Wright, et al.. Risk prediction of late-onset Alzheimer’s disease implies an oligogenic architecture. Nature Communications, 2020, 11 (1), pp.4799. ⟨10.1038/s41467-020-18534-1⟩. ⟨hal-04520268⟩
31 View
9 Download

Altmetric

Share

More